#ClearScape Analytics™ Demonstrations via Jupyter
Welcome to ClearScape Analytics Experience. This service consists of multiple demonstrations of the industry leading in-database analytics that you can run on your own. You can modify them or use them as examples to use with your own tools against our data or small (not sensitive) data you upload. Each notebook will:
- describe the business situation,
- will attach the needed data from the cloud, and
- walk you step-by-step through the use of the ClearScape Analytics functionality.
These are functional demonstrations executed on a tiny platform with small data, but the same functionality is available on all of our platforms up to one with hundreds of nodes and petabytes of data. ClearScape Analytics allows you to apply AI, ML and advanced statistics to your data without the cost and complexity of exporting data. You can develop sophisticated models on other platforms with your favorite tools and import those models to execute in production at massive scale.
If you've never used Jupyter before, we strongly recommend reviewing the First Time User section of Getting Started.
You'll find an introduction video with tips on using this platform.
There are also tips for you if you just want to look without programming.
If you have questions or issues, click here to send an e-mail to ClearScape Analytics Support.
Items in italics are coming soon.
| Getting Started | Industries | Business Function | Analytic Function | 3rd Party Tools |
|---|---|---|---|---|
| First Time User | Automotive | Finance | AWS SageMaker | |
| I am not a programmer | Energy & Natural Resources | Marketing | Azure ML | |
| Developer Information | Financial | Celebrus | ||
| Healthcare | Dataiku | |||
| Manufacturing | H2O.ai | |||
| Retail | Microsoft PowerBI | |||
| Telco | MicroStrategy | |||
| Travel & Transportation | ||||
| Defense | SAP Business Objects | |||
| SAS | ||||
| Tableau | ||||
| Vertex AI | ||||
| AWS Bedrock |
Follow these steps to create your first Azure account and create an Azure Machine Learning workspace to complete our AzureML use cases.
Information
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Video description how to find demos in the index and folder view, tips on running demos and options for foreign vs local tables used in the demonstrations in your ClearScape Analytics environment.
Information
When running a Jupyter Notebook, there are various indicators that show what is happening. This is a guide to those indicators.
SQL Version
Not everyone that uses this site will want to learn programming. Some will want to review the business cases, look at the steps for the analysis and look at the tables, charts and maps. This is a guide for those people.
Information
The td_plot method streamlines large-scale visualizations by providing users with efficient tools to create visualizations directly within the Vantage platform
Python Version
Shows how to use python to load CSV data from local storage and from zipped files
Python Version
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
SQL Version
Demonstration of using REST API calls to Vantage which is useful for web or mobile applications to access and maintain data.
Python Version
To ensure optimal utilization of the OpenAI API in generative AI notebooks, it is essential to establish the API keys correctly. This concise guide outlines the process of configuring OpenAI API keys for seamless integration across multiple notebooks.
Information
Convert a PySpark script to teradatamlspk syntax and generate a HTML report using Housing Prices to generate price predictions.
Python Version
Work through using the bgasics of the Teradata R package, tdplyr
R Version
Analysis of sessions and queries you executed using the built-in logging facilities of Vantage.
SQL Version
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
R Version
Use Teradataml Widgets to display interactive prompting to generate datasets from the Vantage database.
Python Version
This is an introduction to using the various Plot types available as widgets: Line, Bar, Mesh, Wiggle, Geometry, etc.
Python Version
Discoverer how the Teradata Package for R (tdplyr) allows users to develop and run R programs to take advantage of Big Data and Machine Learning analytic capabilities of Vantage.
Information
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
This guide will walk you through writing your first SQL queries in Jupyter. It uses some of the Vantage system tables as a source for the queries.
SQL Version
Introduction to Teradataml package for Python including connecting to Vantage, Teradata DataFrames, data manipulation and export to Pandas.
Python Version
Provides step-by-step instructions on the basics of using Python Pandas with Jupyter notebooks.
Python Version
Data from queries is brought to life with graphics and charts. This shows how to use the %chart magic command to display results.
SQL Version
Vantage Analytics Library (VAL) is a set of over 50 functions for advanced analytics. This provides an overview and links to an 8 minute video overview.
SQL Version
This provides an index to all of the databases used by demo notebooks on this machine, allowing you to use that data for your own notebooks or BI tools.
Python Version
It is very easy to submit your demo for publication. Tell us directory with the notebook and referenced files and grant us access to your database. We'll take it from there.
Python Version Video
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
A key component of ESG is Carbon Footprint. This demonstrates a part of a solution available from Teradata to integrate multiple data sources to calculate carbon footprint of various corporate activities.
Python-SQL Version
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
This leverages the power of AzureML and Teradata Vantage to enhance our machine learning capabilities and enable scalable model scoring to forecast energy consumption.
Python Version
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Uses functions from TeradataML Python library to prepare data for analysis using data cleansing, exploration and feature engineering functions.
Python Version
Use Retrieval-Augmented Generation (RAG), Lanchain and LLM models to as questions about loans and retrieve relevant data from Vantage.
Python Version
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
Predicts the future demand for cast in Automatic Teller Machines (ATMs) using Auto Regressive Integrated Moving Average (ARIMA) using In-Database functions.
SQL Version
Uses government consumer complaint data with SQL queries and visualizations to identify source of top complaints.
SQL Version
This shows the use of ClearScape analytics to reduce the pre-processing effort of incoming raw credit card data to prepare for analysis of potential loan defaulters.
SQL Version
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version Video
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
Show As-IS/As-Was capabilities of Vantage Temporal to dramatically simplify the SQL and improve performance for analyzing insurance policies versus claims.
SQL Version
Use inDb functions with OpensourceML to create multiple DecisionTreeClassifiers to create multiple predictions of a Credit Risk Assessment.
Python Version
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Fine-tuned an OpenAI model using RAG, LangChain and LLM models framework. Query a chatbot for answers about a mortgage and available housing within a predefiend area.
Python Version
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Implement the entire lifecycle of churn prediction using BYOM, VAL and AutoML.
Python Version
This walks through the Cross-Industry Standard Process for Data Mining (CRISP-DM) from data understanding through modeling and evaluation.
Python Version
This uses BYOM to import a trained H2O model to identify potential diabetes patients. BYOM allows the data scientist to create models in languages they prefer and run at scale inside Vantage without moving data.
Python-SQL Version
Decision Forest and Generalized Linear Model are applied to identify factors that indicate diabetes. The earlier the disease is identified, the better the chance of reducing organ damage.
Python Version
Machine learning is applied to the complex attributes of patients to help recognize patterns that may lead to heart failure faster than a human may recognize.
Python-SQL Version
The ClearScape Analytics Attribution function is used to determine the weight of various events that precede the final outcome, in this example, knee replacement.
SQL Version
This uses the ClearScape Analytics nPath® function to provide visuals on the events leading up to the final outcome, in this case, knee replacement.
Python Version
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Train a GPT-3.5 Turbo model using the OpenAI API endpoint. Ask mental health questions using a chat input box.
Python Version
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
Uses analytic and ML techniques to predict how long vehicle testing will take based on combination of features installed.
Python Version
Uses ML functions to predict failures to identify and mitigate potential machine failures before they occur.
Python-SQL Version
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
Build a product recommendation system using OpenAI embeddings and Vantage in db_function VectorDistance. We will also use Vantage as VectorDB, to store the embeddings.
Python Version
This uses nPath® analysis to show the retail events that prevent the ultimate objective: a purchase. This uses interactive Sankey diagrams to understand the problems.
Python Version Video
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
Python Version SQL Version
This demonstrates integration of local data or foreign tables on GCP and integration across cloud providers to detailed historical sales records on AWS.
SQL Version
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
This uses the unsupervised K-Means ML algorithm to identify patterns in retail purchases.
SQL Version
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Uses FlagEmbedding from HuggingFace plus Vantage in-database functions to assess the vector distance between the product entered and similar products.
Python Version
Uses OpenAIEmbeddings and Vantage in-database function to assists consumers in receiving product recommendations
Python Version
This creates an ARIMA time series model based on holidays and merchandising activities impacting store sales for a hypermarket retailer.
Python Version
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Uses the Script Table Operator (STO) to run the H20 machine learning library as an extension to ClearScape Analytics. H20 runs in parallel inside the Vantage database without exporting data to another platform.
Python-SQL Version
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
Python Version Python-SQL Version SQL Version
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
Quality of service analysis of 5G vs 4G video for different situations such as Indoor, outdoor, and moble.
SQL Version
Uses graph analysis to identify communities and key influencers within Call Data Records. This uses Script Table Operator to invoke external procedures to work inside the Vantage database without exporting data.
Python-SQL Version
This uses logistic regression for supervised learning to predict the probability of a customer switching vendors based on usage patterns, billing information demographics and interactions. XGBoost is then used in database to improve the prediction.
Python Version Video
Demonstrates the ability of Geospatial to show signal strength, coverage areas and travel path of customers through cell tower coverage area.
Python Version
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
Demonstrates advanced analytics to find lowest cost routes to deliver a large volume of packages by a fleet of vehicles.
Python-SQL Version
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Applies Auto Regressive Integrated Moving Average (ARIMA) analysis to forecast airplane passenger volume.
SQL Version
Applies geospatial functions to analysis of NYC Taxi pickups and drop off locations.
Python-SQL Version
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
Use the Unbounded Array Framework in ClearScape Analytics to classify sonar signals. Extract and engineer features to use in training and scoring our models.
Python Version
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
This integrates data about customers from multiple sources using multiple matching techniquest to create the "Golden Customer Record" and calculate LTV.
SQL Version
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Uses OpenAI to translate english language questions into SQL queries to run against a table of marketing data. This includes a link to a notebook with instructions for getting the required OpenAI API key.
Python Version Video
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Implement the entire lifecycle of churn prediction using BYOM, VAL and AutoML.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Uses functions from TeradataML Python library to prepare data for analysis using data cleansing, exploration and feature engineering functions.
Python Version SQL Version
This demonstrates a subset of the over 100 analytic functions in the teradataml package for Python
Python Version Python-SQL Version
Outliers in an analysis skew the results and make it difficult to recognize the main patterns. ClearScape Analytics has tools to remove outliers automatically.
SQL Version
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
R Version
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
This performs in-database analysis of data values, distribution, histograms, and text field analysis using SQL to access the Vantage Analytics Library.
SQL Version
Demonstrated the use of Teradataml in Python to perform descriptive statistics, transformation, model building model evaluation and scoring.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
Use Retrieval-Augmented Generation (RAG), Lanchain and LLM models to as questions about loans and retrieve relevant data from Vantage.
Python Version
Build a product recommendation system using OpenAI embeddings and Vantage in db_function VectorDistance. We will also use Vantage as VectorDB, to store the embeddings.
Python Version
Uses OpenAI to translate english language questions into SQL queries to run against a table of marketing data. This includes a link to a notebook with instructions for getting the required OpenAI API key.
Python Version Video
Uses FlagEmbedding from HuggingFace plus Vantage in-database functions to assess the vector distance between the product entered and similar products.
Python Version
Uses OpenAIEmbeddings and Vantage in-database function to assists consumers in receiving product recommendations
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Use context-based product recommendations powered by GenAI Large Language Models to enhance a shopping experience.
Python-SQL Version
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
Fine-tuned an OpenAI model using RAG, LangChain and LLM models framework. Query a chatbot for answers about a mortgage and available housing within a predefiend area.
Python Version
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
Applies geospatial functions to analysis of NYC Taxi pickups and drop off locations.
Python-SQL Version
Creative application of geospatial to locations of sensors in a research lab and integration of data from tables with detailed recordings on cloud storage.
Python-SQL Version
Demonstrates the ability of Geospatial to show signal strength, coverage areas and travel path of customers through cell tower coverage area.
Python Version
Create a predictive algorithm that can identify whethr or not passengers on the Titanic survived the ship's sinking.
Python Version
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
This walks through the Cross-Industry Standard Process for Data Mining (CRISP-DM) from data understanding through modeling and evaluation.
Python Version
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
This demonstrates a subset of the 18 hypothesis test in the Vantage Analytics library using SQL such as Parametric, Binomial, Kolmogorov/Smirnoff, Rank, etc.
SQL Version
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Quality of service analysis of 5G vs 4G video for different situations such as Indoor, outdoor, and moble.
SQL Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
Decision Forest and Generalized Linear Model are applied to identify factors that indicate diabetes. The earlier the disease is identified, the better the chance of reducing organ damage.
Python Version
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version Video
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
Uses graph analysis to identify communities and key influencers within Call Data Records. This uses Script Table Operator to invoke external procedures to work inside the Vantage database without exporting data.
Python-SQL Version
Uses analytic and ML techniques to predict how long vehicle testing will take based on combination of features installed.
Python Version
Machine learning is applied to the complex attributes of patients to help recognize patterns that may lead to heart failure faster than a human may recognize.
Python-SQL Version
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
This uses the unsupervised K-Means ML algorithm to identify patterns in retail purchases.
SQL Version
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Uses ML functions to predict failures to identify and mitigate potential machine failures before they occur.
Python-SQL Version
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Uses the Script Table Operator (STO) to run the H20 machine learning library as an extension to ClearScape Analytics. H20 runs in parallel inside the Vantage database without exporting data to another platform.
Python-SQL Version
This uses logistic regression for supervised learning to predict the probability of a customer switching vendors based on usage patterns, billing information demographics and interactions. XGBoost is then used in database to improve the prediction.
Python Version Video
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
Demonstration of Vantage Analytic Library scoring and analytic functions like linear regression, decision trees, K-Means clustering, Factor Analysis, etc.
SQL Version
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
Compare the distribution and variation of data between two time intervals usin In-DB function executed via a framework to provide alerts highlighting data anomalies.
Python Version
Create a predictive algorithm that can identify whethr or not passengers on the Titanic survived the ship's sinking.
Python Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Use inDb functions with OpensourceML to create multiple DecisionTreeClassifiers to create multiple predictions of a Credit Risk Assessment.
Python Version
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
This demonstrates integration of local data or foreign tables on GCP and integration across cloud providers to detailed historical sales records on AWS.
SQL Version
Creative application of geospatial to locations of sensors in a research lab and integration of data from tables with detailed recordings on cloud storage.
Python-SQL Version
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Demonstrates advanced analytics to find lowest cost routes to deliver a large volume of packages by a fleet of vehicles.
Python-SQL Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
This uses nPath® analysis to show the retail events that prevent the ultimate objective: a purchase. This uses interactive Sankey diagrams to understand the problems.
Python Version Video
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
Python Version SQL Version
Discusses how the 3rd party tool DataIku can be used with Vantage.
Information
This uses BYOM to import a trained H2O model to identify potential diabetes patients. BYOM allows the data scientist to create models in languages they prefer and run at scale inside Vantage without moving data.
Python-SQL Version
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
The ClearScape Analytics Attribution function is used to determine the weight of various events that precede the final outcome, in this example, knee replacement.
SQL Version
This uses the ClearScape Analytics nPath® function to provide visuals on the events leading up to the final outcome, in this case, knee replacement.
Python Version
Introduction to Teradataml package for Python including connecting to Vantage, Teradata DataFrames, data manipulation and export to Pandas.
Python Version
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
Use K-means clustering to segment customers by purchase volume and value using R and tdplyr
R Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Use K-means clustering to segment customers by purchase volume and value using R and tdplyr
R Version
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
This integrates data about customers from multiple sources using multiple matching techniquest to create the "Golden Customer Record" and calculate LTV.
SQL Version
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
Python Version Python-SQL Version SQL Version
This performs in-database analysis of data values, distribution, histograms, and text field analysis using SQL to access the Vantage Analytics Library.
SQL Version
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Applies Auto Regressive Integrated Moving Average (ARIMA) analysis to forecast airplane passenger volume.
SQL Version
A key component of ESG is Carbon Footprint. This demonstrates a part of a solution available from Teradata to integrate multiple data sources to calculate carbon footprint of various corporate activities.
Python-SQL Version
Predicts the future demand for cast in Automatic Teller Machines (ATMs) using Auto Regressive Integrated Moving Average (ARIMA) using In-Database functions.
SQL Version
Uses government consumer complaint data with SQL queries and visualizations to identify source of top complaints.
SQL Version
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Fourier transformations are demonstrated to filter out noise from signals to allow identification of underlying patterns.
SQL Version
Show As-IS/As-Was capabilities of Vantage Temporal to dramatically simplify the SQL and improve performance for analyzing insurance policies versus claims.
SQL Version
Time series data can answer the questions about what was happening at a point in time. This applies Time series and temporal capabilities of vantage to NYC taxi data.
SQL Version
This creates an ARIMA time series model based on holidays and merchandising activities impacting store sales for a hypermarket retailer.
Python Version
Analysis of sessions and queries you executed using the built-in logging facilities of Vantage.
SQL Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Use the Unbounded Array Framework in ClearScape Analytics to classify sonar signals. Extract and engineer features to use in training and scoring our models.
Python Version
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
This leverages the power of AzureML and Teradata Vantage to enhance our machine learning capabilities and enable scalable model scoring to forecast energy consumption.
Python Version
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Discusses how the 3rd party tool DataIku can be used with Vantage.
Information
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
To ensure optimal utilization of the OpenAI API in generative AI notebooks, it is essential to establish the API keys correctly. This concise guide outlines the process of configuring OpenAI API keys for seamless integration across multiple notebooks.
Information
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Train a GPT-3.5 Turbo model using the OpenAI API endpoint. Ask mental health questions using a chat input box.
Python Version
Fine-tuned an OpenAI model using RAG, LangChain and LLM models framework. Query a chatbot for answers about a mortgage and available housing within a predefiend area.
Python Version
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
The td_plot method streamlines large-scale visualizations by providing users with efficient tools to create visualizations directly within the Vantage platform
Python Version
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
Uses functions from TeradataML Python library to prepare data for analysis using data cleansing, exploration and feature engineering functions.
Python Version
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
Use Retrieval-Augmented Generation (RAG), Lanchain and LLM models to as questions about loans and retrieve relevant data from Vantage.
Python Version
Build a product recommendation system using OpenAI embeddings and Vantage in db_function VectorDistance. We will also use Vantage as VectorDB, to store the embeddings.
Python Version
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
Demonstrates advanced analytics to find lowest cost routes to deliver a large volume of packages by a fleet of vehicles.
Python-SQL Version
Use context-based product recommendations powered by GenAI Large Language Models to enhance a shopping experience.
Python-SQL Version
Create a predictive algorithm that can identify whethr or not passengers on the Titanic survived the ship's sinking.
Python Version
Use the Unbounded Array Framework in ClearScape Analytics to classify sonar signals. Extract and engineer features to use in training and scoring our models.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
Analyzing consumer complaints using audio files conversations
Python Version
Use Teradataml Widgets to display interactive prompting to generate datasets from the Vantage database.
Python Version
Convert a PySpark script to teradatamlspk syntax and generate a HTML report using Housing Prices to generate price predictions.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
This is an introduction to using the various Plot types available as widgets: Line, Bar, Mesh, Wiggle, Geometry, etc.
Python Version
Implement the entire lifecycle of churn prediction using BYOM, VAL and AutoML.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Use inDb functions with OpensourceML to create multiple DecisionTreeClassifiers to create multiple predictions of a Credit Risk Assessment.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Compare the distribution and variation of data between two time intervals usin In-DB function executed via a framework to provide alerts highlighting data anomalies.
Python Version
Train a GPT-3.5 Turbo model using the OpenAI API endpoint. Ask mental health questions using a chat input box.
Python Version
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
This uses nPath® analysis to show the retail events that prevent the ultimate objective: a purchase. This uses interactive Sankey diagrams to understand the problems.
Python Version Video
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
A key component of ESG is Carbon Footprint. This demonstrates a part of a solution available from Teradata to integrate multiple data sources to calculate carbon footprint of various corporate activities.
Python-SQL Version
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
Python Version SQL Version
This provides an index to all of the databases used by demo notebooks on this machine, allowing you to use that data for your own notebooks or BI tools.
Python Version
This provides linkage to a larger set of databases and tables than are currently used by the demos in Jupyter.
Python Version
Shows how to use python to load CSV data from local storage and from zipped files
Python Version
This demonstrates a subset of the over 100 analytic functions in the teradataml package for Python
Python Version Python-SQL Version
This walks through the Cross-Industry Standard Process for Data Mining (CRISP-DM) from data understanding through modeling and evaluation.
Python Version
This uses BYOM to import a trained H2O model to identify potential diabetes patients. BYOM allows the data scientist to create models in languages they prefer and run at scale inside Vantage without moving data.
Python-SQL Version
Decision Forest and Generalized Linear Model are applied to identify factors that indicate diabetes. The earlier the disease is identified, the better the chance of reducing organ damage.
Python Version
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
This leverages the power of AzureML and Teradata Vantage to enhance our machine learning capabilities and enable scalable model scoring to forecast energy consumption.
Python Version
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version Video
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
Uses OpenAI to translate english language questions into SQL queries to run against a table of marketing data. This includes a link to a notebook with instructions for getting the required OpenAI API key.
Python Version Video
Uses graph analysis to identify communities and key influencers within Call Data Records. This uses Script Table Operator to invoke external procedures to work inside the Vantage database without exporting data.
Python-SQL Version
Uses analytic and ML techniques to predict how long vehicle testing will take based on combination of features installed.
Python Version
Machine learning is applied to the complex attributes of patients to help recognize patterns that may lead to heart failure faster than a human may recognize.
Python-SQL Version
It is very easy to submit your demo for publication. Tell us directory with the notebook and referenced files and grant us access to your database. We'll take it from there.
Python Version Video
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
Provides step-by-step instructions on the basics of using Python Pandas with Jupyter notebooks.
Python Version
This uses the ClearScape Analytics nPath® function to provide visuals on the events leading up to the final outcome, in this case, knee replacement.
Python Version
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Applies geospatial functions to analysis of NYC Taxi pickups and drop off locations.
Python-SQL Version
Uses ML functions to predict failures to identify and mitigate potential machine failures before they occur.
Python-SQL Version
Uses FlagEmbedding from HuggingFace plus Vantage in-database functions to assess the vector distance between the product entered and similar products.
Python Version
Uses OpenAIEmbeddings and Vantage in-database function to assists consumers in receiving product recommendations
Python Version
Demonstration of using REST API calls to Vantage which is useful for web or mobile applications to access and maintain data.
Python Version
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
This creates an ARIMA time series model based on holidays and merchandising activities impacting store sales for a hypermarket retailer.
Python Version
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
Creative application of geospatial to locations of sensors in a research lab and integration of data from tables with detailed recordings on cloud storage.
Python-SQL Version
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Uses the Script Table Operator (STO) to run the H20 machine learning library as an extension to ClearScape Analytics. H20 runs in parallel inside the Vantage database without exporting data to another platform.
Python-SQL Version
This uses logistic regression for supervised learning to predict the probability of a customer switching vendors based on usage patterns, billing information demographics and interactions. XGBoost is then used in database to improve the prediction.
Python Version Video
Demonstrates the ability of Geospatial to show signal strength, coverage areas and travel path of customers through cell tower coverage area.
Python Version
Introduction to Teradataml package for Python including connecting to Vantage, Teradata DataFrames, data manipulation and export to Pandas.
Python Version
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
Python Version Python-SQL Version SQL Version
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
Demonstrated the use of Teradataml in Python to perform descriptive statistics, transformation, model building model evaluation and scoring.
Python Version
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Quality of service analysis of 5G vs 4G video for different situations such as Indoor, outdoor, and moble.
SQL Version
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
Python Version SQL Version
Use context-based product recommendations powered by GenAI Large Language Models to enhance a shopping experience.
Python-SQL Version
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Applies Auto Regressive Integrated Moving Average (ARIMA) analysis to forecast airplane passenger volume.
SQL Version
When running a Jupyter Notebook, there are various indicators that show what is happening. This is a guide to those indicators.
SQL Version
Predicts the future demand for cast in Automatic Teller Machines (ATMs) using Auto Regressive Integrated Moving Average (ARIMA) using In-Database functions.
SQL Version
Data from queries is brought to life with graphics and charts. This shows how to use the %chart magic command to display results.
SQL Version
Uses government consumer complaint data with SQL queries and visualizations to identify source of top complaints.
SQL Version
This shows the use of ClearScape analytics to reduce the pre-processing effort of incoming raw credit card data to prepare for analysis of potential loan defaulters.
SQL Version
This integrates data about customers from multiple sources using multiple matching techniquest to create the "Golden Customer Record" and calculate LTV.
SQL Version
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
SQL Version
This demonstrates integration of local data or foreign tables on GCP and integration across cloud providers to detailed historical sales records on AWS.
SQL Version
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
Fourier transformations are demonstrated to filter out noise from signals to allow identification of underlying patterns.
SQL Version
Show As-IS/As-Was capabilities of Vantage Temporal to dramatically simplify the SQL and improve performance for analyzing insurance policies versus claims.
SQL Version
This uses the unsupervised K-Means ML algorithm to identify patterns in retail purchases.
SQL Version
The ClearScape Analytics Attribution function is used to determine the weight of various events that precede the final outcome, in this example, knee replacement.
SQL Version
Time series data can answer the questions about what was happening at a point in time. This applies Time series and temporal capabilities of vantage to NYC taxi data.
SQL Version
Outliers in an analysis skew the results and make it difficult to recognize the main patterns. ClearScape Analytics has tools to remove outliers automatically.
SQL Version
This guide will walk you through writing your first SQL queries in Jupyter. It uses some of the Vantage system tables as a source for the queries.
SQL Version
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
Python Version Python-SQL Version SQL Version
Demonstration of Vantage Analytic Library scoring and analytic functions like linear regression, decision trees, K-Means clustering, Factor Analysis, etc.
SQL Version
This performs in-database analysis of data values, distribution, histograms, and text field analysis using SQL to access the Vantage Analytics Library.
SQL Version
This demonstrates a subset of the 18 hypothesis test in the Vantage Analytics library using SQL such as Parametric, Binomial, Kolmogorov/Smirnoff, Rank, etc.
SQL Version
Vantage Analytics Library (VAL) is a set of over 50 functions for advanced analytics. This provides an overview and links to an 8 minute video overview.
SQL Version
Analysis of sessions and queries you executed using the built-in logging facilities of Vantage.
SQL Version
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
Use K-means clustering to segment customers by purchase volume and value using R and tdplyr
R Version
Discoverer how the Teradata Package for R (tdplyr) allows users to develop and run R programs to take advantage of Big Data and Machine Learning analytic capabilities of Vantage.
Information
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Work through using the bgasics of the Teradata R package, tdplyr
R Version
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
R Version
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Compare the distribution and variation of data between two time intervals usin In-DB function executed via a framework to provide alerts highlighting data anomalies.
Python Version
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Classify customer complaints to swiftly identify and address concerns.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Bring a language model into Vantage and run NLP models in the database
Python Version
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Follow these steps to create your first Azure account and create an Azure Machine Learning workspace to complete our AzureML use cases.
Information
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Classify customer complaints to swiftly identify and address concerns.
Python Version
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Analyzing consumer complaints using audio files conversations
Python Version
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
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