Tennis is a highly technical sport that requires more critical feedback than other sports. This means the effort required to improve is substantially higher than other sports. The ability to receive world-class coaching at a low price needs to be accessible for players to improve rapidly.
- 02/14/22 - 02/21/22, Week 9 Reid - integrated all of Backend's changes, Adler - Login implemented, Ventures Accelerated - accepted.
- 02/07/22 - 02/14/22, Week 8 Backend - Grant = Login, Adler = Editing/Deleting Bucket, Request Doubles Partners, Web development - Alex = Get Static, starting web app with Auth, Frontend - Reid & Andrew = Display comments and time stamps,
- 01/31/22 - 02/07/22, Week 7 Same as week 6, automate deployment
- 01/24/22 - 01/31/22, Week 6 Timeline view, marketing website, email/password login, bucket overhaul, combine player/coach accounts, user interaction
- 01/17/22 - 01/24/22, Week 5 Frontend grind, user search, query parameters
- 01/10/22 - 01/17/22, Week 4 Usernames, user search, signed Cloudfront cookies, public/private profile content, deleting resources
- 01/03/22 - 01/10/22, Week 3 Player/coach interaction, user sessions, and domain setup
- 12/27/21 - 01/03/22, Week 2 Frontend & backend integration
- 12/20/21 - 12/27/21, Week 1 Ironing out the kinks
This alpha of our product is only the beginning. Once users begin using our alpha to upload themselves hitting certain shots, we will begin training a machine learning model that will offer automatic coaching to clients. Furthermore, we will develop a coach's version that allows instructors to annotate and offer remote feedback to their players. This dataset will further improve our ML model and the feedback clients receive.
Utilizing the AWS suite of devices, we will train our model using highly scalable EC2 instances from data stored in our S3 bucket. This allows us to scale our backend with demand in a capital effective manner.