Building, training, and deploying a movie recommendation engine

Vale set out to create a better platform for watching movies and TV shows. They wanted to validate their idea with a proof of concept that could make relevant recommendations to viewers, employ social networking elements related to movie watching, identify new viewers, and advise them about updates. The challenge of deploying a scalable recommendation solution was getting up to speed with modern AI/ML technology. So the company partnered with ClearScale, and together they used Amazon Personalize as the foundation for the recommendation function. Model retraining was performed with AWS Step Functions and event Lambda triggers weekly. ClearScale applied this data with the Amazon Personalize engine to create several models, then evaluated and compared the models against one another to identify the best algorithm for Vale’s use case. Together, these services gave Vale the proof of concept it needed to confidently move forward with a full-scale version of its Streaming Guide application.