Build a movie recommendation app with Tensorflow and pgvector
Learn how to create a movie recommendation web app, using PostgreSQL® and pgvector. This workshop is 2 hours long with a short break in the middle.
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Learn how to create a movie recommendation web app, using PostgreSQL® and pgvector. This workshop is 2 hours long with a short break in the middle.
See all workshops
We'll work together to build a movie recommendation system from start to finish, utilizing NodeJS, TensorFlow, and PostgreSQL’s extension pgvector. We'll guide you through the process of creating the vector embeddings using TensorFlow right on your laptop. Additionally, we'll leverage pg-promise to efficiently handle bulk row inserts, and we'll explore the usage of Next.js for a full-stack project. By the end of the workshop, you'll have a fully functional project that generates movie recommendations.
This workshop is particularly useful for those who are intrigued by contextual search and usage of AI, but might find themselves overwhelmed by the complexities of getting started.
Related resource in our developer center: TensorFlow, PostgreSQL®, pgvector & Next.js: building a movie recommender
You will learn:
You’ll also need
We will lead you through setting that up in the workshop, if you don’t already have one.
Senior Developer Advocate, Aiven
Olena is an expert in data, sustainable software development, and teamwork. With a background in software engineering, she's led teams and developed mission-critical applications at Nokia, HERE Technologies, and AWS. Currently, she works at Aiven where she supports developers and customers in using open-source data technologies such as Apache Kafka, ClickHouse, and OpenSearch. She is also an international public speaker and regularly presents at conferences around the world. She holds AWS Developer and Solutions Architect certifications, and is also a Confluent Catalyst.
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