Main Information:

• In the last article, explored how analytics is evolving with ML and SQL integration. Now focuses on combining AI and Big Data/SQL to build recommendation engines.
• SQL plays a fundamental role in the recommendation engine pipeline in data storage, retrieval, and preprocessing. It's used for querying and manipulating structured data like user profiles, item attributes, and interaction data.
• After data preprocessing with SQL, it's fed into AI models like Matrix Factorization, Deep Learning, and Clustering Algorithms for training and prediction.
• Building a recommendation engine involves defining objectives, collecting and storing data, preparing data, selecting and implementing AI models, training and evaluating, and generating personalized recommendations with a feedback loop.
• A simple SQL example shows how to find movie recommendations based on similar users' ratings. Modern database solutions are integrating AI capabilities directly into SQL engines.
• The future of recommendation engines is tighter integration between AI and SQL, with managed services like Google Cloud's Vertex AI Search and Amazon Personalize simplifying the process. SQL is crucial for feeding processed data to models and serving recommendations.

Important Details:

• Relational databases are good for managing structured data.
• Different AI techniques have their advantages in recommendation engines.
• A step-by-step guide for building a recommendation engine is provided.
• The video on building recommendation engines from scratch is helpful.
• Solutions like Snowflake's Cortex AISQL are integrating AI directly into SQL engines.
• Managed services handle underlying AI complexities while relying on SQL-accessible data.