Using PostgreSQL to power your AI applications

Presented by

Mohamed Nsiri, SQL Product Manager at Canonical, and Andreea Munteanu, AI Product Manager at Canonical

About this talk

What can PostgreSQL do for AI application? PostgreSQL is capable of efficiently storing and retrieving large amounts of data, such as training data for your models. Its wide support of the SQL standard, including SQL/MED, makes inspecting and querying that data a breeze. Moreover, PostgreSQL comes with similarity search capabilities, providing convenient index structures (GIST, GIN...) and built-in extensions (fuzzystrmatch, pg_trgm...). Yet, there is more to PostgreSQL's ecosystem than the database server. There are more than 1000 extensions for PostgreSQL, many of which can be useful for AI developers and users, for example pgvector and PostgresML. The Role of PostgreSQL in MLOps MLOps projects such as Kubeflow rely on relational databases for storing different artefacts, and choosing which relational database to use for MLOps is a common dilemma. We approached a similar topic in our recent webinar “PostgreSQL vs MySQL”, where we explored how, despite looking similar on the surface, the two databases are actually very different. The significant structural differences between databases can have a tremendous influence on the performance of any ML project, so understanding the possible impact is critical for both the enterprises who use these platforms and the engineers who develop them. So how do we use PostgreSQL for machine learning? Join the live discussion to learn more about using PostgreSQL for AI projects. The webinar will cover: - An overview of the PostgreSQL ecosystem for AI projects - Benefits of PostgreSQL for AI projects - Use cases where PostgreSQL should be used for AI projects
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