Run Machine Learning & LLMs Inside Your Database with Exasol, Build fast pipelines, run Python functions, ML models and LLMs directly inside Exasol, the scalable analytics engine.
Course Description
Most data teams treat the database as a place to store and query data. This course shows you a different way of thinking about it.
Exasol is a massively parallel processing database that keeps your data compressed in memory and spreads the workload across nodes. That alone makes it significantly faster than traditional data warehouses. But what makes it truly powerful is what you can run inside it: Python code, machine learning models, and even language models, all called directly from SQL.
In this course you will set up a fully functional Exasol cluster on your own AWS infrastructure using Exasol Personal, which is completely free and has no feature or data limits. You will load real datasets, design tables with the right distribution and partition keys, and see with real benchmark numbers what proper data layout does to query performance.
From there you will write Python UDFs that call live APIs and return results inside a single SQL query. Then you will move into the AI Lab, where you will train a scikit-learn classifier, upload it to BucketFS, and run predictions straight from SQL. You will also load a Hugging Face zero-shot classification model into the database and classify text documents without sending a single row to an external service.
By the end of this course you will know how to turn Exasol into a full in-database AI and ML platform that runs entirely on your own infrastructure.
Who this course is for:
- This course is for software engineers, data scientists, and data engineers who want to go beyond standard SQL and run real AI and ML workloads directly inside a high-performance database.
