This is a practical workshop with the goals of learning the following concepts:
- How to setup MLFLow, a tool for ML experiment tracking and model deploying, from zero to hero.
- How to track ML experiments with MLFLow
- How to put models to production with MLFLow.
- How to deploy models to production in AWS Sagemaker with just a couple lines of code.
- How to setup Apache Airflow, a powerful tool to design, schedule and monitor workflows.
- How to create workflows that take advantage of deployed models.
Some great points made.
In my experience, screwups tend to start when
- the "prototype" / minimum viable product can't scale. N^2 algorithms have a cost -- and it can be high!
- the data source is not-repeatable/updating/lacking lineage
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