Explore the capabilities of Apache Spark paired with MLFlow, a comprehensive platform for managing the end-to-end machine learning lifecycle. Understand how the combination of these two open-source solutions can effectively allow applying the data-led machine learning prediction architecture.

Throughout the discussion, we'll examine the different architectures to expose ML models in production. We'll then focus on the data-led approach, demoing in practice how to implement it with Spark and MLFlow.

*Target audience:* data engineers, data scientists, ML engineers, and backend developers who work with machine learning deployment and/or MLOps.

*Technical level:* intermediate. We will discuss ML models exposure architectures, and show implementation code.

*Duration:* flexible from 30 to 1 hour (the depth will be adapted based on the available time.

The session is based on the real-life experience of bringing several ML models from the data scientists' hands to production, using Spark and MLFlow.

I've also shared this experience in a series of three Medium articles:
1 - https://itnext.io/intro-to-mlops-model-life-cycle-from-a-data-engineers-eyes-b9347440fae4?source=friends_link&sk=e313e9855176ba85064408d8251fd50b
2 - https://medium.com/israeli-tech-radar/avoid-the-ml-dependencies-syncing-black-hole-2de061c1870e?source=friends_link&sk=8ad64bb408f9d172c422ddd528ac5a99
3 - https://medium.com/israeli-tech-radar/uncovering-mlflows-spark-udf-e46603971afa?source=friends_link&sk=12ad3474db4d64c789dab8171aa8de74