Many companies at some point have a need to have an ML Platform to accelerate ML adoption. How can you get one and make sure your team can maintain it in the long run? This talk will give you a practical guide on how to approach ML Platform building: What options do we have, and what are the drawbacks? How do you ensure the stability of your ML Platform in a world where new MLOps tools pop up every week and make your ML Platform usable by Data Scientists?