This time we talked about one of the most vibrant questions for any MLOps practitioner: how to choose the right tools for your ML team, given the huge amount of open-source and proprietary MLOps tools available on the market today. 
We discussed several criteria to rely on when choosing a tool, including:
- The requirements of the particular team use-cases
- The scaling capacity of the tool
- The cost of migration from a chosen tool
- The cost of teaching the team to use this tool
- The company or the community behind the tool
Apart from that, we talked about particular use-cases and discussed the trade-offs between waiting for a new release of your tool to get the missing piece of functionality, switching to another tool, and building an in-house solution. 
We also touched the topic of organising MLOps teams and practices across large companies with a lot of ML teams.

// Bio:
Jose Navarro
Jose Navarro is a Machine Learning Infrastructure Engineer making everyday cooking fun at Cookpad, where its recipe platform has more than 40 million monthly users. He holds a MSc in Machine Learning and High-Performance Computing from the University of Bristol. He is interested in Cloud Native technologies, serverless, and event-driven architecture.   

Mariya Davidova
Mariya came to MLOps from a software development background. She started her career as a Java developer in JetBrains in 2011, then gradually moved to developer advocacy for JS-based APIs. In 2019, she joined Neu.ro as a platform developer advocate and then moved to the product management position.  
Mariya has been obsessed with AI and ML for many years: she finished a bunch of courses, read a lot of books, and even wrote a couple of fiction stories about AI. She believes that proper tooling and decent development and operations practices are essential success component for ML projects, as well as they are for traditional SD.

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