DevOps for AI in the Microsoft World

DevOps has become ubiquitous in the world of classical development. Almost all projects that exceed a certain level of complexity become inevitably DevOps projects. Yet there is one category of projects that are stepping out of the line. You’ve guessed it right, it’s the category of Data Science projects. When it comes to DevOps, Data Science projects pose a range of special challenges, whether it’s about the technical side of things, the philosophy of the people involved, or the actors involved. Think about one simple example: versioning. While in a “classical” development project versioning refers almost exclusively to source code, in the world of data science it gets another important aspect: data versioning. It’s not enough to know the version of the code for your model, it’s equally important to know the “version” of the data it was trained on. Another interesting question is, for examples, what does a “build” mean in the world of data science? Or a “release”? Join me in this session for an applied discussion about DevOps principles and approaches for AI and Machine learning projects. In addition to the principles, we’re also going to analyze an end-to-end example of a DevOps pipeline used in a real-world project.
Intermediate AI/ML

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