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You train your model. You check its performance with a validation set. You tweak its hyperparameters, engineer some features and repeat. Finally, you try it out on a test set, and it works great!

Problem solved? Well, probably not.

Five years ago, your job as a data scientist might have ended here, but increasingly, the data science life cycle is expanding to include the steps after basic testing. This shouldn\u2019t come as a surprise: now that machine learning models are being used for life-or-death and mission-critical applications, there\u2019s growing pressure on data scientists and machine learning engineers to ensure that effects like feature drift are addressed reliably, that data science experiments are replicable, and that data infrastructure is reliable.

This episode\u2019s guest is Luke Marsden, and he\u2019s made these problems the focus of this work. Luke is the founder and CEO of Dotscience, a data infrastructure startup that\u2019s creating a git-like tool for data science version control. Luke has spent most of his professional life working on infrastructure problems at scale, and has a lot to say about the direction data science and MLOps are heading in.