🔥 Edge#139: MLOps – one of the hottest topics in the ML space

A new series on TheSequence

In this issue:

  • we start a new series about MLOps;

  • we explore TFX, a TensorFlow-based architecture created by Google to manage machine learning models;

  • we overview MLflow, a platform for end-to-end ML lifecycle management.


💡 ML Concept of the Day: A New Series About MLOps  

We couldn’t resist it any longer and decided to start a new series about machine learning operations (MLOps), considered one of the hottest topics in the ML world. The MLOps is becoming increasingly crowded with a large number of stacks ranging from technology powerhouses to innovative ML startups. As a result, it has become more difficult to keep visibility across the entire MLOps ecosystem.

One of the challenges to understanding MLOps is that the term itself is used very loosely in the ML community. In general, we should think about MLOps as an extension of DevOps methodologies but optimized for the lifecycle of ML applications. This definition makes perfect sense if we consider how fundamentally different the lifecycle of ML applications is comparing to traditional software programs. For starters, ML applications are composed of both models and data, and they include stages such as training or hyperparameter optimization that have no equivalence in traditional software applications.

Just like DevOps, MLOps looks to manage the different stages of the lifecycle of ML applications. More specifically, MLOps encompasses diverse areas such as data/model versioning, continuous integration, model monitoring, model testing, and many others. In no time, MLOps evolved from a set of best practices into a holistic approach to ML lifecycle management.

In this series, we plan to break down the building blocks of MLOps, explaining the concepts behind them, highlighting some of the top research papers and technology stacks in the MLOps space. Keep up!

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