Challenges in operationalizing a machine learning system


The goal of this blog is to cover the key topics to consider in operationalizing machine learning and to provide a practical guide for navigating the modern tools available along the way. To that end, the subsequent blogs will include further detailed architecture concepts and help you apply them to your own model pipelines. This blog series will not explain machine learning concepts but rather to tackle the auxiliary challenges like dealing with large data sets, computational requirements and optimizations, and the deployment of models and data to large software systems. Most classical software applications are deterministic where the developer writes explicit lines of code that encapsulate the logic for the desired behavior. Whereas, the ML software applications are probabilistic where the developer writes a more abstract code and lets the computer write the code in a human unfriendly language i.e. the weights or parameters required for the ML model.

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