KitOps Is the Open Source Tool That Turns DevOps Pipelines Into MLOps Pipelines

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The marriage of machine learning and DevOps practices has given birth to MLOps, a specialized field focused on automating the development, deployment and management of ML models in production environments. However, a major hurdle in achieving streamlined MLOps workflows lies in the traditional separation between DevOps and machine learning pipelines.

This article explores KitOps, an open source project that bridges this gap by allowing you to leverage your existing DevOps pipelines for MLOps tasks through the use of ModelKits. A short walkthrough and code sample in the article will demonstrate how easy it is to get started.

Building and deploying traditional applications typically follows a well-defined DevOps pipeline. Code undergoes version control, automated testing, and seamless integration and delivery. DevOps is an accepted and proven practice for deploying systems at scale. However, ML projects introduce new complexities. Models require specific data sets, training environments, configurations and monitoring tools. Data scientists may use Jupyter notebooks and iterate on model refinement. Building separate MLOps pipelines to manage these aspects alongside existing DevOps pipelines leads to several inefficiencies:

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