Summary
In this chapter, we started with an introduction to MLOps at a high level, which is essentially a blend of machine learning, DevOps, and data engineering, in which the main goal is to automate the ML lifecycle, resulting in improved workflows and collaborations between data scientists and engineers. We discussed how MLOps allows organizations to streamline their ML operations, increase the speed of deployment, and maintain high-quality models in production, leading to a more efficient, effective, and reliable ML workflow, and thereby maximizing the value that organizations get from their ML initiatives.
We touched upon the various pain points that MLOps addresses, including but not limited to challenges related to managing and versioning models, ensuring reproducibility and consistency, monitoring and maintaining models, and fostering collaboration between different teams.
We then dived into why MLOps is important for deploying large-scale machine learning workloads....
