MLOps Enhancements: Migrate To FastAPI, Add Testing, CI/CD, And Production-Ready Infrastructure

Mlops enables supporting machine learning models and datasets to build these models as first-class citizens within ci/cd systems. Mlops reduces technical debt across machine learning models.. In the following, we describe a set of important concepts in mlops such as iterative-incremental development, automation, continuous deployment, versioning, testing, reproducibility, and.

This template breaks down a machine learning workflow into nine components, as described in the mlops principles. Mlops, like devops, emerges from the understanding that separating the ml model development from the process that delivers it — ml operations — lowers quality, transparency, and agility of the whole. To specify an architecture and infrastructure stack for machine learning operations, we reviewed the crisp-ml (q) development lifecycle and suggested an application- and industry-neutral mlops.

Mlops is equivalent to devops in software engineering: