fabioba/mlops-architecture
This is an overview of a MLOps architecture that includes both Airflow and MLflow running on separate Docker containers.
This MLOps architecture provides a ready-to-use environment for data scientists and machine learning engineers to build and manage their machine learning projects. It allows you to define and schedule data pipelines using Airflow, and simultaneously track and compare your machine learning experiments and models with MLflow. You provide your data and model code, and it provides the infrastructure to automate your workflows and monitor your model development.
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Use this if you are a data scientist or ML engineer who needs a pre-configured setup to orchestrate machine learning workflows and track experiments effectively.
Not ideal if you are looking for a simple tool to run a single, isolated script or do not require an end-to-end MLOps setup.
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Python
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Last pushed
Oct 18, 2022
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