Managing Machine Learning Life Cycles with MLflow
This Fullstack Live Event covers the transition from DevOps to MLOps. We’ll start by introducing MLflow and guide you through its installation and training in your own models. We’ll explore hyper-parameter tuning, model selection, and the MLflow model registry. In the second half, we’ll focus on Apache Airflow, an open source platform for workflow orchestration and scheduling. We’ll begin with an overview of Airflow, its concepts, and background. Then, we’ll delve into using Directed Acyclic Graphs (DAGs) and the Airflow GUI. By the end of the workshop, you’ll have a solid understanding of integrating Airflow and MLflow into your existing pipeline. We’ll provide you with an architecture that combines both tools, enabling you to effectively orchestrate and manage your machine learning workflows. For installation and pre-requisites please check:
See this content immediately after install