MLflow in Action – Master the art of MLOps using MLflow tool

A master guide to unleash the full potential of MLflow to optimize MLOps. Streamline MLOps workflows using MLflow tool

Created by: J Garg - Real Time Learning
Data Engineering, Analytics and Cloud Trainer

What Will I Learn?

• Explore the fundamentals of MLOps and how it overcomes the challenges inherent in the traditional ML lifecycle.
• Gain a deep understanding of MLflow and the role of its 4 components in managing the end-to-end Machine learning operations (MLOps).
• Learn how to efficiently Track experiments, Package code, Register and reproduce models in the realm of MLOps using MLflow tool.
• A range of MLflow logging functions to effectively track and record experiments, runs, artifacts, parameters, code, metrics etc.
• MLflow Tracking - To log, organize, and compare Machine learning experiments effortlessly.
• MLflow Model - For efficient model packaging into distinct flavors allowing to streamline model deployment and integration into production systems.
• MLflow Project - To create structured, reproducible, and easily shareable Machine Learning workflows.
• MLflow Registry - For efficient model management, version tracking in order to maintain model quality and performance over time.
• A complete end-to-end ML project demonstrating MLflow integration with AWS cloud.
• Build, Train, Test and Deploy a Machine learning model in AWS cloud using AWS Sagemaker and MLflow.

Requirements

• Basic Machine Leaning knowledge
• Basic Python knowledge

Target audience

• Data Scientists
• Machine Learning Engineers
• MLOps Engineers
• Operations Engineers