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
4.41 (740 reviews)
6,298
students
9.5 hours
content
Aug 2024
last update
$89.99
regular price
What you will 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.
5611434
udemy ID
10/16/2023
course created date
10/30/2023
course indexed date
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