| File Name: | MLflow in Action – Master the art of MLOps using MLflow tool |
| Content Source: | https://www.udemy.com/course/mlflow-course/ |
| Genre / Category: | Other Tutorials |
| File Size : | 3.1 GB |
| Publisher: | udemy |
| Updated and Published: | January 18, 2026 |
MLOps is the backbone of modern Machine learning workflows. It solves the pressing problem of operationalizing the ML models in production systems. Pushing the ML models to production which could traditionally take months can now be operationalized in few days using MLOps tools. As per the tech talks in market, 2024 is the year of MLOps and would become the mandate skill for Enterprise ML projects.
Why MLflow tool for MLOps ?
MLflow is the ultimate tool for MLOps because it streamlines the entire Machine learning lifecycle. It allows you to efficiently track experiments, package code, register versions and deploy models, all within one unified platform. Unlike other tools, MLflow simplifies the process, enabling you to transition from development to deployment seamlessly.
MLflow’s popularity is evident from the thousands of organizations, ranging from startups to Fortune 500 companies, that have integrated MLflow into their MLOps workflows.
What’s included in this MLflow course:
- Understand MLOps basics, limitations of traditional ML lifecycles, how MLOps overcomes those limitations.
- Complete MLflow concepts explained from Scratch to Real-Time implementation.
- Learn in practical the 4 core components of MLflow – Tracking, Model, Project, and Registry.
- Various logging functions in MLflow for precise tracking and recording of experiments, runs, artifacts, parameters, code, metrics, and more.
- Learn to handle customized models using Python in MLflow.
- Learn to interact with MLflow using MLflow library, UI, MLflow Client and CLI commands.
- Learn Best practices and Optimization techniques to follow in Real-Time MLOps/MLflow Projects.
**Exclusive** – A complete end-to-end ML project demonstrating MLflow’s integration with AWS cloud. Build, Train, Test, Deploy a Machine learning model in AWS cloud using AWS Sagemaker, Codecommit, Ec2, ECR, AWS S3, IAM etc services while leveraging MLflow tracking capabilities.
After completing this course, you can start working on any MLOps/MLflow project with full confidence.
Add-Ons:
- Questions and Queries will be answered very quickly.
- Codes and references used in lectures are attached in the course for your convenience.
DOWNLOAD LINK: MLflow in Action – Master the art of MLOps using MLflow tool
MLflow_in_Action_Master_the_art_of_MLOps_using_MLflow_tool.part1.rar – 1000.0 MB
MLflow_in_Action_Master_the_art_of_MLOps_using_MLflow_tool.part2.rar – 1000.0 MB
MLflow_in_Action_Master_the_art_of_MLOps_using_MLflow_tool.part3.rar – 1000.0 MB
MLflow_in_Action_Master_the_art_of_MLOps_using_MLflow_tool.part4.rar – 77.4 MB
FILEAXA.COM – is our main file storage service. We host all files there. You can join the FILEAXA.COM premium service to access our all files without any limation and fast download speed.
