Training: MLflow
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: MLflow
The MLflow training is an intensive two-day course focusing on the practical application of MLflow for managing the machine learning lifecycle. The program is designed so that 80% of the time is dedicated to hands-on workshops and 20% to theory. Participants will learn how to efficiently register, track, deploy, and monitor ML models, working on real-world examples and use cases.
What will you learn?
- How to configure and manage MLflow for tracking ML experiments
- How to monitor and update deployed ML models
- How to register, store, and deploy ML models using MLflow
- How to integrate MLflow with popular ML frameworks and cloud platforms
Prerequisites
- Basic knowledge of Python programming
- Experience with data analysis tools is an advantage
- Basic understanding of machine learning
Who is this training for?
Data scientists and data engineers who want to expand their skills in ML lifecycle management-
IT specialists looking to use MLflow to automate ML processes in their organizations
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ML engineers and developers aiming to deploy and monitor ML models in production
Training Program
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Day 1: Introduction to MLflow and Model Management Basics
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MLflow fundamentals
- Introduction to MLflow and its architecture
- Installation and configuration of MLflow
- Tracking ML experiments with MLflow Tracking
- Recording and managing ML experiment metadata and results
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Model management and storage
- Registering models with MLflow Models
- Storing models in the MLflow Model Registry
- Hands-on practice: registering and tracking ML experiments
- Analysis and interpretation of experiment results
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Day 2: Advanced Techniques and Practical Applications
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Model deployment with MLflow Projects
- Creating and configuring MLflow projects
- Deploying models across different platforms
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Monitoring with MLflow Models
- Tracking and optimizing deployed models
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Integration with tools and services
- Integrating MLflow with popular ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Integrating MLflow with cloud platforms (AWS, Azure, GCP)
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Hands-on practice
- Deploying and monitoring models with MLflow
- Optimizing and maintaining deployed ML models
