Technologies / Artificial Intelligence

MLflow - lifecycle management of ML models

The training provides an in-depth understanding of the MLflow platform and its application in managing the full lifecycle of machine learning models. The program covers all key components of MLflow, from tracking experiments to deploying models, with a special focus on the practical aspects of implementation. Participants learn to effectively organize ML projects, track experiments, and automate model development and deployment processes.

Issues

MLflow architecture
Tracking experiments
Model register
Versioning
ML pipelines
Deployment of models
Process automation
Integration of tools
Monitoring of models
Artifact management
Reproducibility of experiments
MLOps best practices

Benefits

The participant will develop practical skills in ML project management using the MLflow platform
He or she will gain in-depth knowledge of organizing and tracking machine learning experiments
Will learn how to effectively manage model versions and project artifacts
Will learn techniques for automating model development and deployment processes
Will develop the ability to integrate MLflow with other ML ecosystem tools
Will gain the ability to build repeatable and scalable ML processes

Who is this training for?

Data Scientists working on multiple models

ML Engineers responsible for implementations

DevOps Engineers supporting ML teams

MLOps specialists

Model management engineers

Research Scientists conducting ML experiments

Team Leaders of ML teams

Prerequisites

Practical experience in ML projects
Knowledge of Python
Basic knowledge about MLOps
Experience in working with ML models

Training program

MLflow components
Configuring the environment
Organization of projects
Tracking experiments
Tracking experiments
Artifact management
Visualize the results

Model management
Packaging of models

Versioning
Implementing models
Automation and integration

Integration with ML tools
Continuous Deployment
Production monitoring

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For MLflow - lifecycle management of ML models we recommend: Practical experience in ML projects; Knowledge of Python; Basic knowledge about MLOps.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data Scientists working on multiple models; ML Engineers responsible for implementations; DevOps Engineers supporting ML teams.