MLflow Training

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

The MLflow training is an intensive two-day course that focuses on the practical application of MLflow for managing the lifecycle of machine learning models. The training program is designed so that 80% of the time is dedicated to practical workshops and 20% to theory. Participants will learn how to effectively register, track, deploy, and monitor ML models, working on real examples and use cases.

• Data scientists and data engineers who want to expand their skills in managing the lifecycle of ML models
• IT specialists who want to use MLflow to automate ML processes in their organizations
• Developers and ML engineers looking to deploy and monitor ML models in a production environment
• Individuals with a basic knowledge of programming in Python and a foundational understanding of machine learning
• Experience with data analysis tools will be an additional asset

• 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

• DAY 1: INTRODUCTION TO MLFLOW AND MODEL MANAGEMENT BASICS
   • Basics of MLflow
   • Introduction to MLflow and its architecture
   • Installation and configuration of MLflow

  • TRACKING EXPERIMENTS WITH MLFLOW TRACKING
     • Registering and tracking ML experiments
     • Managing metadata and results of experiments

  • MODELING AND STORING MODELS
     • Registering models with MLflow Models
     • Storing models in a model repository
     • Registering and tracking experiments
     • Practical exercises on registering and tracking ML experiments
     • Analyzing and interpreting experiment results

• DAY 2: ADVANCED TECHNIQUES AND PRACTICAL APPLICATIONS
   DEPLOYING MODELS WITH MLFLOW PROJECTS
   • Creating and configuring MLflow projects
   • Deploying models on various platforms

  • MONITORING MODELS WITH MLFLOW MODELS
     • Monitoring deployed ML models
     • Updating and optimizing deployed models

  • INTEGRATION WITH OTHER TOOLS AND SERVICES
     • Integrating MLflow with popular ML frameworks (TensorFlow, PyTorch, Scikit-Learn)
     • Integrating MLflow with cloud platforms (AWS, Azure, GCP)

  • DEPLOYING AND MONITORING THE MODEL
     • Practical exercises on deploying the MLflow model
     • Monitoring and optimizing the deployed model

16 h/2 days