MLFlow Training and Certification in Astana

MLFlow Training and Certification in Astana, Kazakhstan

The course provides an understanding of managing the complete lifecycle of machine learning models.

Enroll Now

MLFlow Training and Certification is a professional training program delivered by ProgNXT in Astana, Kazakhstan. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this MLFlow Training and Certification course with hands-on, instructor-led sessions. This training on MLFlow provides a comprehensive understanding of managing the complete lifecycle of machine learning models, including experiment...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: BFE16
21 Hrs
Last Updated: April 2026

Course Rating5/5

This training on MLFlow provides a comprehensive understanding of managing the complete lifecycle of machine learning models, including experiment tracking, model packaging, and deployment. Participants will explore MLFlow’s four key components—Tracking, Projects, Models, and Model Registry—and learn how to streamline MLOps workflows using practical, real-world examples.

Welcome to the official MLFlow Training and Certification certification program in Astana, Kazakhstan. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

A basic understanding of machine learning concepts and workflows.
Familiarity with Python programming and common ML libraries (e.g., Scikit-learn, TensorFlow, or PyTorch).
Basic knowledge of version control (e.g., Git) and containerization (e.g., Docker).
Exposure to cloud platforms is helpful but not mandatory.

By the end of the training, participants will:

Understand the architecture and components of MLFlow.
Implement experiment tracking for machine learning projects.
Package ML code into reproducible formats for sharing and deployment.
Use MLFlow Models and Model Registry for model management.
Deploy ML models to various environments (local, cloud, or Kubernetes).
Monitor and manage models in production.

Participants will:

Set up and configure MLFlow for tracking and managing ML workflows.
Track experiments and parameter tuning with MLFlow Tracking.
Package and run ML projects in isolated environments using MLFlow Projects.
Register, manage, and deploy ML models with MLFlow Models and Model Registry.
Integrate MLFlow with tools like Docker, Kubernetes, and cloud platforms.
Apply best practices for MLOps workflows using MLFlow.

Participants will be assessed on:

Understanding the MLFlow architecture and core functionalities.
Tracking ML experiments and hyperparameter tuning.
Packaging and sharing ML projects using MLFlow.
Managing model lifecycles with MLFlow Models and Model Registry.
Deploying ML models to different environments.
Best practices for MLOps using MLFlow.

Upon successfully completing the training and assessments, participants will receive the ProgNXT Certificate of Completion, validating their expertise in using MLFlow for end-to-end MLOps workflows.

Introduction and Experiment Tracking

Session 1: Introduction to MLFlow
- Overview of MLOps and the role of MLFlow.
- MLFlow architecture and components: Tracking, Projects, Models, and Model Registry.
- Installing and configuring MLFlow in local and cloud environments.
Session 2: MLFlow Tracking
- Experiment tracking: Parameters, metrics, and artifacts.
- Integration with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
Session 3: Advanced Experiment Tracking
- Organizing experiments with tags and nested runs.
- Automated tracking using autologging features.

Packaging and Managing ML Projects

Session 4: MLFlow Projects
- Structuring ML projects with MLFlow Projects.
- Running and sharing reproducible ML workflows.
Session 5: MLFlow Models
- Saving and packaging models with MLFlow Models.
- Supported flavors (e.g., Scikit-learn, TensorFlow, PyTorch).
Session 6: MLFlow Model Registry
- Managing model lifecycles (staging, production, archived).
- Model versioning and governance.

Deployment and Advanced Integrations

Session 7: Model Deployment
- Deployment options: Local, Docker, Kubernetes, and cloud services.
Session 8: Monitoring and Managing Models
- Monitoring deployed models for performance and drift.
- Integration with monitoring tools (e.g., Prometheus, Grafana).
Session 9: Best Practices and Capstone Project
- Best practices for MLOps with MLFlow.
- Capstone project: End-to-end MLFlow workflow implementation.
- Participant presentations and feedback.

This MLFlow training equips participants with the skills and tools to streamline MLOps workflows and manage machine learning models effectively. Through hands-on sessions, participants gain practical experience to apply MLFlow in production settings, enhancing their organization's machine learning lifecycle management capabilities.

Participants will receive:

Comprehensive course slides and reference documentation.
Pre-configured Jupyter notebooks for hands-on exercises.
Sample ML projects for practice and experimentation.
Access to a cloud-based environment for MLFlow workflows.
Case studies and real-world scenarios for practical implementation.
Step-by-step guides for deploying MLFlow on local and cloud environments.

Participants will be evaluated through a hands-on project and a final quiz.
ProgNXT Certificate of completion will be awarded to participants who successfully complete the assessments.

MLFlow Training and Certification Events in astana

Online Events

Global Region	Location	Start Date	End Date	Action
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌

More Upcoming Session Dates

November 2026

📍 Astana, Kazakhstan

3rd Nov 2026→5th Nov 2026

🌐 Online Instructor-Led

8th Nov 2026→10th Nov 2026

📍 Astana, Kazakhstan

21st Nov 2026→23rd Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→29th Nov 2026

December 2026

🌐 Online Instructor-Led

5th Dec 2026→7th Dec 2026

📍 Astana, Kazakhstan

12th Dec 2026→14th Dec 2026

🌐 Online Instructor-Led

21st Dec 2026→23rd Dec 2026

📍 Astana, Kazakhstan

26th Dec 2026→28th Dec 2026

January 2027

📍 Astana, Kazakhstan

7th Jan 2027→9th Jan 2027

🌐 Online Instructor-Led

12th Jan 2027→14th Jan 2027

📍 Astana, Kazakhstan

18th Jan 2027→20th Jan 2027

🌐 Online Instructor-Led

24th Jan 2027→26th Jan 2027

Events for other courses in Astana, Kazakhstan

Global Region	Course Name	Start Date	End Date	Action
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌

MLFlow Training and Certification in Astana