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MLflow

Learn end-to-end model tracking, packaging, and deployment with MLflow for efficient machine learning lifecycle management.
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Save 59% Offer ends on 31-Dec-2025
Course Duration: 10 Hours
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MLflow is an open-source platform that streamlines the machine learning lifecycle, making it easier to track experiments, package models, and deploy them across various environments. This course introduces you to MLflow’s four main components—Tracking, Projects, Models, and Registry—and shows how to integrate them into real-world ML workflows.
 
Whether you're an individual practitioner or part of a large data team, MLflow helps you manage the complexity of model development and deployment. You'll learn how to record experiments with MLflow Tracking, organize reproducible workflows with MLflow Projects, deploy models to cloud and local environments, and manage model versions through the MLflow Model Registry.
 
By the end of the course, you’ll know how to implement a structured and scalable MLOps strategy using MLflow.
 
What You Will Gain
  • Proficiency in using MLflow Tracking to log experiments

  • Experience packaging ML models with MLflow Projects and Models

  • Skills in deploying MLflow models to REST APIs, AWS SageMaker, or Azure ML

  • Confidence in managing model versions and transitions using MLflow Registry

Who This Course Is For
  • ML Engineers managing large model pipelines

  • Data Scientists working on multiple experiments

  • DevOps professionals enabling MLOps within their teams

  • AI Engineers focused on production readiness

  • Anyone implementing reproducible ML workflows

How to Use This Course Effectively
  1. Start with the fundamentals of ML lifecycle problems MLflow solves

  2. Practice logging and comparing experiments with real datasets

  3. Use MLflow Projects to package models with reproducible environments

  4. Deploy models via MLflow to both local servers and cloud platforms

  5. Register models and simulate production workflows using the Model Registry

  6. Use the final project to build and showcase a complete MLflow pipeline

Why Learn MLflow?
 
As machine learning projects scale, tracking models, managing versions, and deploying across environments becomes complex. MLflow is a popular and versatile solution trusted by industry leaders. Learning MLflow not only makes your ML workflow more efficient but also prepares you for MLOps roles that require reproducibility and deployment skills.

Course/Topic 1 - Coming Soon

  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

    • 01:20
Course Objectives Back to Top

By the end of this course, you will be able to:

  • Track and visualize machine learning experiments

  • Package models with reproducible environments using MLflow Projects

  • Serve models via REST APIs using MLflow Models

  • Register and transition models in the MLflow Model Registry

  • Integrate MLflow with cloud platforms and orchestration tools

  • Apply MLOps best practices for team collaboration

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to MLflow

  • Overview of the ML lifecycle

  • Introduction to MLflow and its components

  • Setting up MLflow locally and on the cloud

Module 2: MLflow Tracking

  • Logging metrics, parameters, and artifacts

  • Comparing runs and experiment organization

  • Custom logging with callbacks and autologging

Module 3: MLflow Projects

  • Creating reproducible ML pipelines

  • Packaging models with Conda and Docker

  • Entry points and project parameters

Module 4: MLflow Models

  • Model packaging and standard formats

  • Serving models with REST API

  • Exporting to SageMaker, Azure ML, or Spark

Module 5: MLflow Model Registry

  • Registering and versioning models

  • Staging and production workflows

  • Model governance and access control

Module 6: CI/CD and MLflow Integration

  • Automating model deployment

  • Integrating MLflow with Jenkins, GitHub Actions

  • Monitoring deployments

Module 7: Final Project

  • Complete MLflow lifecycle with experiment tracking

  • Model packaging, deployment, and registry use

  • End-to-end documentation and demo

Certification Back to Top

Learners who complete the course will receive a Certificate of Completion from Uplatz, confirming their ability to use MLflow for experiment tracking, model packaging, deployment, and lifecycle management within modern ML workflows.

Career & Jobs Back to Top

Upon completion, learners will be prepared for roles like:

  • MLOps Engineer

  • Machine Learning Engineer

  • ML Workflow Automation Specialist

  • Model Lifecycle Manager

  • AI Engineer with DevOps Integration

Interview Questions Back to Top
  1. What is MLflow and why is it important in machine learning projects?

  2. What are the core components of MLflow?

  3. How do you log experiments using MLflow Tracking?

  4. What’s the difference between MLflow Projects and MLflow Models?

  5. How do you serve a model with MLflow as a REST API?

  6. What is the purpose of the MLflow Model Registry?

  7. How can MLflow help with reproducibility in ML pipelines?

  8. How do you deploy MLflow models to AWS SageMaker or Azure ML?

  9. What are the benefits of using autologging in MLflow?

  10. How would you implement a CI/CD pipeline using MLflow for deployment?

Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

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A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
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b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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BUY THIS COURSE (USD 17 USD 41)