MLOps on Azure: From Data Science to Deployment

4.83(2,235 Ratings)

Duration

5 Days

Upcoming Batch

28 December 2026 - 01 January 2027

Batch Options

8 hours & 4 hours

Language

English / Arabic

MLOps on Azure: From Data Science to Deployment

The “MLOps on Azure: From Data Science to Deployment” course from Microsoft is designed to equip data scientists and machine learning engineers with the skills and knowledge needed to streamline the end-to-end machine learning lifecycle. This comprehensive course covers the principles of MLOps, emphasizing the importance of collaboration between data scientists and operations teams to ensure robust, scalable, and reliable ML solutions. Participants will learn how to utilize Azure Machine Learning to manage the entire workflow from data ingestion and preparation to model training, deployment, and monitoring. The course delves into best practices for version control, continuous integration, continuous delivery, and automated monitoring, ensuring that machine learning models can be reliably reproduced and deployed. By the end of the course, learners will have hands-on experience in implementing MLOps practices using Azure, enabling them to accelerate model development and deployment, reduce operational overhead, and enhance the overall performance and reliability of machine learning applications.

Course Content

13 modules · 51 topics

01Module 1: Designing and Preparing a Machine Learning Solution6 topics

Identify and analyze business requirements for a machine learning solution
Design a machine learning solution architecture
Select appropriate compute resources for a machine learning solution
Provision Azure resources for a machine learning solution
Manage data storage for machine learning workloads
Implement security and access controls for machine learning resources

02Module 2: Exploring Data and Training Models 5 topics

Explore and prepare data for machine learning
Select and apply appropriate data featurization techniques
Select and train appropriate machine learning models
Evaluate and compare machine learning models
Optimize machine learning models for performance

03Module 3: Preparing a Model for Deployment 5 topics

Package and deploy machine learning models
Implement model governance and lifecycle management
Monitor and troubleshoot machine learning models
Manage and optimize machine learning pipelines
Implement responsible AI principles

04Module 4: Deploying and Retraining a Model 5 topics

Deploy machine learning models to production environments
Implement continuous integration and continuous delivery (CI/CD) for machine learning solutions
Monitor and manage deployed machine learning models
Retrain and update machine learning models
Troubleshoot and debug deployed machine learning models

05Module 5: Introduction to MLOps 3 topics

What is MLOps?
The benefits of MLOps
Key MLOps concepts

06Module 6: Introduction to Azure DevOps & GitHub 6 topics

Introduction to CI/CD tools: Azure DevOps & GitHub
Azure Boards
Azure Repos & GitHub
Azure Pipeline (Build & Release) and GitHub Actions
Introduction to Infrastructure as a Code (IaaC) in Azure Pipeline
Azure Artifacts

07Module 7: Setting up your MLOps environment 3 topics

Creating an Azure Machine Learning workspace
Connecting to your workspace
Setting up a Git repository

08Module 8: Automating your ML workflow with GitHub Actions 3 topics

Creating a GitHub Actions workflow
Triggering your workflow
Monitoring your workflow

09Module 9: Protecting your main branch 3 topics

Creating a branch protection rule
Using branch policies
Enabling required reviews

10Module 10: Automating code checks 3 topics

Setting up continuous integration (CI)
Running code checks
Configuring code coverage

11Module 11: Training, testing, and deploying models 3 topics

Creating and managing environments
Training and testing models
Deploying models to Azure

12Module 12: Automating model deployment 3 topics

Creating a deployment workflow
Testing your deployment
Monitoring your deployment

13Module 13: Project Deployment using Azure MLOps (V2) Accelerator Solutions3 topics

ntroduction and Structure of Accelerator Solutions
Supported Machine Learning Patterns
Deployment of ML Solution using

Schedule Dates

4 upcoming batches

Session Type

Physical sessions run at our training facility.

MLOps on Azure: From Data Science to Deployment
Batch Dates	Duration	Batch Options	Language
28 December 2026 - 01 January 2027Next	5 Days	8 hours & 4 hours	English / Arabic
29 March 2027 - 02 April 2027	5 Days	8 hours & 4 hours	English / Arabic
05 July 2027 - 09 July 2027	5 Days	8 hours & 4 hours	English / Arabic
11 October 2027 - 15 October 2027	5 Days	8 hours & 4 hours	English / Arabic

Can’t find a suitable date? Request a schedule that fits your team.

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FAQs

What are the prerequisites for this course?

Basic knowledge of machine learning concepts and experience with Microsoft Azure are recommended. Familiarity with programming (preferably in Python) and cloud computing concepts will also be beneficial.

How is the course structured?

The course is divided into several modules covering:

Developing ML Models: Using Azure ML service to build and train models.
Version Control: Managing different versions of ML models.
Model Validation: Strategies for validating model performance.
Retraining Pipelines: Creating pipelines for model retraining.
Model Deployment: Deploying models into production.
Model Monitoring: Monitoring models for performance and reliability.

Are there any hands-on projects in this course?

Yes, the course includes practical hands-on labs and projects that provide experience in applying the concepts learned to real-world scenarios.

What will I learn from this course?

You will learn to streamline the ML Life Cycle using Azure’s MLOps capabilities. This includes developing, validating, deploying, and monitoring ML models, as well as managing model versions and creating retraining pipelines.

How can I enroll in the course?

To enroll, visit the CounselTrain website and follow the registration process. For additional assistance, you can contact our support team.

What industries benefit from MLOps?

MLOps is valuable across various industries, including finance, healthcare, technology, and manufacturing. It helps organizations manage and operationalize ML models efficiently, leading to improved decision-making and business outcomes.

This course includes:

Soft copy of Courseware
Certificate of Course Completion
Experienced and Professional Trainer
Lab and Exercises During Training
Sample Questions for Exam Preparation
Mock Test on the Last Day of Training
Exam Registration Guidance
5-Star Training Environment

Upcoming Batch

28 December 2026 - 01 January 2027

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