WhatsApp Contact

+971 54 326 9878

learning@counseltrain.com

+971 4 385 4500

MLOps on Azure: From Data Science to Deployment

4.83(2,235 Rating)

Duration

5 days

Upcoming Batch

22 June 2026

Batch Options

8 hours & 4 hours

Language

English / Arabic

Overview Schedule Dates Course Content FAQs

Course Overview

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.

IT Course Offerings with Flexible Scheduling Options to Fit Your Life

We know that flexibility is key to effective learning and professional development. That’s why CounselTrain offers multiple delivery styles for all IT training courses in Ethiopia, so every learner can access the information they need at their own pace and convenience.

Online Instructor-Led Training

Learn from the comfort of your workplace or at home through live virtual sessions led by expert trainers.

Classroom Training

Participate in interactive, face-to-face training in our top 5-star training facilities in Ethiopia

Onsite Training

Learn a customized curriculum in your workplace to ensure the most impact and team participation.

Overseas Training

Participate in our international training sessions and improve your abilities with world-class instructors.

Request More Information

Schedule Dates

22 June 2026 - 26 June 2026

MLOps on Azure: From Data Science to Deployment

28 September 2026 - 02 October 2026

MLOps on Azure: From Data Science to Deployment

28 December 2026 - 01 January 2027

MLOps on Azure: From Data Science to Deployment

29 March 2027 - 02 April 2027

MLOps on Azure: From Data Science to Deployment

Course Content

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

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

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

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

What is MLOps?
The benefits of MLOps
Key MLOps concepts

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

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

Creating a GitHub Actions workflow
Triggering your workflow
Monitoring your workflow

Creating a branch protection rule
Using branch policies
Enabling required reviews

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

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

Creating a deployment workflow
Testing your deployment
Monitoring your deployment

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

FAQs

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.

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.

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

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.

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

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.