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MLOps on Azure: From Data Science to Deployment

  • 4.83(2,235 Rating)

Duration

5 days

Upcoming Batch

28 September 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.

Flexible Training Delivery Options to Fit the Learner

We know that practitioners and organisations in Oman have different schedules, locations, and learning methods. That’s why CounselTrain offers a full range of delivery formats so that high-quality IT training is available to every participant, whether in Muscat, Sohar, Nizwa or Salalah.

Instructor-led classroom training in Muscat- Fully equipped, professional training environment. Live online training offers virtual classes with expert trainers, accessible anywhere in Oman. On-site corporate training features customised programs delivered at your organisation’s premises across Oman.

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 Oman

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.
This course includes:
  • Soft copy of Courseware
  • Certificate of Course Completion
  • Experienced and Professional Trainer
  • Lab and Exercises During Training
  • Sample Questions for Exam Prepration
  • Mock Test on the Last Day of Training
  • Exam Registration Guidance
  • 5-Star Training Environmment
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Schedule Dates

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
05 July 2027 - 09 July 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.

 

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