Azure MLOps from Microsoft

Overview Schedule Dates Course Content Faqs

Course Overview

Azure MLOps is a comprehensive set of practices and tools designed to streamline and enhance the management of machine learning (ML) workflows on Microsoft’s Azure cloud platform. It integrates machine learning operations into the broader DevOps practices, facilitating the efficient deployment, monitoring, and governance of ML models. Azure MLOps aims to address the complexities of ML lifecycle management by automating processes such as model training, testing, and deployment, ensuring that models are not only accurate but also reliable and scalable. By leveraging Azure’s robust infrastructure and advanced tools, Azure MLOps empowers organizations to accelerate their ML initiatives, improve collaboration among data scientists and engineers, and achieve better outcomes through a unified approach to managing ML projects.

Learning Options

Online Instructor Led

Learn from the comforts of your home/office with our expert trainers via online platforms.

Classroom Training

Face-to-face physical classroom training with max interactions at our 5* training venues.

Onsite Training

Learn in your own environment from our expert trainers for maximum impact.

Overseas Training

Choose your desired country for learning with our expert trainers.

Schedule Dates

Azure MLOps from Microsoft

19 February 2025 - 20 February 2025

Azure MLOps from Microsoft

19 May 2025 - 20 May 2025

Azure MLOps from Microsoft

19 August 2025 - 20 August 2025

Azure MLOps from Microsoft

19 November 2025 - 20 November 2025

Course Content

What is Azure ML Ops?
Why do we need Azure ML Ops?
Overview of the Azure ML Ops workflow

Creating an Azure ML workspace
Configuring Azure DevOps and GitHub for Azure ML Ops
Setting up the Azure ML Ops service connection

Overview of Azure ML Experiment
Creating an Azure ML Experiment
Running an Azure ML Experiment
Viewing experiment results and logs

Introduction to Azure ML Pipelines
Creating an Azure ML Pipeline
Running an Azure ML Pipeline
Monitoring and Troubleshooting Azure ML Pipelines

Setting up version control for Azure ML assets
Collaborating with team members using Azure ML Ops
Configuring continuous integration and delivery for Azure ML projects

Introduction to Azure ML Model Deployment
Creating a scoring script and environment file
Creating an Azure ML Deployment Target
Deploying a model to Azure Kubernetes Service (AKS)

Monitoring Azure ML Deployments with Application Insights
Scaling and Updating Azure ML Deployments
Scaling and Updating Azure ML Deployments

Best practices for using Azure ML Ops
Recap of key concepts and features
Next steps for implementing Azure ML Ops in your organization

FAQs

Basic knowledge of machine learning concepts and familiarity with Azure services are recommended. Some experience with Azure DevOps and GitHub will be beneficial but not mandatory.

The course is divided into eight modules:

Module 1: Introduction to Azure MLOps – Overview of MLOps and its workflow.
Module 2: Environment Setup – Setting up Azure ML workspaces and integrating with Azure DevOps and GitHub.
Module 3: Experimentation – Creating and running machine learning experiments.
Module 4: Building Pipelines – Developing and managing ML pipelines.
Module 5: Collaboration and Version Control – Best practices for team collaboration and version control.
Module 6: Model Deployment – Deploying models, including to Azure Kubernetes Service (AKS).
Module 7: Monitoring and Maintenance – Monitoring and maintaining model deployments.
Module 8: Implementing MLOps Strategies – Applying MLOps strategies within organizations and preparing for Azure MLOps certification.

You will learn how to set up and manage Azure ML workspaces, create and run ML experiments, build and manage ML pipelines, collaborate effectively on ML projects, deploy models, and monitor and maintain deployments.

Yes, the course includes practical hands-on labs and projects that allow you to apply the concepts learned in real-world scenarios.

Azure MLOps is beneficial across various industries, including finance, healthcare, retail, and technology. It helps organizations manage and operationalize machine learning models efficiently, leading to improved decision-making and business outcomes.

You can enroll by visiting the CounselTrain website and following the registration process. For further assistance, you can contact our support team.