×

MLOps on Azure: From Data Science to Deployment

4.83(2,235 Rating)

Duration

5 days

Language

English / Arabic

Batch Options

8 hours & 4 hours

Upcoming Batch

August 19, 2024

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.

Multiple Routes of Delivery to
Fit Your Timetable

We understand that flexibility is key to effective learning and development, especially in today’s dynamic work environment. That’s why we offer multiple delivery formats for our IT trainings in UAE. Whether you prefer the interaction of in-person classes, the convenience of live virtual training, or the independence of self-paced online learning, we have a solution tailored to your schedule. Our goal is to make professional growth accessible to everyone, allowing you to upskill without compromising your other commitments.

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.

Request More Information

Schedule Dates

03 March 2025 - 07 March 2025

MLOps on Azure: From Data Science to Deployment

09 June 2025 - 13 June 2025

MLOps on Azure: From Data Science to Deployment

15 September 2025 - 19 September 2025

MLOps on Azure: From Data Science to Deployment

15 December 2025 - 19 December 2025

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.

Open chat

Hello
How Can We Help You?