40559: Microsoft Cloud Workshop: MLOps Course Overview

40559: Microsoft Cloud Workshop: MLOps Course Overview

The 40559: Microsoft Cloud Workshop: MLOps course is designed to provide a comprehensive understanding of implementing Machine Learning Operations, or MLOps, within the Azure cloud environment. This course offers a blend of whiteboard strategy discussions and practical hands-on labs aimed at building proficiency in MLOps in Azure.

Module 1 focuses on conceptual design, where learners review a case study, create a PoC solution, and present their strategy, ensuring they grasp the complexities and best practices of MLOps strategies. Module 2 is a deep dive into hands-on exercises, where participants engage in creating machine learning models, setting up CI/CD pipelines in Azure DevOps, and monitoring model performance, thus gaining valuable MLOps training.

By the end of the course, learners will be equipped with the skills needed to effectively deploy, manage, and operationalize ML models in Azure, setting a strong foundation for continuous integration and deployment in the context of machine learning projects.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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Course Prerequisites

To ensure you have a successful learning experience in the 40559: Microsoft Cloud Workshop: MLOps course, it is important that you come prepared with the following minimum prerequisites:

  • Basic understanding of Azure services, including Azure DevOps, Azure Machine Learning, and Azure storage solutions.
  • Familiarity with machine learning concepts and common machine learning algorithms.
  • Experience with Python programming, especially in the context of data science and machine learning.
  • Knowledge of version control systems, preferably Git, and continuous integration/continuous delivery (CI/CD) concepts.
  • Ability to navigate and configure cloud-based environments using the Azure portal and Azure CLI (Command Line Interface).
  • Understanding of data science workflows and the role of data preparation, model training, model evaluation, and deployment.
  • Some exposure to DevOps practices and tools within the context of data science and machine learning projects is beneficial.

These prerequisites are designed to ensure that you can actively participate in both the design discussions and hands-on lab portions of the course. If you are not yet comfortable with some of these areas, we recommend exploring foundational courses or resources in Azure, Python programming, and machine learning before enrolling in the MLOps workshop.

Target Audience for 40559: Microsoft Cloud Workshop: MLOps

Course 40559: Microsoft Cloud Workshop: MLOps focuses on implementing and managing machine learning operations within Azure, suitable for IT professionals involved in data science workflows.

Target audience for the course includes:

  • Data Scientists and Machine Learning Engineers seeking to operationalize machine learning models
  • DevOps Engineers aiming to integrate ML workflows into CI/CD pipelines
  • IT Professionals who manage machine learning lifecycle operations
  • Cloud Solution Architects designing MLOps strategies on Azure
  • AI Engineers looking to enhance their skills in model deployment and monitoring
  • Software Developers interested in adding machine learning operations to their skillset
  • Technical Managers overseeing teams that deploy and manage machine learning solutions
  • Data Engineers who support data science teams with operational tooling

Learning Objectives - What you will Learn in this 40559: Microsoft Cloud Workshop: MLOps?

Introduction to Learning Outcomes and Concepts Covered

The 40559: Microsoft Cloud Workshop: MLOps course equips students with comprehensive skills to implement and manage machine learning operations (MLOps) using Azure services, focusing on compliance, automation, and deployment pipelines.

Learning Objectives and Outcomes

  • Understand the customer case study to identify business requirements and challenges in implementing MLOps.
  • Design a proof-of-concept solution that integrates machine learning workflows with Azure DevOps.
  • Develop skills to present and communicate MLOps solutions effectively.
  • Learn to create and evaluate machine learning models while ensuring compliance with industry standards and regulations.
  • Gain proficiency in registering and managing models within the Azure ML service.
  • Set up a new project in Azure DevOps to enable version control and collaboration for MLOps processes.
  • Construct and configure a Build Pipeline to automate machine learning model training and validation.
  • Establish a Release Pipeline to automate the deployment of machine learning models into production environments.
  • Execute and monitor Build and Release Pipelines to ensure seamless CI/CD for machine learning projects.
  • Evaluate the performance of deployed machine learning models and understand how to iterate and improve models based on operational feedback.