Azure MLOps Course Overview

Course Prerequisites

Target Audience for Azure MLOps

Azure MLOps course by Koenig Solutions focuses on integrating machine learning workflows with Azure DevOps for streamlined deployment and management.

Target Audience for the Azure MLOps Course:

• Data Scientists looking to operationalize machine learning models
• DevOps Engineers aiming to incorporate ML workflows into the CI/CD pipeline
• ML Engineers seeking to standardize and scale machine learning operations
• Cloud Engineers specializing in Azure services and interested in ML deployment
• IT Professionals responsible for managing and deploying ML solutions
• Software Developers interested in adding machine learning operations to their skillset
• Technical Project Managers overseeing ML projects
• AI Architects designing and implementing scalable AI solutions on Azure
• Quality Assurance Engineers involved in testing ML systems
• Technical Leads coordinating cross-functional teams for ML initiatives

Learning Objectives - What you will Learn in this Azure MLOps?

Introduction to the Course's Learning Outcomes:

Gain proficiency in Azure ML Ops to streamline your machine learning workflow, from experiment management and version control to model deployment and monitoring, ensuring repeatable and scalable ML processes.

Learning Objectives and Outcomes:

• Understand the concept and importance of MLOps within the Azure ecosystem for enhancing the machine learning lifecycle.
• Set up and configure an Azure ML workspace and integrate it with Azure DevOps and GitHub for continuous integration and delivery.
• Create, run, and analyze Azure ML experiments to iteratively improve machine learning models.
• Build, execute, and manage Azure ML Pipelines to automate and scale machine learning workflows.
• Implement version control best practices for machine learning assets to maintain a history of changes and facilitate collaboration.
• Collaborate effectively with team members using Azure ML Ops tools, improving productivity and reducing errors in the ML development process.
• Deploy machine learning models reliably to Azure Kubernetes Service (AKS) and manage deployment targets within Azure ML.
• Monitor ML deployments using Application Insights and adopt strategies for scaling and updating models in production.
• Troubleshoot common issues in Azure ML deployments to minimize downtime and ensure high-quality model performance.
• Apply best practices for MLOps in Azure and plan the next steps for incorporating these practices into your organizational workflow.