What will you in Azure Machine Learning & MLOps : Beginner to Advance Course
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Understand the complete MLOps lifecycle using Azure Machine Learning.
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Learn how to build, train, and deploy ML models in the Azure cloud environment.
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Implement automated ML pipelines with Azure ML and DevOps tools.
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Gain hands-on experience with version control, containerization, and model monitoring.
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Use tools like MLflow, GitHub Actions, and Azure DevOps for real-world MLOps practices.
Program Overview
Module 1: Introduction to MLOps on Azure
30 minutes
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Overview of MLOps and its importance in scalable ML solutions.
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Role of Azure Machine Learning in operationalizing AI.
Module 2: Azure Machine Learning Workspace Setup
45 minutes
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Creating and configuring Azure ML workspaces and environments.
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Resource setup and permissions for teams.
Module 3: Model Training and Experimentation
60 minutes
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Running and logging experiments using MLflow and Azure ML SDK.
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Hyperparameter tuning and compute management.
Module 4: ML Pipelines & Automation
60 minutes
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Building reusable training pipelines with steps and data dependencies.
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Automating with Azure ML Pipelines and GitHub Actions.
Module 5: Model Registration and Deployment
60 minutes
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Registering models and deploying as web services (ACI/AKS).
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Endpoint management and authentication.
Module 6: Monitoring & Lifecycle Management
45 minutes
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Tracking model performance and detecting data drift.
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Implementing feedback loops and retraining triggers.
Module 7: End-to-End Project Walkthrough
75 minutes
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Building, deploying, and monitoring a complete ML project.
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Best practices, challenges, and governance tips.
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Job Outlook
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High Demand: Azure ML and MLOps are critical in enterprise AI transformation.
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Career Advancement: Great for ML engineers, cloud AI specialists, and DevOps engineers.
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Salary Potential: $110K–$170K/year depending on role and cloud expertise.
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Freelance Opportunities: Azure MLOps consulting, cloud ML deployments, workflow automation.
Explore More Learning Paths
Boost your MLOps and Azure Machine Learning expertise with these carefully curated programs designed to help you build, deploy, and scale machine learning models effectively.
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Related Reading
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What Does a Data Engineer Do? – Explore how robust data engineering supports ML model deployment, scaling, and monitoring in real-world systems.
