Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
22 Hour(s) 42 Minute(s)
Language
English
Taught by
Cyberdefense Learning
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Design, Train & Deploy ML Models on Azure using AutoML, Pipelines, MLOps, and LLMs with Prompt Engineering & RAG
Build and Deploy Intelligent Machine Learning Solutions Using Microsoft Azure
This course is your complete guide to mastering data science workflows in the cloud. Designed for professionals who want to go beyond experimentation and take their machine learning models into production, it covers every stage of the ML lifecycle using Azure’s powerful suite of tools.
Whether you're looking to scale your data science capabilities, prepare for the DP-100 certification, or enhance your organization’s AI capabilities, this course delivers hands-on experience with the platforms and practices used in real-world enterprise environments.
You will gain hands-on expertise in:
Designing effective ML architectures on Azure
Choosing the right dataset formats and compute targets
Structuring experiments for scalability and performance
Integrating Git and CI/CD pipelines for streamlined collaboration
Preparing and managing data at scale
Wrangling and transforming data using notebooks and Synapse Spark
Accessing and versioning datasets via Azure ML datastores
Building and sharing environments across workspaces
Training models using both automated and custom approaches
Leveraging AutoML for classification, regression, vision, and NLP
Developing custom training scripts using Python and MLflow
Tuning hyperparameters for optimal model performance
Building and managing reproducible ML pipelines
Creating modular training components
Passing and transforming data between pipeline steps
Scheduling, monitoring, and debugging workflows
Deploying models for real-time and batch inference
Configuring online endpoints for scalable predictions
Setting up batch endpoints for large-scale processing jobs
Implementing secure and compliant deployment workflows
Optimizing advanced AI models and LLMs
Selecting and fine-tuning large language models
Designing prompt engineering strategies for accuracy and context
Implementing Retrieval Augmented Generation (RAG) systems
Ensuring responsible AI and operational excellence
Applying fairness, transparency, and explainability principles
Using MLflow for experiment tracking and model governance
Automating retraining and monitoring in production
If you’re ready to move beyond theory and start building machine learning systems that solve real business problems, this course is designed for you. It’s perfect for learners who want structured guidance, practical tools, and hands-on labs that mirror what professionals do in industry every day.
Course Content
- 73 section(s)
- 195 lecture(s)
- Section 1 Module 1 - Lesson 1 - What is Azure Machine Learning
- Section 2 Module 1 - Chapter 2 - Azure ML Architecture Deep Dive
- Section 3 Module 1 - Chapter 3 - Navigating the Azure ML Studio Interface
- Section 4 Module 1 - Chapter 4 - Workspace Resources and Asset Types
- Section 5 Module 1 - Chapter 5- Working with Visual Studio Code & Azure ML
- Section 6 Module 1 - Chapter 6 - Understanding Workspace Editions
- Section 7 Module 1 - Chapter 7 - Creating an Azure ML Workspace
- Section 8 Module 1 - Chapter 8 - Creating Compute Resources in Azure ML
- Section 9 Module 1- Chapter 9 - Exploring Azure ML with the CLI
- Section 10 DP-100 Module 1 Quiz – Azure Machine Learning Fundamentals
- Section 11 Module 2 - Chapter 1- Introduction to Azure ML Designer
- Section 12 Module 2 - Chapter 2 - Exploring the Designer Interface
- Section 13 Module 2- Chapter 3 - No-Code vs Code-Based Machine Learning
- Section 14 Module 2 - Chapter 4 - Building a Training Pipeline with Designer
- Section 15 Module 2 - Chapter 5 - Interpreting Experiment Results in Designer
- Section 16 Module 2 - Chapter 6 - Creating an Inference Pipeline from Training Pipeline
- Section 17 Module 2- Chapter 7 - Real-Time vs. Batch Inference in Designer
- Section 18 Module 2 - Chapter 8 - Deploying a Model with Designer to ACI or AKS
- Section 19 DP-100 Module 2 Quiz-Azure ML Designer
- Section 20 Module 3 - Section 1. What Are Experiments and Runs in Azure ML?
- Section 21 Module 3 - Section 2- Anatomy of a Training Run in Azure ML
- Section 22 Module 3 -Section 3- Logging Metrics and Monitoring Runs
- Section 23 Module 3 - Section 4. Using Compute Targets: Local vs. Remote
- Section 24 Module 3 - Section 5. Experimentation Best Practices
- Section 25 DP-100 Modlule 3 Quiz- Azure ML Experimentation, Metrics, and Compute
- Section 26 Module 4 -Section 1 - Introduction to Data Management in Azure ML
- Section 27 Module 4 -Section 2 - Understanding Datastores in Azure ML
- Section 28 Module 4 - Section 3. Registering and Using Datastores
- Section 29 Module 4 - Section 4 - Creating and Registering Datasets in Azure ML
- Section 30 Module 4 - Section 5 - Mounting vs. Downloading Data
- Section 31 Module 4 -Section 6 - Best Practices for Managing Data in Azure ML
- Section 32 Module 4- Managing Data and Datastores in Azure ML - Quiz
- Section 33 Module 5: Section 1. Introduction to Compute in Azure ML
- Section 34 Module 5: Section 2. Compute Instances vs. Compute Clusters
- Section 35 Module 5: Section 3. Attached Compute (Advanced Concepts)
- Section 36 Module 5: Section 4. Environments in Azure ML: What and Why
- Section 37 Module 5: Section 5 -Creating Custom Environments
- Section 38 Module 5- Section 6- Submitting Jobs to Compute Clusters
- Section 39 Module 5-Azure ML Compute & Environments- Quiz
- Section 40 Module 6- Section 1- What Is an ML Pipeline in Azure ML?
- Section 41 Module 6 - Section 2. Components of a Pipeline Step
- Section 42 Module 6 - Section 3. Creating a Simple Two-Step Pipeline
- Section 43 Module 6 - Section 4 - Passing Data Between Pipeline Steps
- Section 44 Module 6 - Section 5. Publishing Pipelines for Reuse
- Section 45 Module 6 - Section 6. Pipeline Scheduling and Automation Options
- Section 46 Module 6 - Section 7. Best Practices for Pipelines
- Section 47 Module 6- ML Pipelines in Azure ML - Quiz
- Section 48 Module 7 - Overview of Deployment Targets in Azure ML
- Section 49 Module 7 - Creating a Real-time Inference Endpoint
- Section 50 Module 7 - Consuming Real-time Endpoints via REST API
- Section 51 Module 7 - Creating a Batch Inference Pipeline
- Section 52 Module 7- Versioning and Updating Deployments
- Section 53 Module 7- Real-World Deployment in Azure ML- Quiz
- Section 54 Module 8 - Hyperparameters vs. Model Parameters
- Section 55 Module 8 - Azure ML Hyperparameter Tuning (HyperDrive / SweepJob)
- Section 56 Module 8 - Performing Hyperparameter Tuning in Azure ML
- Section 57 Module 8 - Introduction to Automated Machine Learning (AutoML) Type
- Section 58 Module 8 - Running an AutoML Experiment in Azure ML
- Section 59 Module 8 - Understanding AutoML Output & Explainability
- Section 60 Module 8 - Responsible AI Features in AutoML
- Section 61 Module 8- Hyperparameter Tuning & AutoML- Quiz
- Section 62 Module 9 - Why Model Interpretability Matters ?
- Section 63 Module 9 - Model Explanation Techniques in Azure ML
- Section 64 Module 9 -Reviewing AutoML Explanations
- Section 65 Module 9 - Using the Explanation Client and SDK
- Section 66 Module 9 - Responsible AI & Fairness: What Azure ML Covers
- Section 67 Module 9- Model Interpretability & Responsible- Quiz
- Section 68 Module 10 - Why Monitor ML Models in Production?
- Section 69 Module 10 - Overview of Monitoring Tools in Azure ML
- Section 70 Module 10 - Monitoring Model Services with Application Insights
- Section 71 Module 10 - Logging Custom Metrics in score.py
- Section 72 Module 10 - Monitoring Data Drift in Azure ML
- Section 73 Module 10- Monitoring Deployed ML Models- Quiz
What You’ll Learn
- Learn how to architect ML workflows using Azure services, from data ingestion to model deployment.
- Create, configure, and manage workspaces, datastores, compute targets, and environments.
- Use Azure Notebooks and Synapse Spark to clean, transform, and explore datasets.
- Train models automatically for tabular, vision, and NLP tasks while applying responsible AI guidelines.
- Perform hyperparameter tuning using Bayesian optimization, random search, and early stopping.
- Record model training runs, metrics, parameters, and artifacts for robust experimentation tracking.
- Design modular ML pipelines that can be automated, reused, and scaled in production.
- Serve real-time and batch predictions using Azure endpoints with appropriate compute configurations.
- Apply fairness, explainability, and model management best practices throughout the ML lifecycle.
- Fine-tune, prompt-engineer, and deploy LLMs using Azure OpenAI, Prompt Flow, and Retrieval Augmented Generation (RAG).
Skills covered in this course
Reviews
-
SShrishti Rawat
Excellent practical labs with Azure Machine Learning control-plane and MLOps workflows.
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SSri Hari K
As someone with basic Python/ML knowledge but zero cloud experience, this class gave me a crash course in Azure ML: workspace setup, experiment tracking with MLflow, and scalable training. Exactly what I needed for real-world data science.
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PPranay K
Preparing for the DP-100 exam felt way less intimidating after taking this — the sections on hyperparameter tuning, data exploration, and Azure endpoints were exactly aligned with Microsoft’s exam skills guide. I feel ready.
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DDhanush Chowdary
is a solid curriculum that allows you to learn a broad aspects of ML and data science for production. The biggest challenge is the lack of materials. Since they switched to SDK v2 last year, there aren’t any practice exams that are up-to-date.”
