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Designing ML Solutions on Azure & Preparing for DP-100 Exam

  Course Overview
  Module 1 - Lesson 1 - What is Azure Machine Learning
  Module 1 - Chapter 2 - Azure ML Architecture Deep Dive
  Module 1 - Chapter 3 - Navigating the Azure ML Studio Interface
  Module 1 - Chapter 4 - Workspace Resources and Asset Types
  Module 1 - Chapter 5- Working with Visual Studio Code & Azure ML
  Module 1 - Chapter 6 - Understanding Workspace Editions
  Module 1 - Chapter 7 - Creating an Azure ML Workspace
  Module 1 - Chapter 8 - Creating Compute Resources in Azure ML
  Module 1- Chapter 9 - Exploring Azure ML with the CLI
  Module 2 - Chapter 1- Introduction to Azure ML Designer
  Module 2 - Module 2 - Exploring the Designer Interface
  Module 2- Module 3 - No-Code vs Code-Based Machine Learning
  Module 2 - Module 4 - Building a Training Pipeline with Designer
  Module 2 - Module 5 - Interpreting Experiment Results in Designer
  Module 2 - Module 6 - Creating an Inference Pipeline from Training Pipeline
  Module 2- Module 7 - Real-Time vs. Batch Inference in Designer
  Module 2 - Module 8 - Deploying a Model with Designer to ACI or AKS
  Module 3 - Section 1. What Are Experiments and Runs in Azure ML?
  Module 3 - Section 2- Anatomy of a Training Run in Azure ML
  Module 3 -Section 3- Logging Metrics and Monitoring Runs
  Module 3 - Section 4. Using Compute Targets: Local vs. Remote
  Module 3 - Section 5. Experimentation Best Practices
  Module 4 -Section 1 - Introduction to Data Management in Azure ML
  Module 4 -Section 2 - Understanding Datastores in Azure ML
  Module 4 - Section 3. Registering and Using Datastores
  Module 4 - Section 4 - Creating and Registering Datasets in Azure ML
  Module 4 - Section 5 - Mounting vs. Downloading Data
  Module 4 -Section 6 - Best Practices for Managing Data in Azure ML
  Module 5: Section 1. Introduction to Compute in Azure ML
  Module 5: Section 2. Compute Instances vs. Compute Clusters
  Module 5: Section 3. Attached Compute (Advanced Concepts)
  Module 5: Section 4. Environments in Azure ML: What and Why
  Module 5: Section 5 -Creating Custom Environments
  Module 5- Section 6- Submitting Jobs to Compute Clusters
  Module 6- Section 1- What Is an ML Pipeline in Azure ML?
  Module 6 - Section 2. Components of a Pipeline Step
  Module 6 - Section 3. Creating a Simple Two-Step Pipeline
  Module 6 - Section 4 - Passing Data Between Pipeline Steps
  Module 6 - Section 5. Publishing Pipelines for Reuse
  Module 6 - Section 6. Pipeline Scheduling and Automation Options
  Module 6 - Section 7. Best Practices for Pipelines
  Module 7 - Overview of Deployment Targets in Azure ML
  Module 7 - Creating a Real-time Inference Endpoint
  Module 7 - Consuming Real-time Endpoints via REST API
  Module 7 - Creating a Batch Inference Pipeline
  Module 7- Versioning and Updating Deployments
  Module 8 - Hyperparameters vs. Model Parameters
  Module 8 - Azure ML Hyperparameter Tuning (HyperDrive / SweepJob)
  Module 8 - Performing Hyperparameter Tuning in Azure ML
  Module 8 - Introduction to Automated Machine Learning (AutoML) Type
  Module 8 - Running an AutoML Experiment in Azure ML
  Module 8 - Understanding AutoML Output & Explainability
  Module 8 - Responsible AI Features in AutoML
  Module 9 - Why Model Interpretability Matters ?
  Module 9 - Model Explanation Techniques in Azure ML
  Module 9 -Reviewing AutoML Explanations
  Module 9 - Using the Explanation Client and SDK
  Module 9 - Responsible AI & Fairness: What Azure ML Covers
  Module 10 - Why Monitor ML Models in Production?
  Module 10 - Overview of Monitoring Tools in Azure ML
  Module 10 - Monitoring Model Services with Application Insights
  Module 10 - Logging Custom Metrics in score.py
  Module 10 - Monitoring Data Drift in Azure ML
  Audio Version of Training

  How Azure ML tracks experiment metadata, source code, outputs, and metrics

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