Previous Lesson Complete and Continue  

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

  Core architecture workspace, compute, storage, environments, models

Lesson content locked
If you're already enrolled, you'll need to login.
Enroll in Course to Unlock