The Benefits of Scalability, Automation, Managed infrastructure, MLOps

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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

Overview-Deploying a Model with Designer to ACI or AKS (7:10)

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)

2 When to use - hybrid pipelines, data proximity, existing infrastructure (6:37)

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

How to use Explanation Client (5:01)

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

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