Duke University - MLOps Platforms: Amazon SageMaker and Azure ML 

Meet your Course Instructor: Noah Gift

Using Sagemaker Studio Lab

Getting Started with AWS CloudShell

Advantages of Using Cloud Developer Workspaces

Prototyping AI APIs in CloudShell

Cloud9 with AWS Codewhisperer AI Pair Programming Tool

Introduction to Data Storage

Determining the Correct Storage Medium

Working with Amazon S3

Batch vs. Streaming Job Styles

Introduction to Data Ingestion and Processing Pipelines

Working with AWS Batch

Working with AWS Step Functions

Transforming Data in Transit

Handling Map Reduce for Machine Learning

Working with EMR Serverless

Meet your Supporting Instructor: Alfredo Deza

Course Structure and Discussion Etiquette

Welcome to AWS Academy Machine Learning Foundations

Studio Lab Examples

Developing AWS Storage Solutions

MLOps Template GitHub

Data Engineering with AWS Machine Learning Technology

Cleaning Up Data

Scaling Data

Labeling Data

Identifying and Extracting Features

Feature Engineering Concepts

Graphing Data

Clustering Data

AWS Academy Introduction to Machine Learning

AWS Resources for Exploratory Data Analysis

Exploratory Data Analysis

When to Use Machine Learning?

Supervised vs. Unsupervised Machine Learning

Selecting a Machine Learning Solution

Selecting a Machine Learning Model

Modeling Demo with Sagemaker Canvas

Using Train, Test and Split

Solving Optimization Problems

Selecting GPU vs. CPU

Neural Network Architecture

Overfitting vs. Underfitting

Selecting Metrics

Comparing Models using Experiment Tracking

Introduction to Implementing a Machine Learning Pipeline with Amazon SageMaker

Introducing Forecasting on Sagemaker

Introducing Computer Vision

More Practice: Train an Image Classification Model with PyTorch

Machine Learning Modeling

Monitoring and Logging

Multiple Regions

Reproducible Workflows

AWS-Flavored DevOps

Reviewing Compute Choices

Provisioning EC2

Provisioning EBS

AWS AI ML Services

Principle of Least Privilege AWS Lambda

Integrated Security

Overview of Sagemaker Studio Workflow

Model Predictions with Sagemaker Canvas

Data Drift and Model Monitoring

Running PyTorch with AWS App Runner

Introducing Natural Language Processing

More Practice: Deploy a Hugging Face Pre-trained Model to Amazon SageMaker

More Practice: Deploy Models for Inference

AWS Certified Machine Learning – Specialty

Getting Started with MLOps

Introduction to Azure Certifications

Learning Resources for Azure Certifications

Microsoft Learning Paths and Study Notes

Creating an Azure ML Workspace

Creating an Azure Auto ML Job

Introductory Azure ML and MLOps Concepts

Prerequisite Technology

Real Time and Batch Deployment

Azure Open Datasets

Exploring Open Datasets SDK

More Advanced Azure ML and MLOps Concepts

Exploring Azure ML Command Line

Triggering Azure ML with GitHub

Using Hyperparameters

Train a Model using the Python SDK

Tutorial: Azure Machine Learning in a Day