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Master Cloud MLOps: AWS SageMaker & Azure ML
Course Highlights:
Ideal for data scientists, ML engineers, analysts & cloud professionals. Master comprehensive MLOps skills on AWS & Azure through real-world training.
Module 1: Data Engineering with AWS Technology (7 hours)
\- Video: Meet your Course Instructor: Noah Gift (3 minutes)
\- Video: Using Sagemaker Studio Lab (7 minutes)
\- Video: Getting Started with AWS CloudShell (12 minutes)
\- Video: Advantages of Using Cloud Developer Workspaces (4 minutes)
\- Video: Prototyping AI APIs in CloudShell (12 minutes)
\- Video: Cloud9 with AWS Codewhisperer AI Pair Programming Tool (9 minutes)
\- Video: Introduction to Data Storage (1 minute)
\- Video: Determining the Correct Storage Medium (3 minutes)
\- Video: Working with Amazon S3 (6 minutes)
\- Video: Batch vs. Streaming Job Styles (2 minutes)
\- Video: Introduction to Data Ingestion and Processing Pipelines (2 minutes)
\- Video: Working with AWS Batch (3 minutes)
\- Video: Working with AWS Step Functions (8 minutes)
\- Video: Transforming Data in Transit (2 minutes)
\- Video: Handling Map Reduce for Machine Learning (1 minute)
\- Video: Working with EMR Serverless (1 minute)
\- Reading: Meet your Supporting Instructor: Alfredo Deza (10 minutes)
\- Reading: Course Structure and Discussion Etiquette (10 minutes)
\- Reading: Getting Started and Course Gotchas (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Welcome to AWS Academy Machine Learning Foundations (10 minutes)
\- Reading: Studio Lab Examples (10 minutes)
\- Reading: AWS Academy Onboard (Optional) (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Developing AWS Storage Solutions (10 minutes)
\- Reading: Data Lakes with Amazon S3 (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Interactive Marco Polo Pipeline Programming Challenge (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Quiz: Data Engineering with AWS Machine Learning Technology (30 minutes)
\- Quiz: Quiz-Getting Started with AWS Machine Learning Technology (30 minutes)
\- Quiz: Quiz-Create Data Repository for Machine Learning (30 minutes)
\- Quiz: Quiz-Identifying and Implementing Data Ingestion and Transformation Solutions (30 minutes)
\- Discussion Prompt: Meet and Greet (optional) (10 minutes)
\- Discussion Prompt: Let Us Know if Something's Not Working (10 minutes)
\- Ungraded Lab: Build and Deploy a Marco Polo AWS Step Function (60 minutes)
Module 2: Exploratory Data Analysis with AWS Technology (7 hours)
\- Video: Cleaning Up Data (1 minute)
\- Video: Scaling Data (1 minute)
\- Video: Labeling Data (1 minute)
\- Video: Identifying and Extracting Features (1 minute)
\- Video: Feature Engineering Concepts (1 minute)
\- Video: Graphing Data (3 minutes)
\- Video: Clustering Data (2 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: AWS Academy Introduction to Machine Learning (10 minutes)
\- Reading: AWS Resources for Exploratory Data Analysis (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Feature engineering with scikit-learn on Databricks (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Quiz: Exploratory Data Analysis (30 minutes)
\- Quiz: Quiz-Sanitizing and Preparing Data for Modeling (30 minutes)
\- Quiz: Quiz-Feature Engineering (30 minutes)
\- Ungraded Lab: Jupyter Sandbox (60 minutes)
\- Ungraded Lab: Feature Engineering-Creating a Winning Season (60 minutes)
\- Ungraded Lab: Covid19 Exploratory Data Analysis (60 minutes)
\- Ungraded Lab: Clustering and Plotting Clusters in Housing Prices (60 minutes)
Module 3: Modeling with AWS Technology (7 hours)
\- Video: When to Use Machine Learning? (1 minute)
\- Video: Supervised vs. Unsupervised Machine Learning (2 minutes)
\- Video: Selecting a Machine Learning Solution (1 minute)
\- Video: Selecting a Machine Learning Model (1 minute)
\- Video: Modeling Demo with Sagemaker Canvas (5 minutes)
\- Video: Using Train, Test and Split (1 minute)
\- Video: Solving Optimization Problems (2 minutes)
\- Video: Selecting GPU vs. CPU (1 minute)
\- Video: Neural Network Architecture (2 minutes)
\- Video: Overfitting vs. Underfitting (1 minute)
\- Video: Selecting Metrics (5 minutes)
\- Video: Comparing Models using Experiment Tracking (1 minute)
\- Reading: Key Terms (10 minutes)
\- Reading: Introduction to Implementing a Machine Learning Pipeline with Amazon SageMaker (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Introducing Forecasting on Sagemaker (10 minutes)
\- Reading: Interactive Gradient Descent (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Introducing Computer Vision (10 minutes)
\- Reading: More Practice: Train an Image Classification Model with PyTorch (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Quiz: Quiz-Selecting the Appropriate Model(s) for a Given Machine Learning Problem (30 minutes)
\- Quiz: Quiz-Training Machine Learning Models (30 minutes)
\- Quiz: Machine Learning Modeling (30 minutes)
\- Quiz: Quiz-Evaluating Machine Learning Problems (30 minutes)
\- Ungraded Lab: Gradient Descent Sandbox (60 minutes)
\- Ungraded Lab: Building a Linear Regression Model (60 minutes)
\- Ungraded Lab: Underfitting vs Overfitting (60 minutes)
Module 4: MLOps with AWS Technology (5 hours)
\- Video: Monitoring and Logging (1 minute)
\- Video: Multiple Regions (1 minute)
\- Video: Reproducible Workflows (1 minute)
\- Video: AWS-Flavored DevOps (1 minute)
\- Video: Reviewing Compute Choices (1 minute)
\- Video: Provisioning EC2 (1 minute)
\- Video: Provisioning EBS (1 minute)
\- Video: AWS AI ML Services (4 minutes)
\- Video: Principle of Least Privilege AWS Lambda (1 minute)
\- Video: Integrated Security (1 minute)
\- Video: Overview of Sagemaker Studio Workflow (2 minutes)
\- Video: Model Predictions with Sagemaker Canvas (1 minute)
\- Video: Data Drift and Model Monitoring (1 minute)
\- Video: Running PyTorch with AWS App Runner (7 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Introducing Natural Language Processing (10 minutes)
\- Reading: Interactive Python Logging (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: More Practice: Deploy a Hugging Face Pre-trained Model to Amazon SageMaker (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: More Practice: Deploy Models for Inference (10 minutes)
\- Reading: AWS Certified Machine Learning – Specialty (10 minutes)
\- Reading: External Lab: MLOps Template GitHub (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Quiz: Getting Started with MLOps (30 minutes)
\- Quiz: Quiz-Building Machine Learning Solutions (30 minutes)
\- Quiz: Quiz-Recommending and Implementing Appropriate Machine Learning Services (30 minutes)
\- Ungraded Lab: Python Logging Lab (60 minutes)
Module 5: Machine Learning Certifications (4 hours)
\- Video: Introduction to Azure Certifications (2 minutes)
\- Video: Learning Resources for Azure Certifications (8 minutes)
\- Video: Microsoft Learning Paths and Study Notes (6 minutes)
\- Video: Creating an Azure ML Workspace (6 minutes)
\- Video: Creating an Azure Auto ML Job (14 minutes)
\- Video: Introductory Azure ML and MLOps Concepts (0 minutes)
\- Video: Prerequisite Technology (1 minute)
\- Video: Real Time and Batch Deployment (2 minutes)
\- Video: Azure Open Datasets (3 minutes)
\- Video: Exploring Open Datasets SDK (1 minute)
\- Video: More Advanced Azure ML and MLOps Concepts (1 minute)
\- Video: Exploring Azure ML Command Line (3 minutes)
\- Video: Triggering Azure ML with GitHub (2 minutes)
\- Video: Using Hyperparameters (3 minutes)
\- Video: Train a Model using the Python SDK (6 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Key Terms (10 minutes)
\- Reading: Lesson Reflection (10 minutes)
\- Reading: Next Steps (10 minutes)
\- Quiz: Tutorial: Azure Machine Learning in a Day (60 minutes)
\- Quiz: Quiz-Azure AI Fundamentals and other Azure Certifications (30 minutes)
\- Quiz: Quiz-Introductory Azure ML and MLOps Concepts (30 minutes)
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.