MLOps Platforms: Amazon SageMaker and Azure ML
Offered By: Pragmatic AI Labs via edX
Course Description
Overview
Master Cloud MLOps: AWS SageMaker & Azure ML
- Build end-to-end machine learning pipelines on leading cloud platforms
- Gain practical experience through hands-on exercises and projects
- Prepare for AWS & Azure ML certifications and job roles
Course Highlights:
- Explore data engineering & ML foundations on AWS
- Create data repos, ETL pipelines & serverless solutions
- Learn data science skills - cleaning, visualization, analysis
- Train, select & tune ML models on AWS SageMaker
- Operationalize models for production with MLOps best practices
- Deploy & maintain ML solutions using CPU/GPU instances
Ideal for data scientists, ML engineers, analysts & cloud professionals. Master comprehensive MLOps skills on AWS & Azure through real-world training.
Syllabus
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)
Taught by
Alfredo Deza and Noah Gift
Related Courses
Software as a ServiceUniversity of California, Berkeley via Coursera Software Defined Networking
Georgia Institute of Technology via Coursera Pattern-Oriented Software Architectures: Programming Mobile Services for Android Handheld Systems
Vanderbilt University via Coursera Web-Technologien
openHPI Données et services numériques, dans le nuage et ailleurs
Certificat informatique et internet via France Université Numerique