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MLOps Platforms

Amazon SageMaker and Azure ML

Alfredo Deza and Noah Gift

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:

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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.

Enroll now

What's inside

Learning objectives

  • Apply exploratory data analysis (eda) techniques to data science problems and datasets.
  • Build machine learning modeling solutions using both aws and azure technology.
  • Train and deploy machine learning solutions to a production environment using cloud technology.

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)
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\- 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)
\- Quiz: Data Engineering with AWS Machine Learning Technology (30 minutes)
\- Reading: Developing AWS Storage Solutions (10 minutes)
\- Reading: Data Lakes with Amazon S3 (10 minutes)
\- Reading: Interactive Marco Polo Pipeline Programming Challenge (10 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: AWS Academy Introduction to Machine Learning (10 minutes)
\- Reading: AWS Resources for Exploratory Data Analysis (10 minutes)
\- Reading: Feature engineering with scikit-learn on Databricks (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: Introduction to Implementing a Machine Learning Pipeline with Amazon SageMaker (10 minutes)
\- Reading: Introducing Forecasting on Sagemaker (10 minutes)
\- Reading: Interactive Gradient Descent (10 minutes)
\- Reading: Introducing Computer Vision (10 minutes)
\- Reading: More Practice: Train an Image Classification Model with PyTorch (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: Introducing Natural Language Processing (10 minutes)
\- Reading: Interactive Python Logging (10 minutes)
\- Reading: More Practice: Deploy a Hugging Face Pre-trained Model to Amazon SageMaker (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)
\- 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: 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)

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops machine learning pipelines, which are standard in industry
Builds a strong foundation in Cloud MLOps, which is highly relevant to cloud computing
Taught by Alfredo Deza and Noah Gift, who are recognized for their work in Cloud MLOps
Examines MLOps on AWS and Azure, which are highly relevant to cloud computing
Teaches skills and tools that can strengthen an existing foundation for intermediate learners
Teaches machine learning modeling, which is a core skill for data scientists and ML engineers

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in MLOps Platforms: Amazon SageMaker and Azure ML with these activities:
Brush up on Azure ML workflow
This activity will help you refresh your knowledge of concepts relevant to Azure ML, giving you a better foundation before the course begins.
Show steps
  • Look at your notebook from previous coursework in Azure ML.
  • Go through Azure's beginner documentation for ML.
  • Look at content by Microsoft Learning for Azure ML.
Review Machine Learning modeling concepts
This activity will help you refresh your knowledge of concepts relevant to Machine Learning models, giving you a better foundation before the course begins.
Browse courses on Machine Learning
Show steps
  • Look over class notes and assignments from your previous Machine Learning course.
  • Go through a quick online tutorial about Machine Learning models.
Compile tools and documentation
This activity will assist you in organizing course materials, helping you be more efficient in the long run.
Browse courses on Tools
Show steps
  • Set up a digital folder where you will keep resources.
  • Bookmark useful documentation.
  • Create a list of tools you learn about in class.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice gradient descent
This activity will help you practice key skills and concepts necessary for the topic of gradient descent.
Browse courses on Gradient Descent
Show steps
  • Look through the provided interactive gradient descent sandbox.
Learn from AWS Academy
This activity will help you expand your learning and build a stronger understanding of Machine Learning.
Browse courses on AWS
Show steps
  • Enroll in AWS Academy Machine Learning Foundations.
  • Complete the onboarding and course structure materials.
  • Go through the first two modules in your own time.
Read Pattern Recognition and Machine Learning
This book will provide a comprehensive overview of machine learning and help you reinforce the fundamental principles covered in the course.
View Pattern Recognition and Machine Learning... on Amazon
Show steps
  • Read the first three chapters.
  • Do the exercises from the first three chapters.
  • Go to the course forum and discuss the book with other students.
Review CI/CD tools
This activity will help you understand a topic of interest for expanding on what is taught in the course.
Browse courses on Continuous Integration
Show steps
  • Look at beginner resources for DevOps.
  • Go through tutorials for AWS CodeWhisperer AI Pair Programming Tool.
Deploy a machine learning model
This project will give you hands-on experience deploying a model, reinforcing the skills and knowledge you have learned through the course.
Browse courses on Machine Learning
Show steps
  • Choose a dataset.
  • Build a model.
  • Train the model.
  • Deploy the model.

Career center

Learners who complete MLOps Platforms: Amazon SageMaker and Azure ML will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Alfredo Deza
Noah Gift
Session
March 19, 2024 - March 20, 2025
Language
English
Transcript
English
Good to know
Develops machine learning pipelines, which are standard in industry
Builds a strong foundation in Cloud MLOps, which is highly relevant to cloud computing
Taught by Alfredo Deza and Noah Gift, who are recognized for their work in Cloud MLOps
Examines MLOps on AWS and Azure, which are highly relevant to cloud computing
Teaches skills and tools that can strengthen an existing foundation for intermediate learners
Teaches machine learning modeling, which is a core skill for data scientists and ML engineers
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