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MLOps Platforms: Amazon SageMaker and Azure ML
  • Level Expert
  • Duration 13 hours
  • Course by Duke University
  • Offered by

About

In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning. Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology. Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation. Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient. Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models. Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model. Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML.

Modules

About the Course

    • 1 Videos
    • 3 Readings
    • 2 Discussion
Show info about module content

2 Discussions

  • Meet and Greet (optional)
  • Let Us Know if Something’s Not Working

1 Videos

  • Meet your Course Instructor: Noah Gift

3 Readings

  • Meet your Supporting Instructor: Alfredo Deza
  • Course Structure and Discussion Etiquette
  • Getting Started and Course Gotchas

Getting Started with AWS Machine Learning Technology

    • 5 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Getting Started with AWS Machine Learning Technology

5 Videos

  • 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

5 Readings

  • Key Terms
  • Welcome to AWS Academy Machine Learning Foundations
  • Studio Lab Examples
  • AWS Academy Onboard (Optional)
  • Lesson Reflection

Creating Data Repositories for Machine Learning

    • 3 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Create Data Repository for Machine Learning

3 Videos

  • Introduction to Data Storage
  • Determining the Correct Storage Medium
  • Working with Amazon S3

4 Readings

  • Key Terms
  • Developing AWS Storage Solutions
  • Data Lakes with Amazon S3
  • Lesson Reflection

Identifying and Implementing Data Ingestion and Transformation Solutions

    • 7 Videos
    • 3 Readings
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Data Engineering with AWS Machine Learning Technology
  • Quiz-Identifying and Implementing Data Ingestion and Transformation Solutions

1 Labs

  • Build and Deploy a Marco Polo AWS Step Function

7 Videos

  • 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

3 Readings

  • Key Terms
  • Interactive Marco Polo Pipeline Programming Challenge
  • Lesson Reflection

Sanitizing and Preparing Data for Modeling

    • 3 Videos
    • 4 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Sanitizing and Preparing Data for Modeling

1 Labs

  • Jupyter Sandbox

3 Videos

  • Cleaning Up Data
  • Scaling Data
  • Labeling Data

4 Readings

  • Key Terms
  • AWS Academy Introduction to Machine Learning
  • AWS Resources for Exploratory Data Analysis
  • Lesson Reflection

Performing Feature Engineering

    • 2 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Feature Engineering

1 Labs

  • Feature Engineering-Creating a Winning Season

2 Videos

  • Identifying and Extracting Features
  • Feature Engineering Concepts

3 Readings

  • Key Terms
  • Feature engineering with scikit-learn on Databricks
  • Lesson Reflection

Analyzing and Visualizing Data for Machine Learning

    • 2 Videos
    • 2 Readings
    • 2 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Exploratory Data Analysis

2 Labs

  • Covid19 Exploratory Data Analysis
  • Clustering and Plotting Clusters in Housing Prices

2 Videos

  • Graphing Data
  • Clustering Data

2 Readings

  • Key Terms
  • Lesson Reflection

Selecting the Appropriate Model(s) for a Given Machine Learning Problem

    • 3 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Selecting the Appropriate Model(s) for a Given Machine Learning Problem

3 Videos

  • When to Use Machine Learning?
  • Supervised vs. Unsupervised Machine Learning
  • Selecting a Machine Learning Solution

3 Readings

  • Key Terms
  • Introduction to Implementing a Machine Learning Pipeline with Amazon SageMaker
  • Lesson Reflection

Training Machine Learning Models

    • 6 Videos
    • 4 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Training Machine Learning Models

1 Labs

  • Gradient Descent Sandbox

6 Videos

  • 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

4 Readings

  • Key Terms
  • Introducing Forecasting on Sagemaker
  • Interactive Gradient Descent
  • Lesson Reflection

Evaluating Machine Learning Problems

    • 3 Videos
    • 4 Readings
    • 2 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Machine Learning Modeling
  • Quiz-Evaluating Machine Learning Problems

2 Labs

  • Building a Linear Regression Model
  • Underfitting vs Overfitting

3 Videos

  • Overfitting vs. Underfitting
  • Selecting Metrics
  • Comparing Models using Experiment Tracking

4 Readings

  • Key Terms
  • Introducing Computer Vision
  • More Practice: Train an Image Classification Model with PyTorch
  • Lesson Reflection

Building Machine Learning Solutions for Performance, Availability, Scalability, Resilience and Fault

    • 4 Videos
    • 4 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Building Machine Learning Solutions

1 Labs

  • Python Logging Lab

4 Videos

  • Monitoring and Logging
  • Multiple Regions
  • Reproducible Workflows
  • AWS-Flavored DevOps

4 Readings

  • Key Terms
  • Introducing Natural Language Processing
  • Interactive Python Logging
  • Lesson Reflection

Recommending and Implementing Appropriate Machine Learning Services

    • 4 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Recommending and Implementing Appropriate Machine Learning Services

4 Videos

  • Reviewing Compute Choices
  • Provisioning EC2
  • Provisioning EBS
  • AWS AI ML Services

3 Readings

  • Key Terms
  • More Practice: Deploy a Hugging Face Pre-trained Model to Amazon SageMaker
  • Lesson Reflection

Deploying and Operationalizing Secure Machine Learning Solutions

    • 6 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Getting Started with MLOps

6 Videos

  • 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

5 Readings

  • Key Terms
  • More Practice: Deploy Models for Inference
  • AWS Certified Machine Learning – Specialty
  • External Lab: MLOps Template GitHub
  • Lesson Reflection

Azure AI Fundamentals and other Azure Certifications

    • 5 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Azure AI Fundamentals and other Azure Certifications

5 Videos

  • 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

2 Readings

  • Key Terms
  • Lesson Reflection

Introductory Azure ML and MLOps Concepts

    • 5 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Quiz-Introductory Azure ML and MLOps Concepts

5 Videos

  • Introductory Azure ML and MLOps Concepts
  • Prerequisite Technology
  • Real Time and Batch Deployment
  • Azure Open Datasets
  • Exploring Open Datasets SDK

2 Readings

  • Key Terms
  • Lesson Reflection

More Advanced Azure ML and MLOps Concepts

    • 5 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Tutorial: Azure Machine Learning in a Day

5 Videos

  • 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

3 Readings

  • Key Terms
  • Lesson Reflection
  • Next Steps

Auto Summary

This expert-level course, "MLOps Platforms: Amazon SageMaker and Azure ML," is designed for data scientists, software engineers, and data analysts. Taught by Coursera, it focuses on building, training, and deploying machine learning solutions using AWS and Azure. Over five weeks, learners will explore data engineering, data science skills, machine learning models, and MLOps with AWS, before transitioning to Azure ML. The course includes hands-on exercises and is ideal for those preparing for AWS or Azure machine learning certifications. Subscription options include Starter and Professional.

Noah Gift
Instructors

Noah Gift

Alfredo Deza
Instructors

Alfredo Deza