/amazon-sagemaker-architecting-for-ml

Materials for a 3-day instructor led course on applying machine learning

Primary LanguageJupyter NotebookMIT LicenseMIT

Architecting For Machine Learning on Amazon SageMaker

Welcome to the art and science of machine learning! During this 3-day course you will learn about the theory and application of machine learning in industry. This course is designed for architects and developers who did not previously have a background in AI/ML. You will spend 3 days performing data science tasks: training models, evaluating them, analyzing data, etc. After this 3 day period you will be better suited to design architecture guidelines for ML production applications. This course is also open to full-time data scientists, who will learn how to perform those tasks on AWS.

We will cover:

  • Statistical machine learning
  • Deep Learning
  • Feature engineering
  • Deploying a model into production
  • Model evaluation and comparison

As a prerequisite to attending this course, we recommend reviewing Python programming using the statistical package Pandas. We also recommend having a Cloud Practiioner AWS Certification, but it is not required. Lastly, we recommend the book listed below. It is an excellent read, and clearly demonstrates all important concepts.

Agenda

Day One:

  • Learn about ML on AWS
  • Go through a sample lab
  • Break into teams and focus on a new machine learning project
    Deliverable: Produce a sample writeup explaining your modeling strategy

Day Two:

  • Learn about feature engineering on AWS
  • Start new notebooks, sample your code, and develop preliminary data sets
  • Read the evaluation questions, and begin to think about how your modeling strategy compares to the evaluation questions.
  • Finish most of your feature engineering.
    Deliverable: Product the first version of your trained model

Day Three:

  • Learn about putting your model into production.
  • Evalute your project against other approaches
  • Design a reference architecture demonstrating your final solution Stretch goal: Produce multiple versions of your model and compare them

What you'll need

  • AWS Account log in credentials
  • Github account to share code with your project partners
  • Kaggle account to download data sets

We're assuming that you will complete this course using an AWS account we will provide you with throughout the course.