The Machine Learning Pipeline on AWS

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentation

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Learning Objectives

In this course, you will learn to:

  • Select and justify the appropriate ML approach for a given business problem
  • Use the ML pipeline to solve a specific business problem
  • Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
  • Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
  • Apply machine learning to a real-life business problem after the course is complete


    Course Details

    Course Outline

    1 - Day 1
  • Module 0: Introduction
  • Pre-assessment
  • Module 1: Introduction to Machine Learning and the ML Pipeline
  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline
  • Introduction to course projects
  • 2 - Day 2
  • Module 4: Preprocessing
  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about projects
  • 3 - Day 3
  • Module 5: Model Training
  • Choosing the right algorithm
  • Formatting and splitting your data for training
  • Loss functions and gradient descent for improving your model
  • Demo: Create a training job in Amazon SageMaker
  • Module 6: Model Evaluation
  • How to evaluate c
  • 4 - Day 4
  • Checkpoint 3 and Answer Review
  • Module 7: Feature Engineering and Model Tuning
  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization
  • Practice feature engineering and model tuning
  • Apply
  • Actual course outline may vary depending on offering center. Contact your sales representative for more information.

    Who is it For?

    Target Audience

    This course is intended for:

    Researchers and IT Professionals interested in an introduction to machine learning using Python and Amazon SageMaker

    Other Prerequisites

    We recommend that attendees of this course have:

    Basic knowledge of Python programming language

    Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

    Basic experience working in a Jupyter notebook environment

    The Machine Learning Pipeline on AWS

    Course Length : 4 Days

    There are currently no scheduled dates for this course. Please contact us for more information.

    Need Help Picking the Right Course? Give us a call! 800-201-0555