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AWS Authorized Training Course - The Machine Learning Pipeline on AWS

Course Summary

The Machine Learning Pipeline on AWS training course is designed to demonstrate how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.

The course begins by exploring each phase of the pipeline from instructor presentations and demonstration. Next, it expresses how to apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, the course concludes by illustrating how to successfully build, train, evaluate, tune, and deploy an ML model using Amazon SageMaker that solves selected business problems.

Prerequisites:

    • 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

AWS Authorized Training is only available in Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, Mexico, United States, and Peru.
THIS COURSE IS NOT ELIGIBLE FOR TRAINING BUNDLES.

Purpose
 To demonstrate how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment
Audience
 Developers , Solutions Architects, Data Engineers, Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
Role
Business Analyst - Data Engineer - Data Scientist - DevOps Engineer - Project Manager - Q/A - Software Developer - System Administrator - Technical Manager - Web Developer
Skill Level
Intermediate
Style
Workshops
Duration
4 Days
Related Technologies
Cloud Computing Training | AWS

 

Productivity Objectives
  • 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

What You'll Learn:

In the AWS Authorized Training Course - The Machine Learning Pipeline on AWS training course, you'll learn:
  • Introduction
    • Pre-assessment
  • 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 Amazon SageMaker
    • Introduction to Amazon SageMaker
    • Amazon SageMaker and Jupyter notebooks demonstration
  • Problem Formulation
    • Overview of problem formulation and deciding if ML is the right solution
    • Convert a business problem into an ML problem
    • Amazon SageMaker Ground Truth demonstration
    • Amazon SageMaker Ground Truth
    • Practice problem formulation
    • Formulate problems for projects
  • Preprocessing
    • Overview of data collection and integration
    • Techniques for data preprocessing and visualization
    • Practice preprocesses
    • Preprocess project data
    • Class discussion about projects
  • Model Training
    • Choose the right algorithm
    • Format and split your data for training
    • Loss functions and gradient descent for improving the model
    • Create a training job in Amazon SageMaker demonstration
  • Model Evaluation
    • Evaluate classification models
    • Evaluate regression models
    • Practice model training and evaluation
    • Train and evaluate project models
    • Initial project presentations
  • Feature Engineering and Model Tuning
    • Feature extraction, selection, creation, and transformation
    • Hyperparameter tuning
    • SageMaker hyperparameter optimization demonstration
    • Practice feature engineering and model tuning
    • Apply feature engineering and model tuning to projects
    • Final project presentations
  • Deployment
    • How to deploy, inference, and monitor your model on Amazon SageMaker
    • Deploy ML at the edge
    • Create an Amazon SageMaker endpoint demonstration
    • Post-assessment
    • Course wrap-up
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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