IntroductionCourse OutlineEnroll

Duration

4 days.

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

Skills Gained

  • 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

Who Can Benefit?

  • Developers
  • Solutions Architects
  • Data Engineers
  • Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Outline for The Machine Learning Pipeline on AWS Training

Day 1

  • Module 0: Introduction
  • Module 1: Introduction to Machine Learning and the ML Pipeline
  • Module 2: Introduction to Amazon SageMaker
  • Module 3: Problem Formulation

Day 2

  • Module 4: Preprocessing

Day 3

  • Module 5: Model Training
  • Module 6: Model Evaluation

Day 4

  • Module 7: Feature Engineering and Model Tuning
  • Module 8: Deployment
11/14/2023 - 11/17/2023
09:00 AM - 05:00 PM
Eastern Standard Time
Online Virtual Class
USD $2,700.00
Enroll
11/28/2023 - 12/01/2023
12:00 PM - 08:00 PM
Eastern Standard Time
Online Virtual Class
USD $2,700.00
Enroll
12/18/2023 - 12/21/2023
09:00 AM - 05:00 PM
Eastern Standard Time
Online Virtual Class
USD $2,700.00
Enroll