The Machine Learning pipeline on AWS Training Course Overview

This Machine Learning Pipeline on AWS course from CloudThat teaches candidates how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Candidates taking up this Machine Learning Pipeline on AWS training also learn to solve one of three business problems, including recommendation engines, fraud detection, or flight delays.

By the end of the Machine Learning Pipeline AWS training and course, candidates are successfully trained to build, evaluate, tune, and deploy an ML model using Amazon SageMaker. Candidates with basic knowledge of Statistics will find this course helpful. This course comprises presentations, group exercises, demonstrations, and hands-on labs.

After completing AWS Certified Machine Learning Course, students will be able 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

Upcoming Batches

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2024-09-16

2024-09-20

2024-09-23

2024-09-27

2024-09-30

2024-10-04

2024-10-07

2024-10-11

2024-10-14

2024-10-18

AWS Certified Machine Learning Training Course Key Features

  • Our training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • AWS certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Being an authorized AWS Training Partner gives us an edge over competition.

Who should attend this AWS Certified Machine Learning Course

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

AWS Certified Machine Learning Course Prerequisites

We recommend that attendees of this Machine Learning Pipeline on AWS course must 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 on AWS Course Outline Download Course Outline

  • Pre-assessment

  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline
  • Introduction to course projects and approach

  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Hands-on: Amazon SageMaker and Jupyter notebooks

  • Overview of problem formulation and deciding if ML is the right solution
  • Converting a business problem into an ML problem
  • Demo: Amazon SageMaker Ground Truth
  • Hands-on: Amazon SageMaker Ground Truth
  • Practice problem formulation
  • Formulate problems for projects

Checkpoint 1 and Answer Review

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about projects

Checkpoint 2 and Answer Review

  • 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

  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Initial project presentations

Checkpoint 3 and Answer Review

  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization
  • Practice feature engineering and model tuning
  • Apply feature engineering and model tuning to projects
  • Final project presentations

  • How to deploy, inference, and monitor your model on Amazon SageMake
  • Deploying ML at the edge
  • Demo: Creating an Amazon SageMaker endpoint
  • Post-assessment
  • Course wrap-up

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Course ID: 13504

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