The Machine Learning Pipeline on AWS Course

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.

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

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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

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about our training

Real-World Content

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

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

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