Machine Learning Pipeline on AWS | Cloud Wizard Consulting

The Machine Learning Pipeline on AWS

New

Course description

This Machine Learning Pipeline on AWS 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. Learners will learn about each phase of the process pipeline from instructor presentations and demonstrations and then 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, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.

Activities

The course will emphasize a practical learning environment, including group presentations, demonstrations and hands-on labs, to enhance a basic understanding of how AWS machine learning works.

COURSE OBJECTIVES

In this course, you will:

Use the ML pipeline to address a particular business issue
Using Amazon SageMaker, you can develop, test, deploy, and fine-tune an ML model.
Describe a few recommended practices for creating scalable, economical, and secure machine learning pipelines in AWS.
After completing the course, apply machine learning (ML) to a genuine business issue.

Intended Audience

While this course doesn’t require you to be a tech expert, this course can be beneficial for the following group of people

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

Prerequisites

Attendees of this course are advised to have the following:

Some familiarity with the Python programming language
A fundamental knowledge of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
Experience in working with Jupyter notebooks on a basic level

Module Breakdown

Course outline

Day1

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 and approach

Module 2: Introduction to Amazon SageMaker

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

Module 3: Problem Formulation

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

Day2

Checkpoint 1 and Answer Review

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

Day3

Checkpoint 2 and Answer Review

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

Day4

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 feature engineering and model tuning to projects
Final project presentations

Module 8: Deployment

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

CERTIFICATION : AWS CERTIFIED MACHINE LEARNING - SPECIALTY

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified Machine Learning – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

EXAM READINESS: AWS CERTIFIED MACHINE LEARNING - SPECIALTY

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.