Machine Learning on AWS Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

The Machine Learning on AWS training course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. You will learn about each phase of the pipeline 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, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

By attending Machine Learning on AWS workshop, delegates will learn 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 in 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

Basic knowledge of Python programming language
Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
Basic understanding of working in a Jupyter notebook environment

Developers
Solutions Architects
Data Engineers

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

Introduction to Amazon SageMaker

Introduction to Amazon SageMaker
Demo: Amazon SageMaker and Jupyter notebooks

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
Problem Formulation Exercise and Review
Project work for Problem Formulation

Preprocessing

Overview of data collection and integration, and techniques for data preprocessing and visualization

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

Model Training - II

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

Feature Engineering and Model Tuning

Feature extraction, selection, creation, and transformation
Hyperparameter tuning
Demo: SageMaker hyperparameter optimization

Module Deployment

How to deploy, inference, and monitor your model on Amazon SageMaker
Deploying ML at the edge

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Machine Learning Pipeline on AWS

COURSE AGENDA