AWS Amazon Web Services Machine Learning Pipeline on AWS

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
Overview of data collection and integration, and techniques for data preprocessing and visualization
Practice preprocessing
Preprocess project data and discuss project progress
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, then present findings
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, interfere, and monitor your model on Amazon SageMaker
Deploying ML at the edge
Demo: Creating an Amazon SageMaker endpoint

This course is designed for:
Developers
Solution architects
Data engineers
Anyone interested in learning more about Amazon SageMaker and the ML pipeline.

We recommend that participants in this course have studied the following areas in advance:
Basic knowledge of Python
Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch).
Basic understanding of working in a Jupyter Notebook environment.

We run this course in collaboration with an official AWS Training Partner.