The Machine Learning Pipeline on AWS (E)

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Dauer

Trainingssprache

Englisch

Trainingsart

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Was werden Sie in diesem Training erlernen?

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays. 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

Agenda

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

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 2: Introduction to Amazon SageMaker

Introduction to Amazon SageMaker

Demo: Amazon SageMaker and Jupyter notebooks

Hands-on: Amazon SageMaker and Jupyter notebooks

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

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

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

Module 3: Problem Formulation (continued)

Practice problem formulation

Formulate problems for projects

Module 3: Problem Formulation (continued)

Practice problem formulation

Formulate problems for projects

Module 3: Problem Formulation (continued)

Practice problem formulation

Formulate problems for projects

Module 4: Preprocessing

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

Practice preprocessing

Preprocess project data and discuss project progress

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 4: Preprocessing

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

Practice preprocessing

Preprocess project data and discuss project progress

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 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 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, then present findings

Module 6: Model Evaluation

How to evaluate classification models

How to evaluate regression models

Practice model training and evaluation

Train and evaluate project models, then present findings

Module 6: Model Evaluation

How to evaluate classification models

How to evaluate regression models

Practice model training and evaluation

Train and evaluate project models, then present findings

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 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 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

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

Module 4: Preprocessing

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

Practice preprocessing

Preprocess project data and discuss project progress

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

REVIEWING ARCHITECTING CONCEPTS

SINGLE TO MULTIPLE ACCOUNTS

Group Exercise: Review Architecting on AWS core best practices
AWS Organizations for multi-account access and permissions
AWS SSO to simplify access and authentication across AWS accounts and third-party services
AWS Control Tower
Permissions, access, and authentication

HYBRID CONNECTIVITY

SPECIALIZED INFRASTRUCTURE

Zielgruppe

Developers, Solutions architects, Data engineers, Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning

Voraussetzung für den Kurs

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

Partner

AWS