The Machine Learning Pipeline on AWS

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.

Day one

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

Day Two

Module 3: Problem Formulation (continued)

• Practice problem formulation
• Formulate problems for projects

Checkpoint 1 and Answer ReviewModule 4: Preprocessing

• Overview of data collection and integration, and techniques for data preprocessing and visualization
• Practice preprocessing
• Preprocess project data and discuss project progress

Day Three

Checkpoint 2 and Answer ReviewModule 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

Day Four

Checkpoint 3 and Answer ReviewModule 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

WICHTIG: Bitte bringen Sie zu unseren Trainings Ihr Notebook (Windows, Linux oder Mac) mit. Wenn dies nicht möglich ist, nehmen Sie bitte mit uns vorher Kontakt auf.

Kursunterlagen sind in englischer Sprache, Kurssprache des Trainers ist deutsch.

We recommend that attendees of this course have:

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

Dieser Kurs setzt sich zusammen aus:

• Schulung mit Kursleiter
• Praktische Übungen
• Gruppenübungen

Dieser Kurs ist konzipiert für:

• 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