AWS - Machine Learning Pipeline on AWS

Groupes cibles:

Developers Solutions Architects Data Engineers Mitarbeiter, die wenig bis keine Erfahrung mit ML hat und die ML-Pipeline mit Amazon SageMaker kennenlernen möchten.

Connaissances spécialisées:

- Grundkenntnisse der Programmiersprache Python - Grundlegendes Verständnis der AWS-Cloud-Infrastruktur (Amazon S3 und Amazon CloudWatch) - Grundlegende Erfahrung mit der Arbeit in einer Jupyter-Notebook-Umgebung

Connaissances techniques:

Keine besonderen Anforderungen.

Classification de l’Agence pour l’emploi:

• C 1430-10-10 System-, Netzwerkadministration - allgemein

Contenus

- 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
- 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
- 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
- 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