Detaillierte Informationen zum Seminar
Inhalte:
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
Dauer/zeitlicher Ablauf:
4 Tage
Ziele/Bildungsabschluss:
Siehe Beschreibung und Inhalt.
Teilnahmevoraussetzungen:
- 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
Material:
Im Preis enthalten sind: Technische Beratung, Kursmaterial und Schulungszertifikat.
Förderung:
Bildungsscheck, andere auf Anfrage
Zielgruppe:
Siehe Beschreibung und Inhalt.
Seminarkennung:
AWJ241111FL-ONL
