- Next Date:
- 12.08.2024 - 09:00 - 16:00 Uhr
- Course ends on:
- 15.08.2024
- Total Duration:
- 32 Stunden in 4 Tage
- Internship:
- Nein
- Teaching Languages:
- Deutsch
- Type of Course:
- Weiterbildung
- Type of Provision:
- E-Learning
- Execution Time:
- Tagesveranstaltung
- min. Participants:
- 1
- max. Participants:
- 8
- Price:
- €3,796.10 - Inklusive Schulungsunterlagen und Pausenversorgung
- Funding:
- Bildungsscheck Brandenburg für Beschäftigte
- Betriebliche Weiterbildung Brandenburg
- Type of Qualification:
- Zertifikat/Teilnahmebestätigung
- Final Examination:
- Nein
- Qualification Title:
- keine Angaben
- Certifications of the Course:
- Nicht zertifiziert
- Courses for Women only:
- Nein
- Childcare:
- Nein
- Link to Course:
- Zum Angebot auf der Anbieter-Website
- Quantity of Details:
- Suchportal Standard nicht erfüllt - further information
- Target Groups:
- Developers Solutions Architects Data Engineers Mitarbeiter, die wenig bis keine Erfahrung mit ML hat und die ML-Pipeline mit Amazon SageMaker kennenlernen möchten.
- Professional Requirements:
- - 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
- Technical Requirements:
- Keine besonderen Anforderungen.
- Classification of the Federal Employment Agency:
- C 1430-10-10 System-, Netzwerkadministration - allgemein
Contents
- 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
All statements without guarantee. The providers are solely responsible for the correctness of the given information.
Published on 20.06.2024, last updated on 12.08.2024