The Machine Learning Pipeline on AWS [GK7376]
Startdaten und Startorte
computer Online: VIRTUAL TRAINING CENTER 17. Jun 2024 bis 20. Jun 2024Details ansehen event 17. Juni 2024, 09:00-17:00, VIRTUAL TRAINING CENTER, NL225499.1 event 18. Juni 2024, 09:00-17:00, VIRTUAL TRAINING CENTER, NL225499.2 event 19. Juni 2024, 09:00-17:00, VIRTUAL TRAINING CENTER, NL225499.3 event 20. Juni 2024, 09:00-17:00, VIRTUAL TRAINING CENTER, NL225499.4 |
placeNieuwegein (Iepenhoeve 5) 16. Sep 2024 bis 19. Sep 2024Details ansehen event 16. September 2024, 09:30-17:30, Nieuwegein (Iepenhoeve 5), NL225500.1 event 17. September 2024, 09:30-17:30, Nieuwegein (Iepenhoeve 5), NL225500.2 event 18. September 2024, 09:30-17:30, Nieuwegein (Iepenhoeve 5), NL225500.3 event 19. September 2024, 09:30-17:30, Nieuwegein (Iepenhoeve 5), NL225500.4 |
computer Online: VIRTUAL TRAINING CENTRE 16. Sep 2024 bis 19. Sep 2024Details ansehen event 16. September 2024, 09:30-17:30, VIRTUAL TRAINING CENTRE, NL225500V.1 event 17. September 2024, 09:30-17:30, VIRTUAL TRAINING CENTRE, NL225500V.2 event 18. September 2024, 09:30-17:30, VIRTUAL TRAINING CENTRE, NL225500V.3 event 19. September 2024, 09:30-17:30, VIRTUAL TRAINING CENTRE, NL225500V.4 |
computer Online: VIRTUAL TRAINING CENTER 9. Dez 2024 bis 12. Dez 2024Details ansehen event 9. Dezember 2024, 09:30-17:30, VIRTUAL TRAINING CENTER, NL225501.1 event 10. Dezember 2024, 09:30-17:30, VIRTUAL TRAINING CENTER, NL225501.2 event 11. Dezember 2024, 09:30-17:30, VIRTUAL TRAINING CENTER, NL225501.3 event 12. Dezember 2024, 09:30-17:30, VIRTUAL TRAINING CENTER, NL225501.4 |
Beschreibung
Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
OBJECTIVES
In this course, you will learn to:
- Select and justify the appropriate ML appr…
Frequently asked questions
Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!
Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
OBJECTIVES
In this course, you will learn to:
- 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 using 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
AUDIENCE
This course is intended for:
- Developers
- Solutions Architects
- Data Engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
CONTENT
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
- Practice problem formulation
- Formulate problems for projects
Day Two
Checkpoint 1 and Answer Review
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
Day Three
Checkpoint 2 and Answer Review
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
Day Four
Checkpoint 3 and Answer Review
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
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