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Beginnt 4 June 2026 02:54
Endet 4 June 2026
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Sekunden
4 hours
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Paid Course
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Übersicht
Gain hands-on experience with AWS machine learning services through gamified, real-world scenarios and interactive challenges designed for ML engineers and data scientists.
Lehrplan
- Introduction to AWS Machine Learning
- Data Preprocessing and Management
- Building Machine Learning Models
- Training Machine Learning Models
- Model Evaluation and Validation
- Deploying Machine Learning Models
- Automation and CI/CD in AWS
- Security and Compliance in AWS ML
- Real-World Scenarios and Challenges
- Capstone Project
- Conclusion and Next Steps
Overview of AWS Machine Learning services
Setting up your AWS account and environment
Introduction to Amazon SageMaker
Storing and retrieving data with Amazon S3
Using AWS Glue for ETL processes
Data preparation and feature engineering in SageMaker
Introduction to built-in algorithms in Amazon SageMaker
Custom model development with Jupyter notebooks
Utilizing SageMaker Studio for model development
Understanding managed training services
Optimizing training jobs with hyperparameter tuning
Distributed training and using spot instances
Techniques for validating model performance
Monitoring training jobs
Practical tips for model evaluation on AWS
Model deployment options with SageMaker
Real-time and batch inference
A/B testing and endpoint scaling
Implementing MLOps with SageMaker Pipelines
Automating end-to-end workflows with AWS Step Functions
Version control and continuous deployment with AWS CodePipeline
Security best practices for AWS ML services
Managing IAM roles and policies
Data encryption and compliance considerations
Gamified exercises using real-world industry data sets
Interactive ML challenges and competitions
Case studies on successful ML implementations on AWS
Design and implement a complete ML solution on AWS
Presentation and demonstration of the project
Peer review and feedback
Resources for further learning in machine learning on AWS
Career paths and certification opportunities
Q&A and course wrap-up
Fachgebiete
Data Science