Machine Learning Engineering on AWS
Use AWS machine learning services to prepare data, train and tune models, deploy inference, and automate ML engineering workflows.
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-by SkillDynamo
- 0 Students
- Last updated
Course Description
SkillDynamo’s Machine Learning Engineering on AWS training develops practical, source-aligned skills for use AWS machine learning services to prepare data, train and tune models, deploy inference, and automate ML engineering workflows.
The 24 Hours curriculum is independently rewritten into four structured lessons. Learners move from essential concepts to implementation, secure operations, troubleshooting, applied scenarios, and solution validation.
What You Will Learn
- Machine learning lifecycle on AWS
- Data collection and preprocessing
- Feature engineering and data quality
- SageMaker training jobs
- Built-in algorithms and frameworks
- Hyperparameter tuning
Training Approach
Instructor-guided explanations, AWS service mapping, practical configuration or design activities, scenario analysis, validation checklists, and a final improvement plan.
Curriculum
- 1 Section
- 4 Lessons
- 24 Hours
SkillDynamo Expert Team
SkillDynamo Expert Team
Instructor-led delivery focused on the source-aligned scope of Machine Learning Engineering on AWS, with practical AWS service mapping, scenarios, validation, and troubleshooting.
- Structured explanations and demonstrations
- Applied exercises and design reviews
- Course-specific validation and improvement guidance
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