AWS Certified AI Practitioner
AWS Certified AI Practitioner
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Chapter 1
AWS AI and Machine Learning Services
Overview of AWS AI/ML services, differences between pretrained vs custom, infrastructure, shared responsibility, service selection.
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Chapter 2
Framing Machine Learning Problems
Translate business objectives into ML problem statements; choose learning approaches; define targets, metrics, and success criteria.
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Chapter 3
Formulating Data Requirements
Identify and gather structured/unstructured data, formats, labelling, dataset splits; explore AWS storage, privacy, encryption, and governance best practices.
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Chapter 4
Exploring and Visualizing Data
Apply EDA: summary statistics, missing values, outliers, correlations; use SageMaker Data Wrangler and QuickSight to visualize and derive insights.
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Chapter 5
Developing Machine Learning Models on AWS
Select algorithms, train and tune with SageMaker, use Autopilot for AutoML, and manage experiments, hyperparameters, and model evaluation.
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Chapter 6
Operationalizing ML Solutions
Deploy models with endpoints, batch or serverless; implement monitoring, CI/CD, security, VPC, cost optimization via SageMaker and AWS tools.