AWS Certified AI Practitioner Training & Certification
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- Important IT Service Management Practices.
- Real World Application Via Case Studies
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AWS Certified AI Practitioner Certification
The AWS Certified AI Practitioner Certification is a foundational credential implemented for professionals who seek to demonstrate a strong understanding of Artificial Intelligence, Machine Learning, and fundamental concepts of Generative AI. This certification includes the use of these technologies and their implementation with the help of AWS services.
Our AWS Certified AI Practitioner Certification also focuses on practical use cases, implementing AI solutions to resolve complex business problems and helping professionals to analyse and identify the causes of errors. This certification will help you demonstrate your ability to contribute to AI-related projects and discussions in your business while showcasing your knowledge of AWS AI Services. AWS Certified AI Practitioner Certification is a globally recognized credential for professionals who want to start their AI journey.
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What You Will Get?
Learning Outcome
After the completion of the course, the participants would be able to:
Align various AI practices with the organization.
Learn AWS AI-related use cases and practices for businesses.
Understand operations of AWS AI.
Understand AWS Framework for AI and ML.
Learning Security and Compliance Practices.
Course Curriculum
Fundamentals of AI and ML (20%)+
- Basic AI/ML concepts, types of data, and learning methods (supervised, unsupervised, reinforcement).
- Use cases and capabilities of AWS AI/ML services (SageMaker, Polly, Lex, etc.).
- ML lifecycle and performance metrics.
Fundamentals of Generative AI (24%)+
- Concepts like tokens, embeddings, and transformer-based models.
- Applications of generative AI (chatbots, code generation, etc.).
- AWS services for generative AI (SageMaker JumpStart, Amazon Bedrock).
Applications of Foundation Models (28%)+
- Pre-trained model selection and customization techniques.
- Prompt engineering techniques and training/fine-tuning models
- Evaluation methods and metrics for foundation models.
Guidelines for Responsible AI (14%)+
- Bias, fairness, transparency, and inclusivity in AI systems.
- Tools for responsible AI (SageMaker Clarify, Model Monitor).
- Principles of human-centred design and legal considerations.
Security, Compliance, and Governance for AI (14%)+
- Securing AI systems (IAM roles, encryption).
- Compliance standards and AWS tools for governance (AWS Config, CloudTrail).
- Best practices for secure and ethical AI implementation.