AWS Certified AI Practitioner - Concepts, Demos, Questions

Explore the fundamentals of ai and machine learning, clarify terminology, and outline learning techniques and deep learning processes. Preview ai life cycle and amazon ai and machine learning stack.

Learn how training data quality drives model performance, differentiate labeled from unlabeled data and structured from unstructured data, and cover supervised, unsupervised, and reinforcement learning with spam detection.

Explore how a trained machine learning model uses brand new data to draw conclusions through batch and real-time inferencing, including real-world examples like stop-sign recognition.

Explore deep learning fundamentals, neural networks and generative AI, and learn the foundational models lifecycle from data selection to training, optimization, evaluation, and fine-tuning, with Amazon AI services.

Explore Amazon SageMaker and Bedrock to build, train, and deploy models with tools like Clarify and Data Wrangler, and access foundation models via a serverless API.

Explore real world AI use cases across retail, healthcare, and financial services, and discover computer vision, natural language processing, and ML techniques powering Amazon AI model support.

Explore amazon bedrock foundation models, including titan embeddings and titan text g1 light express, and test via console and api calls for stability ai image and text prompts.

Learn prompts and prompt engineering to generate accurate ai outputs, including building blocks like instructions, context, examples, negative prompt technique, and zero-shot, few-shot, and chain-of-thought techniques.

Use negative prompting to guide the model away from producing content and prevent hate speech, explicit language, bias, and include keywords such as worst quality, low quality, and low blurry.

Explore responsible ai principles that ensure transparency and trustworthiness while mitigating bias across design, data, and interaction. Learn mitigation strategies—diverse data, explainability, diverse stakeholders, and auditing.

Explore gen AI challenges such as hallucination, toxicity, intellectual property concerns, cheating, and disruption of work, with exam emphasis on understanding these risks.

Compare models in Bedrock and SageMaker to evaluate latency and accuracy, then use SageMaker Clarify to assess metrics like accuracy, robustness, and toxicity.

Explore how foundation models use vector embeddings to convert text and media into vectors stored in vector stores for fast, scalable enterprise search with AWS OpenSearch, RDS Postgres, and Kendra.

Fine-tune foundational models to boost output specificity, accuracy, and efficiency with domain-specific, diverse data, covering instruction tuning, reinforcement learning from human feedback, domain adaptation, transfer learning, and continuous pre-training.

Explore model evaluation metrics such as ROUGE (ROUGE-N and ROUGE-L) for summarization and translation quality, BLUE for translation, and BIRD score for semantic similarity.

Explore data governance for AI apps, covering data life cycle, residency, logging, disposal, encryption in transit, monitoring, and retention. Address governance strategies, transparency, bias checks, and responsible AI practices.