[NEW] Ultimate AWS Certified AI Practitioner AIF-C01

Explore how the AWS cloud underpins AI workloads and how managed AI services like Amazon Bedrock operate, in the context of the AWS certified AI practitioner exam.

Learn how cloud computing delivers on-demand compute, memory, storage, databases, and networking, with IP addresses, routers, switches, and DNS, replacing traditional on-premises data centers.

Discover the on-demand, pay-as-you-go model that powers instant provisioning of compute, storage, and applications with AWS. Learn how cloud types—public, private, and hybrid—offer scalability, cost efficiency, and global accessibility.

Create an AWS account on a free plan, verify identity with a root user email and phone, set a compliant password, and access the console for hands-on learning.

Explore the AWS console, select the nearest region to reduce latency, and use the services list or search bar to compare global versus regional availability.

Set up a zero-spend and monthly cost budget with alerts in the AWS billing console. Enable IAM access to billing from the root account and review bills by service.

Discover what generative AI is, from foundation models and large language models to diffusion-based image generation, and understand non-deterministic outputs. Explore examples like GPT-4o and AWS Bedrock.

Explore how to choose a base foundation model on Amazon Bedrock by weighing performance, customization, context windows, and token limits across Amazon Titan, Llama-2, Claude, and Stability AI.

Evaluate foundation models on Amazon Bedrock using automatic evaluation and benchmark datasets. Compare model outputs to benchmark answers with metrics such as rouge, bleu, bertscore, and perplexity to gauge quality.

Explore guardrails in Amazon Bedrock to control interactions with foundation models, filter harmful content, block topics like food recipes, and monitor inputs to reduce hallucinations and protect privacy.

Explore how Amazon Bedrock integrates with CloudWatch. Log all model invocations to CloudWatch Logs or S3, including inputs, outputs, and embeddings, with real-time insights and guardrail metrics.

Explore prompt engineering by designing prompts with instructions, context, input data, and output indicators, enhanced by negative prompting to guide foundation models toward clear, concise AWS summaries.

Discover Amazon Q Developer, an AWS assistant that answers questions about documentation and resources and acts as a code companion. Lists Lambda functions, suggests CLI commands, and analyzes bills.

PartyRock offers a playground without an AWS account, not a real AWS service, powered by Amazon Bedrock to build gen AI apps with widgets and prompts, restaurant recommendations, recipe ideas.

Explore the behind-the-scenes ideas of AI, ML, DL, and generative AI in a theory-oriented section that goes beyond concrete AWS services, preparing you for the exam.

Explore model evaluation using a confusion matrix for classification, with precision, recall, f1, and auc-roc, then assess regression with mae, mape, rmse, and r-squared.

Explore hyperparameters that define model structure and learning: learning rate, batch size, epochs, and regularization. Use grid or random search and SageMaker AMT to improve accuracy, generalization, and reduce overfitting.

Discover Amazon Comprehend, a natural language processing tool that extracts entities, PII, dates, and sentiment from text, enables syntax analysis, and supports custom classification with training data.

Discover Amazon Transcribe's automatic speech recognition that converts audio to text, with PII redaction, multilingual language identification, custom vocabularies, domain-specific language models, and toxicity detection for searchable transcripts and metadata.

Explore Amazon Transcribe to generate transcripts from audio, enable PII redaction, and stream in multiple languages with automatic language identification, including English and French.

Explore Amazon Rekognition's capabilities—label detection with object and custom labels, image properties, moderation, facial analysis and comparison, text in image, celebrity recognition, and PPE detection.

Explore Amazon Personalize, a fully machine learning service for real-time personalized recommendations built with ready-to-use recipes. Use cases span retail, media, and entertainment, with data from S3 and real-time APIs.

Discover how SageMaker Data Wrangler and SageMaker Feature Store streamline data preparation, transformation, feature engineering, and exporting data flows for machine learning in SageMaker Studio.