Learn Generative AI and pass AWS AI Certification

Explore ai and ml fundamentals, tracing the data and cloud computing trends that fueled generative ai. Understand artificial intelligence, machine learning, and their everyday uses like facial detection and search.

Explore how classical programming rules and machine learning models generate product recommendations from a customer's purchase history and profile data, and how AI-powered models adapt over time.

Define and explain a model as a trained algorithm that identifies patterns and anomalies in data, enabling tasks from fraud detection to recommendations.

Learn how a machine learning model trains on data using a recipe-like algorithm, improves through feedback, and performs inference in production with specialized hardware.

Understand supervised, unsupervised, and reinforcement learning, including labeled-data mapping, clustering, and reward-based trial-and-error in agent environments like self-driving cars.

Compare supervised, unsupervised, and reinforcement learning, highlighting labeled versus unlabeled data, agent–environment dynamics, and use cases like self-driving cars, anomaly detection, fraud detection, and recommendation systems.

Explore data types for AI models, including labeled and unlabeled data, images, text, and time series, and learn how structured, semi-structured, and unstructured data can drive training and inference.

Inference runs unseen data through a trained model to make predictions, with batch inference processing stored requests at intervals and real-time inference delivering instant results for chatbots and smart cars.

Explore deep learning and neural networks, how multilayer architectures simulate brain processes, enabling generative AI, image and voice recognition, and complex decision making.

Explore generative AI, a branch of artificial intelligence that creates new content, such as text or images, driven by prompts and training data in foundation models.

Foundation models form the base of generative AI, trained on broad unlabeled data, and adaptable to many tasks including text generation, summarization, Q&A, multimodal outputs, and chatbots.

Explore how large language models, trained on vast text data, process natural language and generate human-like text for tasks like chatbots, translation, and summarization, with examples like ChatGPT and Bard.

Trace the evolution from word embeddings and word2vec to transformer models, highlighting attention, context, and how these architectures enable large language models and text generation.

Learn how a transformer model completes sentences through tokenization, encoding, word embeddings, and decoding to predict the next word in a given context.

Discover how generative AI works on AWS, leveraging AWS Trainium and AWS Inferentia for training and inference, with Amazon SageMaker and Amazon Bedrock for building or using foundation models.

Discover how Amazon Bedrock unifies foundation models behind a single API, enables privacy and security, and supports customization and retrieval augmented generation for scalable generative AI apps.

Explore the Amazon Bedrock demo, navigate the console, enable access, and use chat and text playgrounds to explore Titan and other foundation models through prompts and examples.

Learn the basics of Amazon Bedrock concepts: tokens, context windows, and prompts. Understand input and output tokens, cost implications, and how to craft effective prompts for better results.

Customize foundation models to domain-specific data and terminology to improve context awareness and response quality. Start with prompt engineering, then RAG, fine-tuning, or training from scratch by complexity and cost.

Master prompt engineering to guide foundation models through in-context learning, persona, and chain-of-thought prompting from zero-shot to few-shot for clearer, better AI responses.

Discover retrieval augmented generation (rag) that fetches external knowledge, augments prompts with relevant data, and lets the model generate up-to-date, domain-specific answers.

Learn how the knowledge base in Amazon Bedrock enables retrieval-augmented generation by sourcing external data, chunking and embedding it into a vector store to inform a foundation model.

Explore how Amazon Bedrock agents extend foundation models by orchestrating knowledge bases, data sources, and APIs to perform tasks and return sourced, actionable answers.

Learn how bedrock agents orchestrate tasks by linking knowledge bases and action groups to lambda functions, enabling pricing, ordering, and reviews through OpenAPI or API endpoints.

Compare on-demand and provisioned throughput for Amazon Bedrock pricing, and learn how pay-as-you-go, commitments, and token-based costs affect inference pricing.

Learn how Amazon Bedrock guardrails enforce responsible AI usage by filtering harmful content, PII, and denial topics. They prevent prompt injection and hallucination across all foundation models.

Control randomness and diversity in foundation models' outputs by adjusting temperature, top p, and top k to tailor creativity and response specificity.

Customize foundation models for domain-specific tasks through fine tuning and retraining, deciding when to use prompt engineering, rag, or agents before training from scratch.

Compare fine-tuning and continued pre-training to customize models on Amazon Bedrock. Use fine-tuning for task-specific outputs with labeled data and continued pre-training for domain enrichment with unlabeled data.

Explore how to customize a base model in Amazon Bedrock through fine-tuning and continuous pre-training. Manage inputs, outputs, and logs, and deploy with provisioned throughput for secure inferences.

Explore why organizations may build or train their own models when foundation models fall short, weighing prompt engineering, model customization, and from scratch training against business needs, costs, and control.

Build a beer or wine prediction model using features such as color and alcohol percentage, applying supervised learning and linear regression, with data balancing, training, evaluation, and deployment.

Explore the roles in an ML team from data engineers to data scientists and ML engineers, mapping responsibilities across data ingestion, feature store, validation, deployment, and monitoring.

Explore the machine learning workflow and MLOps from data preparation to deployment in AWS SageMaker. Iteratively train, monitor, and retrain models with data engineering and DevOps to maintain production readiness.

Explore Amazon SageMaker’s end-to-end ml workflows, no-code tools, data labeling and preparation, MLOps automation, and the bedrock vs SageMaker use-case distinctions for building or deploying foundation models.

Explore key SageMaker components and features tailored for exam use cases, focusing on prepare, build, train, and deploy stages, with configuration options and end-to-end workflow visibility.

Learn to prepare data with Amazon SageMaker Canvas and Studio, using no-code tools for exploratory data analysis, Data Wrangler, and Feature Store to build, train, and deploy models.

Build your model by leveraging SageMaker tools—SageMaker Clarify for bias and explainability, model cards and registry, Studio Notebook, Jumpstart, and Autopilot—to accelerate training and deployment.

Amazon SageMaker's distributed training across multiple nodes and accelerators, including automatic model tuning and hyperparameter optimization, and deploy inference endpoints.

Deploy your model with SageMaker pipeline to build, verify, and productionize end-to-end ML workflows, then use SageMaker Neo and Model Monitor for edge deployment and quality monitoring.

Explore deploying a trained model with SageMaker endpoints for real-time, batch, asynchronous, and serverless inferences. Understand how endpoints, compute, and S3 data flow enable scalable predictions.

Demonstrate end-to-end machine learning with Amazon SageMaker Canvas and Data Wrangler, from data preparation to training, evaluation, and deployment of a deposit-purchase classifier.

Learn end-to-end model building in sageMaker canvas, from training and evaluating with accuracy, precision, recall, and confusion matrix to deploying real-time endpoints and performing batch predictions.

Learn to monitor generative AI models by tracking business metrics and technical metrics, assessing impact on customer support, personalized recommendations, and fraud detection against benchmarks.

Master monitoring metrics for generative AI using SageMaker and CloudWatch, covering response time, accuracy, and confusion matrices. Explore benchmark data sets, rogue/blue/bert scores, and transfer learning with Jumpstart.

Learn to manage generative ai responsibly by identifying risks such as hallucination, bias, and plagiarism. See how technical safeguards and policy frameworks mitigate these challenges.

Tackle generative AI challenges by auditing training data, applying guardrails and machine unlearning, using data augmentation, pursuing explainability, and leveraging human-in-the-loop moderation for responsible AI.

Discover the AWS AI/ML stack—from foundational AWS Trainium and Inferentia to Bedrock and SageMaker—with core and specialized services for text, speech, vision, and fraud detection.

Augment AI by routing low-confidence predictions to a built-in human review workflow in SageMaker, and use AWS Mechanical Turk or private workers to ensure precision.

Leverage Amazon Comprehend to extract insights from text, analyze sentiment, detect PII, and identify entities, key phrases, and syntax across emails, chats, reviews, and transcripts.

Discover how Amazon Fraud Detector detects fraud in online transactions by using S3 data, training a model, building an event data set, and generating real-time fraud predictions.

Explore how Amazon Kendra delivers an ML powered enterprise search service that indexes data from Salesforce, Slack, S3, Box, and SharePoint for fast, natural language queries across apps.

Explore amazon lex to build voice and text chatbots, using templates and cloudformation to deploy bots for customer service, order tracking, and returns, with dynamodb, lambda, and amazon connect integration.

Discover how Amazon Personalize powers real-time recommendations by feeding user data, history, and metadata, then training, tuning, and hosting models for tailored suggestions in apps.

Learn how Amazon Polly converts text to lifelike speech for content creation, e-learning, and phone conversations, with options for neural voices, languages, and easy deployment via API calls.

Explore Amazon Q business and Amazon Q developer, AI assistants that answer questions by indexing data with Kendra. Set up IAM Identity Center SSO and integrate with QuickSight.

Learn how Amazon Rekognition enables image and video analysis, including face comparison, facial analysis, text extraction, label detection, and liveliness checks, with API-driven workflows and streaming video processing.

Amazon Textract extracts text, handwriting, and layout from scanned documents. Analyze PDFs, forms, and tables with confidence scores to identify data such as names, amounts, and signatures.

Amazon Transcribe, a speech recognition service that generates transcripts from video and audio, with real-time streaming or batch processing from S3 and JSON outputs for analytics.

Amazon translate enables fluent, accurate machine translation across 505,500 language combinations, with inputs in text or utf-8 and outputs to text or S3, plus custom terminologies and source language detection.

Discover how AWS AI services integrate with CloudWatch, CloudTrail, VPC endpoints, and AWS PrivateLink to keep traffic private and fast, while AWS Glue and AWS Artifact support compliance documentation.

Prepare for the aws ai certification by reviewing the public exam guide; the 90-minute, 65-question exam tests ai, ml, and generative ai concepts.