Mastering AWS Certified AI Practitioner AIF-C01 - Hands On!

Map the ai landscape from artificial intelligence to machine learning and generative ai, clarify deep learning vs classical algorithms like xgboost and decision trees, and note transformer-based large language models.

Train models with labeled data to predict new labels, using supervised learning across image recognition, health outcomes, fraud detection, and regression methods like linear, polynomial, and logistic.

Evaluate supervised models by train/test split (80/20), measure accuracy with known labels, and apply k-fold cross-validation to guard against overfitting.

Explore unsupervised learning where data attributes reveal labels via k-means clustering and centroid updates. Grasp self-supervised learning with next-word labels and reinforcement learning with Pac-Man and Q-learning.

Explore the bias-variance trade-off and the role of regularization in preventing overfitting. Balance model complexity to improve accuracy on unseen data, considering training, evaluation, and testing data sets.

Explore a taxonomy of artificial intelligence algorithms by distinguishing supervised, unsupervised, and self-supervised machine learning, with examples like regression and neural networks, k-means, PCA, and GPT or Dall-E.

Explore natural language processing (NLP) as machine learning that interprets and analyzes human language, enabling chatbots, sentiment analysis, and insights from text, audio, and video data.

Trace the origins of deep learning from artificial neurons to modern deep neural networks. Explore emergent behavior, weights, and layered architectures powering natural language processing.

Learn how gradient descent trains neural networks by adjusting weights through epochs, exploring local minima via cost function, and hyperparameter tuning with learning rate and batch size to optimize training.

Explore convolutional neural networks (CNNs) and how they locate features across data, from images to text, using local receptive fields, convolution, and layered hierarchies inspired by the brain.

Explore recurrent neural networks for processing sequential data, including time series and language, and learn how memory cells and sequence-to-sequence encoder-decoder architectures enable diverse tasks.

Explore transformer architecture and self-attention, overcoming the information bottleneck in RNNs to enable parallel training and powerful language models via token embeddings, positional encoding, and decoder blocks.

Review transformer terms such as tokens and embeddings in embedding space, and explain their numerical representations and semantic relationships; explore top p, temperature, context window, and max token limits.

Generative adversarial networks train a generator and a discriminator to model latent vector distributions, using random noise to produce images and stop when real and generated images are indistinguishable.

Explore diffusion models as a text-to-image approach that converts text into embeddings, uses a stable diffusion model to iteratively denoise and produce an image tensor decoded by an image decoder.

Apply Amazon's machine learning design principles across the lifecycle—ownership, protection, resiliency, recoverability, reuse, automation, and monitoring—to frame business goals as machine learning problems, process data, and deploy and monitor models.

Identify business goals and balance model explainability with performance using SageMaker and explainability tools, while planning data permissions, compliance, KPIs, ROI, and environmental impact for responsible AI.

Frame a business problem as a machine learning task by navigating the ML lifecycle from data collection and preparation to training, deployment, monitoring, and continuous retraining.

Advance data processing by collecting data, preprocessing, labeling, and engineering features for model training, with ingestion into a data lake. Apply SageMaker pipelines, data cataloging, and security for unbiased training.

Learn to develop, train, tune, evaluate, and deploy machine learning models on AWS SageMaker, using data preparation, feature stores, model and data parallelism, and MLOps best practices.

Understand deploying trained models as inference code in containers, managed across endpoints and edge or cloud environments, with pipelines, monitoring, testing strategies, and optimization for efficient, scalable AI.

Master monitoring across the machine learning life cycle by enabling model observability, tracking, and drift detection with SageMaker model monitor, CloudWatch, and automated retraining pipelines.

Explore Amazon's well-architected machine learning lens, a JSON-based guide within the AWS Well-Architected tool that interviews your ML approach to boost best practices and outcomes.

Explore mlops as the machine learning lifecycle on AWS, automating data preparation, feature engineering, training, evaluation, deployment, and continuous training with governance.

Explore AI use cases across business domains, from intelligent document processing and AI based application performance monitoring to predictive maintenance, medical research, and data driven decision making.

Explore computer vision use cases from public safety and authentication to autonomous driving and medical imaging, including doorbell cameras, gesture interaction, facial recognition, and defect detection, with AWS Rekognition.

explore generative ai use cases across customer experience, content creation, and operations, including chatbots, personalization, text summarization, and synthetic data.

Explore prompt engineering, improving generative AI results and safety by asking the right way, and learn how retrieval augmented generation and external tools enhance context and output.

Learn the anatomy of a good prompt: instructions, context, input data, and output indicator. The example features two friends in cafe with genre preferences and a 200-word dialogue expressing enthusiasm.

Master practical prompt engineering by crafting clear, concise prompts with context, specifying output type, end format, and examples; embrace step-by-step thinking, chain-of-thought prompts, subtasks, and trial-and-error experimentation.

Explore zero-shot, few-shot, and chain of thought prompting, and learn how examples, sentiment analysis tasks, and step-by-step reasoning guide models to classify text.

Learn how prompt mis-use and prompt injection threaten AI systems, and how guardrails and system prompts prevent leakage of pii and bias.

Set up a billing alarm in the AWS console to monitor monthly expenses, choose a budget template, and receive email notifications if spending exceeds your threshold.

Explore how Amazon SageMaker supports the full machine learning lifecycle, from data preparation and training to deployment and inference, using S3, ECR, and scalable endpoints.

Prepare data in SageMaker from S3, process with containers, train using built-in or custom algorithms, then deploy via runtime endpoints or batch transform with edge and scaling options.

SageMaker studio unifies ML tooling in a visual IDE with notebooks and collaboration, while SageMaker experiments compare training runs and SageMaker debugger logs gradients and triggers rules.

Autopilot automates algorithm selection, data preprocessing, tuning, and deployment for tabular data. Load data from S3, use notebooks and leaderboards, explainability via Clarify, and optional hyperparameter optimization or ensembling.

Learn how SageMaker deployment safeguards enable safe real-time inference with deployment guardrails, blue-green, canary, and linear deployments, shadow tests, and auto rollbacks.

Explore SageMaker feature store, which organizes features into feature groups, offers online and offline stores for real-time and batch access, and supports secure, shared access across models.

Track end-to-end lineage in SageMaker to govern models and artifacts. Audit changes, visualize the machine learning pipeline, and enable cross-account lineage with AWS Resource Access Manager.

Explore, visualize, and transform data inside SageMaker Studio with Data Wrangler, performing exploratory data analysis, importing from S3 and other sources, applying over 300 transformations, and training quick models.

Explore SageMaker canvas, a no-code machine learning tool for business analysts that builds models from CSV data, handles classification and regression, and automatically cleans data before sharing with SageMaker Studio.

Explore SageMaker Studio and Canvas, using Data Wrangler to wrangle data, build a predictive model, and generate absenteeism predictions from a tabular dataset.

Explore SageMaker built-in algorithms like linear learner for regression and logistic classification, XGBoost for boosted trees, and seq2seq for translation and summarization using RNNs, CNNs, and self-attention.

Explore built-in SageMaker algorithms DeepAR, BlazingText, Obj2Vec, and object detection to forecast time series, classify text, embed words and objects, and detect objects with bounding boxes and confidence scores.

Explore SageMaker built-in algorithms, including image classification, semantic segmentation, random cut forest, neural topic model (NTM), and LDA, highlighting unsupervised learning and anomaly detection.

Explore SageMaker's built-in algorithms, including KNN, K-means, PCA, factorization machines, and IP insights, with hands-on unsupervised and supervised techniques for classification, regression, clustering, anomaly detection, and recommender systems.

Master the old-fashioned SageMaker workflow by creating a training job with built-in XGBoost, training on S3 data, and running a batch transform to generate predictions.

Explore foundation models in AWS, from large language models like GPT and Claude to image models like Stable Diffusion, and how SageMaker Jumpstart connects third-party models within bedrock.

Explore Bedrock, AWS's serverless platform for generative AI, with a console and API to compare foundation models, fine-tune, use knowledge bases for retrieval augmented generation, and run agents.

Explore the Amazon Bedrock Playground to experiment with foundation models, compare chat and image modalities, adjust prompts and tokens, explore model reasoning, and learn about routing, guardrails, and prompt caching.

Fine-tune foundation models with bedrock to adapt large or small language models to your use case using labeled data, enabling smarter chatbots, up-to-date knowledge, and cost-efficient prompts.

Explore retrieval augmented generation with bedrock knowledge bases and vector search. Learn to embed data, query a vector store, and fuse results into prompts for reliable AI-powered search.

Create a bedrock knowledge base with a vector store from an S3 data source, test retrieval with Titan G1 embeddings, and learn about data sync and chunking for agent systems.

Explore amazon bedrock guardrails for content filtering, including profanity, topic controls, hate speech, and personal data detection, plus prompt masking and contextual grounding to reduce hallucinations in Rag systems.

Create guardrails in bedrock to block politics, hate speech, prompt attacks, and personal data. Test and deploy with configurable messages, word filters, grounding, and cross-region inference for compliant AI responses.

Discover how lm agents combine foundation models, knowledge bases, and guardrails with tools like lambda functions to act in the real world.

Create and deploy a Bedrock agent that uses memory, a knowledge base and guardrails, defines a weather action group with a lambda, and prepares for production deployment via alias.

Explore advanced Bedrock features, including importing your own models from S3 or SageMaker, and built-in model evaluation and monitoring. Use Bedrock Studio for web-based collaboration with IAM Identity Center integration.

Enable bedrock model invocation logging in settings to capture request, response data, and metadata, with logs to CloudWatch or S3 (or both) for all endpoints including text, images, and embeddings.

Explore bedrock pricing models—on demand, batch, and provisioned throughput—along with token and image costs, cross-region inference, and essential encryption, VPC isolation, and Privatelink security.

Deploy ai agents at scale with Bedrock's agent core, a serverless, framework-agnostic runtime that wraps any agent SDK, and provides short and long-term memory, gateways, and observability with a toolkit.

Explore AgentCore memory and tools, including short-term session memory and long-term memory with insights and user preferences, plus browser and code interpreter tools.

Import bedrock agents into agent core to extend capabilities with Strands SDK, Lang Chain. Use the gateway to expose tools as mcp-like endpoints and manage credentials with agent core identity.

Define Cedar-based policies in Bedrock Agent Core to govern gateway tool calls. Enforce least privilege with default deny, offer log-only or enforcement modes, and generate Cedar from plain language.

Evaluate agent performance with Agent Core Evaluations to measure faithfulness, helpfulness, correctness, and safety. Monitor results in CloudWatch across sessions, traces, or tool calls.

Interact with Amazon Q business, a rag chatbot that ingests your documents, answers questions, and integrates with 40+ enterprise apps via web interface or api.

Discover Amazon Q apps in Q business, build web-embedded LLM apps that summarize uploaded documents via prompt-driven cards, with lite and pro pricing and storage considerations.

QuickSight offers fast, cloud-powered data visualization for dashboards and charts across browsers and mobile devices, using diverse data sources. QuickSight Q adds a natural language interface to answer business questions.

Explore Amazon Comprehend, a natural language processing and text analytics service that extracts entities and sentiment. It provides phrases, language, syntax, topics, classifications, PII redaction, and training on your data.

Explore Amazon Translate, a deep learning service that translates text between languages, auto-detects source language, supports custom vocabularies via CSV or HTML, and offers API-ready requests for runtime integration.

Explore Amazon Transcribe, AWS’s speech-to-text service, which converts audio to text in flac, mp3, mp4, or wav formats, supports real-time streaming, and offers speaker and language auto-detection with custom vocabularies.

Explore Amazon Polly as a text-to-speech service with multiple voices and languages, and learn to use lexicons and SSML for pronunciation, emphasis, timing, including speech marks for lip syncing.

Explore Amazon Rekognition, a general computer vision service for objects, scenes, and faces. Use custom labels, text extraction, video analysis, and celebrity recognition with S3 or inline images.

Explore Amazon Forecast, a fully managed machine learning service that delivers highly accurate forecasts from time series data using a suite of algorithms, including deep learning, to predict future values.

Explore how Amazon Lex powers chatbots by mapping utterances to intents, using slots, and invoking lambda functions to fulfill requests, deployable to messenger, slack, and twilio via AWS SDK.

Explore Amazon Personalize, a fully managed recommender engine that uses past purchases and behavior to deliver real-time or batch recommendations, including personalized rankings, similar items, and contextual suggestions.

Explore a quick tour of AWS AI and ML services, including Textract for OCR, Fraud Detector for risk, CodeGuru for automated review, and augmented AI for human review.

Fine-tuning foundation models adds data into the model with new weights and layers to boost specificity, accuracy, and efficiency, contrasting with retrieval-augmented approaches.

Explore how reinforcement learning from human feedback (RLHF) combines supervised fine-tuning with a reward model and proximal policy optimization to align AI replies with human goals using SageMaker Ground Truth.

Prioritize quality over quantity when preparing data for fine tuning, using specific, relevant examples. Ensure accurate labels, address bias and governance for compliant, generalizable data.

Assess foundation models using benchmark prompts, measure accuracy, speed, scalability, and retrieval augmented generation, while guarding against data leakage and unreliable human judgments; adopt hybrid evaluation with judge models.

Compare language model outputs to ground truth using rouge, bleu, and bert score, examining unigram and n-gram overlaps, longest common subsequence, precision, and embedding-based semantic similarity.

Select an evaluation path based on labeled data, using precision and F1 for discrete targets, or Rouge and Burt score for free text, with humans in the loop.

Learn to evaluate models with a confusion matrix, comparing true positives, false positives, false negatives, and true negatives, and use recall, precision, F1, and RMSE to assess performance.

Evaluate machine learning models with ROC curves and AUC, comparing true and false positive rates and visualizing area under the curve beyond random chance; consider PR curves for precision-recall insights.

Learn to evaluate regression models with r-squared, RMSE, and MAE, and distinguish these numerical metrics from classification measures like accuracy, precision, recall, and F1.

Explore the dimensions of responsible AI—fairness, explainability, privacy, safety, controllability, veracity and robustness, governance, and transparency—and learn how AWS tools like Bedrock and SageMaker support responsible AI.

Master responsible AI with Bedrock and SageMaker Clarify, evaluating accuracy, robustness, and toxicity; apply guardrails, detect bias, balance data, explain decisions, and monitor with humans in the loop.

Explore how SageMaker Clarify enables explainability by assessing pretraining bias through class imbalance and DPL, and by using PDPs and Shapley values to interpret feature influence.

Explore governance frameworks and how SageMaker role manager, model cards, and the model dashboard enable responsible AI deployment across services like Rekognition, Textract, and Transcribe.

Evaluate models with SageMaker or Bedrock, selecting a foundation model that fits your use case, audience, and scale, while ensuring responsible agency through testing, tuning, and transparency.

Balance data, apply data augmentation for underrepresented groups, correct biases with SageMaker Data Wrangler, and use cleaning, feature selection, and normalization for equitable preprocessing.

Explore how AI models reveal decision processes through transparency and explainability, use model cards and tools to audit, and balance performance with safety.

Explore human-centered design in AI, covering reinforcement learning from human feedback, explainable AI, unbiased and transparent decision making, and accessible, personalized learning for humans and AI.

Defense in depth guides AWS AI security, emphasizing data protection with at-rest encryption via KMS, in-transit TLS, IAM, private connectivity via PrivateLink, and least-privilege governance.

Explore ai security services on AWS, using SageMaker role manager for persona-based IAM roles, Macie to scan S3 data lakes for PII/PHI, Inspector for vulnerabilities, and Detective for forensic investigations.

Safeguard data in engineering pipelines by tracing data lineage and provenance, citing sources, masking PII, and enforcing privacy, access control, and data integrity across AWS tools and stages.

Learn how the AWS shared responsibility model divides security: AWS protects the cloud infrastructure, while you secure data, services, patches, and credentials with least privilege.

Develop an AI governance framework with a cross-functional board to mitigate bias, privacy risks, and regulatory requirements, guided by standards like NIST 853, ISO 27001, HIPAA, GDPR, and PCI-DSS.

Develop and enforce data governance for AI applications by ensuring data quality and integrity, tracking lineage and provenance, applying lifecycle management, protecting privacy, and implementing policies, roles, and audit processes.

Explore AWS governance services for auditing and security, including AWS Config for configurations, Amazon Inspector for vulnerabilities, AWS Audit Manager for evidence integrity, and AWS CloudTrail for activity logs.

Explore AWS's generative AI security scoping matrix across five scopes—from consumer apps to self-trained models—and five security dimensions: governance and compliance, legal and privacy, risk management, controls, and resilience.