Comprehensive AWS Certified AI Practitioner AIF-C01 Bootcamp

Understand the machine learning process from training data to a deployable model, and explore how Amazon SageMaker enables building, training, and deploying ML at scale.

Explore anomaly detection and density estimation in unsupervised learning. See how isolation forest and kernel density estimation reveal patterns with fraud detection and customer segmentation examples.

Learn reinforcement learning where an agent maximizes rewards through trial and error in an environment, using model-based or model-free approaches, and reinforcement learning from human feedback to fine-tune foundation models.

Compare batch and real-time inferencing, focusing on latency and payload size to decide the best approach. See how SageMaker batch Transform and real-time endpoints support these workflows.

Explore Amazon Rekognition, a fully managed computer vision service that analyzes images and videos to detect objects, people, text, scenes, activities, and inappropriate content, with content moderation and custom labels.

Explore Amazon Rekognition's capabilities, including label detection with confidence scores, image properties analysis, moderation, facial analysis and liveness, celebrity recognition, text extraction, PPE detection, and custom labels.

Explore Amazon Fraud Detector, a fully managed service that enables data preparation in S3, real-time evaluation with a 0 to 1000 risk score, and automated rule-based protection.

Explore Amazon SageMaker AI with its integrated development environment, prebuilt algorithms, and automated training, and map the ML development lifecycle from problem definition to deployment using technical and business metrics.

Navigate the seven-phase machine learning development lifecycle—from business goal identification through ML problem framing, data processing, model development, deployment, monitoring, to retraining—with AWS support.

Explore the machine learning development lifecycle's training, tuning, and evaluation subphases. Learn about data splitting, feature scaling, batch size, learning rate, epochs, and overfitting, with SageMaker guidance.

Map AWS SageMaker AI tools to the ML pipeline components—data preparation, training and tuning, deploy and manage—covering Studio, Unified Studio, Canvas, Data Wrangler, Processing Jobs, and Feature Store.

Learn regression performance metrics—mae, mse, rmse, r-squared, and mape—through a house price example to quantify prediction errors and explained variation.

Explore how SageMaker AI provides end-to-end ML with unified Studio, Jumpstart, and HyperPod, and follow the ML development lifecycle from data processing to deployment, monitoring, and model registry.

Explore the foundation model life cycle from data collection and pre-processing to deployment, including pre-training, fine-tuning, and evaluation. Understand architecture choices and how large-scale data and resources enable general-purpose models.

Explore how generative AI transforms business operations, boosting creativity and streamlining processes. Assess advantages like adaptability and scalability, address biases and security concerns to guide model selection and boost ROI.

Discover the advantages of generative AI, including adaptability, real-time responsiveness, and simplicity. See how creativity, data efficiency, personalization, and scalability enable innovative, scalable solutions across industries.

Explore the disadvantages of generative AI, including toxicity, hallucinations, non-determinism, regulatory violations, and data privacy risks, with mitigations through governance and AWS SageMaker tools.

AWS infrastructure enables generative AI with Bedrock and SageMaker, lowers entry barriers, speeds time to market with pay-as-you-go efficiency, and emphasizes security, compliance, and responsible AI practices.

Test chat, text, image, and video models in the Amazon Bedrock playground from providers like AI21 Labs and Anthropic, and compare outputs with adjustable inference settings.

Explore PartyRock, a no-code ai-powered playground built on Amazon Bedrock foundation models. Create ai-powered apps with a widget-based system, without an AWS account, and try a free trial.

Learn six key cost factors for AWS AI services, including on demand vs provisioned pricing, responsiveness, redundancy, hardware options, token pricing, and custom models.

Explore your GenAI toolkit with essential AWS services and features, including Bedrock foundation models, SageMaker, Jumpstart, Party Rock, and Cube, plus security, pricing, and deployment options.

Explore Amazon Bedrock knowledge bases, a fully managed RAC workflow that augments foundation models with private data to deliver accurate, context-aware responses; it covers pre-processing and runtime phases.

Explore foundation model customization methods—pre-training, fine-tuning, continued pre-training, in-context learning, prompt engineering, and rag—alongside cost and implementation considerations in Amazon Bedrock.

Explore prompt engineering and AI vulnerabilities, define zero-shot, few-shot, and chain-of-thought prompting with templates, and learn best practices for clear prompts with context and output considerations while avoiding jailbreaking.

Explore prompt engineering techniques to optimize AI responses, including zero-shot prompting, single-shot prompting, few-shot prompting, chain-of-thought prompting, and prompt templates, using instructions, context, input data, and output indicator.

Master prompt engineering by optimizing instructions, context, output indicators, and formats for higher quality and specificity. Apply best practices, break down tasks, and experiment iteratively.

Explore AI vulnerabilities—exposure, poisoning, hijacking, and prompt injection—and learn how to secure AI applications on AWS and in cloud environments.