/aws-certified-ai-practitioner

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aws-certified-ai-practitioner

  • Artificial Intelligence is a field of computer science dedicated to solving problems that we commonly associate with human intelligence.
  • Artificial Intelligence > Machine Learning > Deep Learning > Generative AI.
  • Generative AI is used to generate new data that is similar to the data it was trained on. To generate data, we must rely on a Foundation Model. Foundation Models are trained on a wide variety of input data. The models may cost tens of millions of dollars to train.
  • Large Language Models (LLM) is the type of AI designed to generate coherent human-like text (Chat GPT).
  • Non-deterministic: the generated text may be different for every user that uses the same prompt.
  • Amazon Bedrock
    • Build Generative AI (Gen-AI) applications on AWS.
    • Fully-managed service, no servers for you to manage.
    • Keep control of your data used to train the model.
    • Access to a wide range of Foundation Models (FM)
  • Amazon Titan
  • High-performing Foundation Models from AWS.
  • Can be customized with your own data.
  • Automatic Evaluation vs Human Evaluation.
  • Business Metrics to Evaluate a Model On: User Satisfaction, Average Revenue Per User (ARPU), Cross-Domain Performance, Conversion Rate and Efficiency.
  • RAG = Retrieval-Augmented Generation
  • Allows a Foundation Model to reference a data source outside of its training data.
  • Amazon Bedrock – Guardrails
    • Control the interaction between users and Foundation Models (FMs).
    • Filter undesirable and harmful content.
    • Remove Personally Identifiable Information (PII).
    • Reduce hallucinations
  • Prompt Engineering = developing, designing, and optimizing prompts to enhance the output of FMs for your needs.
  • Negative Prompting is a technique where you explicitly instruct the model on what not to include or do in its response.
  • Zero-Shot Prompting - Present a task to the model without providing examples or explicit training for that specific task.
  • Few-Shots Prompting - Provide examples of a task to the model to guide its output.
  • Chain of Thought Prompting - Divide the task into a sequence of reasoning steps, leading to more structure and coherence.
  • Retrieval-Augmented Generation (RAG) - Combine the model’s capability with external data sources to generate a more informed and contextually rich response.
  • Amazon Q Business- Fully managed Gen-AI assistant for your employees. Based on your company’s knowledge and data.
  • Amazon Q Apps - Create Gen AI-powered apps without coding by using natural language.
  • Amazon Q Developer - Answer questions about the AWS documentation and AWS service selection. Answer questions about resources in your AWS account.
  • Deep Learning - Uses neurons and synapses (like our brain) to train a model.
  • Supervised Learning
    • Learn a mapping function that can predict the output for new unseen input data.
    • Needs labeled data: very powerful, but difficult to perform on millions of datapoints.
    • Regression - Used to predict a numeric value based on input data.
    • Classification - Used to predict the categorical label of input data.
  • Feature Engineering - The process of using domain knowledge to select and transform raw data into meaningful features.
  • Unsupervised Learning - The goal is to discover inherent patterns, structures, or relationships within the input data.
  • Reinforcement Learning - A type of Machine Learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards.
  • • Inferencing is when a model is making prediction on new data.
  • AWS AI Services are pre-trained ML services for your use case.
  • Amazon Comprehend - Uses machine learning to find insights and relationships in text.
  • Amazon Translate.
  • Amazon Transcribe.
  • Amazon Polly - Turn text into lifelike speech using deep learning.
  • Amazon Rekognition - Find objects, people, text, scenes in images and videos using M.
  • Amazon Forecast - Fully managed service that uses ML to deliver highly accurate forecasts.
  • Amazon Lex & Connect - same technology that powers Alexa. Receive calls, create contact flows, cloud-based virtual contact center.
  • Amazon Personalize - Fully managed ML-service to build apps with real-time personalized recommendations.
  • Amazon Textract - Automatically extracts text, handwriting, and data from any scanned documents using AI and ML.
  • Amazon Kendra - Fully managed document search service powered by Machine Learning.
  • Amazon Mechanical Turk - Crowdsourcing marketplace to perform simple human tasks.
  • Amazon Augmented AI (A2I) - Human oversight of Machine Learning predictions in production.
  • AWS DeepRacer.
  • Amazon Transcribe Medical - Automatically convert medical-related speech to text.
  • Amazon Comprehend Medical - Amazon Comprehend Medical detects and returns useful information in unstructured clinical text.
  • Amazon SageMaker - Fully managed service for developers / data scientists to build ML models.
  • SageMaker Clarify - Evaluate Foundation Models. Ability to detect and explain biases in your datasets and models
  • SageMaker Canvas - Build ML models using a visual interface (no coding required).
  • SageMaker Automatic Model Tuning: tune hyperparameters
  • SageMaker Deployment & Inference: real-time, serverless, batch, async
  • SageMaker Studio: unified interface for SageMaker
  • SageMaker Data Wrangler: explore and prepare datasets, create features
  • SageMaker Feature Store: store features metadata in a central place
  • SageMaker Clarify: compare models, explain model outputs, detect bias
  • SageMaker Ground Truth: RLHF, humans for model grading and data labeling
  • SageMaker Model Cards: ML model documentation
  • SageMaker Model Dashboard: view all your models in one place
  • SageMaker Model Monitor: monitoring and alerts for your model
  • SageMaker Role Manager: access control
  • SageMaker JumpStart: ML model hub & pre-built ML solutions
  • SageMaker Canvas: no-code interface for SageMaker
  • MLFlow on SageMaker: use MLFlow tracking servers on AWS
  • AWS Macie - Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data in AWS.
  • Amazon Inspector - Automated Security Assessments. Only for EC2 instances, Container Images & Lambda functions
  • AWS Artifact - Portal that provides customers with on-demand access to AWS compliance documentation and AWS agreements.