AWS

AWS Services

  • AWS AI Service Cards: documentation for AWS AI services
  • AWS Artifact: provides on-demand access to security and compliance documentation for the AWS Cloud
  • AWS CloudTrail: logs AWS service activity (including API calls)
  • AWS Config: overview of AWS resource configurations
  • AWS DeepRacer: teaches reinforcement learning in an interactive, virtual environment
  • AWS Glue DataBrew: visual data preparation tool
  • AWS Glue: integrate data from multiple sources
  • AWS Identity and Access Management (IAM): control access to AWS resources
  • AWS Inferentia: accelerator that delivers high performance in Amazon EC2
  • AWS Key Management Service (KMS): store and manage cryptographic keys
  • AWS Lambda: serverless compute service
  • AWS Secrets Manager: manage and maintain credentials
  • AWS Trusted Advisor: provide recommendations to improve account (e.g. cost savings)

Amazon Services

  • Amazon Augmented AI (A2I): human review of ML predictions
  • Amazon Bedrock Agents: autonomous agents to orchestrate interactions between FMs, data sources etc.
  • Amazon Bedrock Guardrails: manage generative AI applications (filter topics, add safeguard to models)
  • Amazon Bedrock: provides access to FMs (ready to use, limited customization)
  • Amazon CloudWatch: logs for applications on AWS (NO API calls)
  • Amazon Comprehend: extracts documents insights (key phrases, sentiment)
  • Amazon DocumentDB: fully managed, MongoDB compatible, JSON document database, supports real-time vector search with low latency
  • Amazon DynamoDB: NoSQL key-value database
  • Amazon EC2: secure, resizable computational service
  • Amazon Elastic Container Registry (ECR): fully managed repository for container images
  • Amazon Inspector: vulnerability management service
  • Amazon Kendra: semantic & contextual search
  • Amazon Lex: build chatbots
  • Amazon Macie: discover, monitor, and protect sensitive data in Amazon S3
  • Amazon Mechanical Turk (MTurk): allow businesses to hire remote workers for manual tasks (e.g. data labeling)
  • Amazon OpenSearch Service: fully managed service for OpenSearch on AWS with vector store & similarity search
  • Amazon Personalize: generates user recommendations
  • Amazon Polly: convert text to speech
  • Amazon Q Business: fully managed AI-assistant
  • Amazon Q Developer: AI assistant for AWS applications
  • Amazon Q: generative AI assistant to answer business questions
  • Amazon QuickSight: BI and reporting tool to build reports and dashboards
  • Amazon RDS for Oracle: relational database with Oracle support
  • Amazon Redshift: fully managed SQL database
  • Amazon Rekognition: analyze visual content (image and video)
  • Amazon S3: object storage service
  • Amazon SageMaker Autopilot: automates ML model creation, training, and tuning
  • Amazon SageMaker Canvas: build ML models without writing code
  • Amazon SageMaker Clarify: explain model responses, detect bias
  • Amazon SageMaker Data Wrangler: import, cleanse, analyze, and transform data
  • Amazon SageMaker Feature Store: create, store, share, and manage features
  • Amazon SageMaker Ground Truth: label data for ML model training
  • Amazon SageMaker JumpStart: provides access to FMs (more control, need deployment)
  • Amazon SageMaker Model Cards: documentation for models (intended use, evaluation metrics)
  • Amazon SageMaker Model Monitor: alerts when model quality changes (data drift)
  • Amazon SageMaker Model Registry: fully managed catalog for ML models
  • Amazon SageMaker Pipelines: create MLOps workflows
  • Amazon SageMaker Studio: suite of IDEs (RStudio, VSCode)
  • Amazon SageMaker: collection of tools to train and deploy ML models on AWS
  • Amazon Textract: extract text from images and PDFs
  • Amazon Titan: family of FMs in Amazon Bedrock
  • Amazon Transcribe: convert speech to text
  • Amazon Translate: language translation service
  • PartyRock: Amazon Bedrock Playground to build AI-generated apps

Terms & Definitions

  • accuracy: %ge of correct predictions (ratio of true positives (TP) and true negatives (TN) to the total predictions)
  • anomaly detection: unsupervised learning algorithm to identify anomalies (Random Cut Forest - RCF)
  • asynchronous inference: useful for large data without immediate response
  • attention mechanism: help the model focus on relevant parts of the input when generating text
  • BERTScore: measure similarity between chatbot and human responses
  • bias: low bias = model is not making erroneous assumptions about the training data
  • chain of thought: breaks down a complex question into smaller parts
  • classification: supervised learning algorithm to categorize data (binary/multiclass/image)
  • clustering: unsupervised ML method to group similar objects (k-means)
  • computer vision: field of AI to interpret and understand visual data
  • context window: # tokens model can accept in the context
  • continued pre-training: provide unlabeled data to FM to improve domain knowledge
  • cross-validation: data preparation technique for model training
  • diffusion model: AI model to generate image from prompt
  • domain adaptation fine-tuning: fine-tune FM with domain-specific information
  • embeddings: numerical representations of words
  • explainability: ability to understand how a model arrives at a prediction
  • extract, transform, load (ETL): combines data from multiple sources into single data set
  • F1 score: metric to evaluate classification models: 2 * precision * recall / (precision + recall)
  • fairness: impartial and just treatment without discrimination
  • feature engineering: selecting and transforming data model training
  • few-shot learning: make predictions using a few examples in prompt
  • fine-tuning: improves model’s performance using labeled data
  • forecasting: forecast 1D time series data (DeepAR)
  • foundational model (FM): broader then LLM, can handle various data types
  • gateway endpoint: secure connection from a VPC to Amazon S3/DynamoDB
  • generative AI security scoping matrix: framework to classify generative AI use cases (ownership)
  • generative AI: AI to create new content (images, text, music, or conversations)
  • hallucination: false information generated by LLM
  • image_uri: Docker image URI in Amazon SageMaker AI
  • in-context learning: add instructions & examples inside prompt
  • inference_instances: list of inference instances for a model deployed in Amazon SageMaker AI
  • inference: model prediction
  • instruction-based fine-tuning: use labeled examples (prompt, response pairs) to improve FM on a specific task
  • knowledge cutoff: data limitation due to LLMs pre-trained on static datasets
  • large language model (LLM): focus on text & language tasks
  • machine learning (ML): train models to make predictions based on existing data
  • mean absolute error (MAE): mean of the absolute differences between the actual values and the predicted values
  • mean absolute percentage error (MAPE): mean of the absolute differences between the actual values and the predicted values, divided by the actual values
  • mean squared error (MSE): squared difference between the predicted and actual values
  • multimodal model: can understand data from multiple modalities
  • natural language processing (NLP): ML technology to interpret, manipulate, and comprehend human language
  • overfitting: good model performance on training data but not on new data
  • perplexity: probability of a model to generate a given sequence of words
  • personally identifiable information (PII): data that can identify an individual
  • precision (positive predicted value PPV): TP / (TP + FP)
  • prompt engineering: optimize FM inputs to generate better responses
  • prompt injection attacks: ignoring the prompt template, exploiting friendliness, prompting persona switches
  • prompt template: predefined format to standardize inputs and outputs
  • recall (true positive rate TPR): TP / (TP + FN)
  • regression: predict output based on input
  • reinforcement learning from human feedback (RLHF): use human feedback to train ML model
  • retrieval augmented generation (RAG): technique to let LLM use information from external knowledge base
  • ROUGE-N: measure similarity between generated and reference summary
  • S3 bucket policy: grants access to S3 data
  • semi-structured data: e.g. nested .json file, hierarchically organized .xml file
  • semi-supervised learning: combines a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning is useful to train models to recognize different driving scenarios that might not be fully labeled.
  • serverless inference: useful for near-real time requests with idle periods & cold starts
  • single-shot prompt engineering: provide single example
  • specificity (true negative rate TNR): TN / (TN + FP)
  • stop sequences: inference parameter to interrupt text generation
  • structured data: tabular with rows and columns (e.g. csv)
  • supervised learning: train models on labeled data set
  • temperature: control randomnes of response (higher temperature -> more random)
  • token: sequence of characters used by model to predict single unit of meaning
  • tokenization: splitting input text into individual words or subword units
  • top K: # of most-likely candidates considered for next token.
  • top P: %ge of most-likely candidates considered for next token
  • underfitting: model too simple to capture underlying data patterns
  • unstructured data: plain text file with no order
  • unsupervised learning: detect anomalies or unusual patterns
  • variance: high variance = model is paying attention to noise in the training data and is overfitting
  • vector database: efficiently store and manage high-dimensional data
  • zero-shot learning: make predictions without examples