GenAI Bedrock Flashcards
Knowledge Bases for Amazon Bedrock
Gives FMs and agents contextual information from your company’s private data sources for RAG to deliver more relevant; accurate; and customized responses
Amazon Titan Text Express
A Foundation Model (FM) offered by Amazon on Amazon Bedrock
Transformer models
Use a self-attention mechanism and implement contextual embeddings
Amazon Bedrock
Fully managed service that makes foundation models from Amazon and leading AI startups available through an API
Amazon SageMaker JumpStart
Machine learning hub with foundation models; built-in algorithms; and prebuilt ML solutions that you can deploy with just a few clicks
Amazon Titan
Foundation models developed by AWS that are pre-trained on extensive datasets; suitable for a wide range of applications including text and image generation
Foundation Models
Use self-supervised learning to create labels from input data
Fine-tuning
A customization method for FMs that involves further training and does change the weights of your model
Amazon SageMaker Ground Truth
Helps build high-quality training datasets for machine learning models
Amazon SageMaker Model Dashboard
Centralized repository of all models created in your account
Amazon Comprehend Medical
Detects and returns useful information in unstructured clinical text such as physician’s notes; discharge summaries; test results; and case notes
Amazon SageMaker Data Wrangler
Reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes
Amazon SageMaker Canvas
Gives the ability to use machine learning to generate predictions without needing to write any code
Amazon Forecast
Fully managed service that uses statistical and machine learning algorithms to deliver highly accurate time-series forecasts
Amazon Kendra
Highly accurate and easy-to-use enterprise search service that’s powered by machine learning
Amazon Textract
Machine learning service that automatically extracts text; handwriting; layout elements; and data from scanned documents
Amazon Rekognition
Cloud-based image and video analysis service that makes it easy to add advanced computer vision capabilities to your applications
Amazon Polly
Uses deep learning technologies to synthesize natural-sounding human speech
Amazon Transcribe
Automatic speech recognition service that uses machine learning models to convert audio to text
Amazon Translate
Text translation service that uses advanced machine learning technologies to provide high-quality translation on demand
Amazon Lex
Fully managed artificial intelligence service with advanced natural language models to design; build; test; and deploy conversational interfaces in applications
Amazon Connect
AI-powered cloud contact center that automatically detects customer issues and provides agents with contextual customer information
Amazon Personalize
Fully managed machine learning service that uses your data to generate product and content recommendations for your users
Amazon SageMaker Feature Store
Fully managed; purpose-built repository to store; share; and manage features for machine learning models
Amazon SageMaker Clarify
Helps identify potential bias during data preparation without writing code
Amazon Augmented AI (A2I)
Service that makes it easy to build the workflows required for human review of ML predictions
AWS DeepRacer
Autonomous 1/18th scale race car designed to test RL models by racing on a physical track
Reinforcement Learning
Machine learning technique where an agent learns to make decisions through interactions with an environment; receiving feedback in the form of rewards or penalties
Supervised learning
Involves training models with labeled data to make predictions or classify data
Unsupervised learning
Identifies patterns and relationships in unlabeled data
Semi-supervised learning
Applies both supervised and unsupervised learning techniques to a common problem
Deep learning
Subset of machine learning that uses neural networks with many layers to learn from large amounts of data
Convolutional Neural Networks (CNNs)
Type of deep learning model particularly well-suited for processing grid-like data; such as images
Recurrent Neural Networks (RNNs)
Designed to handle sequential data; where the order of the data points matters
K-Means
Unsupervised learning algorithm used for clustering data points into groups
K-Nearest Neighbors (KNN)
Supervised learning algorithm used for classifying data points based on their proximity to labeled examples
Overfitting
Occurs when a model is overly complex and captures noise or random fluctuations in the training data rather than the underlying patterns
Underfitting
Occurs when a model is too simple to capture the underlying patterns in the data
Bias
Error introduced by approximating a real-world problem with a simpler model
Variance
Error introduced by the model’s sensitivity to small fluctuations in the training data
Feature extraction
Reduces the number of features by transforming data into a new space
Feature selection
Reduces the number of features by selecting the most relevant ones from the existing features
Precision
Measures the accuracy of the positive predictions
Recall
Measures the ability of the classifier to identify all positive instances
F1-Score
Harmonic mean of Precision and Recall
Amazon Q Developer
Generative AI-powered assistant that can help you understand; build; extend; and operate AWS applications
Amazon Q Business
Fully managed; generative-AI powered assistant that you can configure to answer questions; provide summaries; generate content; and complete tasks based on your enterprise data
Amazon Q in QuickSight
Generative BI assistant that allows business analysts to use natural language to build BI dashboards in minutes and easily create visualizations and complex calculations
Amazon Q in Connect
Uses real-time conversation with the customer along with relevant company content to automatically recommend what to say or what actions an agent should take to better assist customers
Large Language Models (LLMs)
Used for generating human-like text; translating languages; summarizing text; and answering questions based on large datasets
Generative AI
Type of AI that can create new content and ideas; including conversations; stories; images; videos; and music
Diffusion models
Create new data by iteratively making controlled random changes to an initial data sample
Generative Adversarial Networks (GANs)
Work by training two neural networks in a competitive manner
Variational autoencoders (VAEs)
Learn a compact representation of data called latent space
Prompt engineering
Practice of carefully designing prompts to efficiently tap into the capabilities of FMs
Zero-shot Prompting
Technique used in generative AI where the model is asked to perform a task or generate content without having seen any examples of that specific task during training
Few-shot Prompting
Technique where you provide a few examples of a task to the model to guide its output
Chain-of-thought prompting
Technique that breaks down a complex question into smaller; logical parts that mimic a train of thought
Negative prompting
Technique used to guide a generative AI model to avoid certain outputs or behaviors when generating content
Retrieval Augmented Generation (RAG)
Process of optimizing the output of a large language model by referencing an authoritative knowledge base outside of its training data sources before generating a response
Agents for Amazon Bedrock
Fully managed capabilities that make it easier for developers to create generative AI-based applications that can complete complex tasks for a wide range of use cases
