--- name: aws-ai description: AWS AI/ML services — Bedrock, SageMaker, Comprehend, Rekognition, Textract, Lex, Polly, Transcribe, Kendra, Q Developer license: MIT version: 1.0.0 updated: 2026-04-24 allowed-tools: - shell - read_file - write_file - glob - grep --- # AWS AI/ML Services — Comprehensive Reference ## Amazon Bedrock ### Model Access ```bash # List available foundation models aws bedrock list-foundation-models \ --query 'modelSummaries[].[modelId,providerName,modelName]' \ --output table --region us-east-1 # Request access to a model (done via console, but can check) aws bedrock get-foundation-model --model-identifier anthropic.claude-3-5-sonnet-20241022-v2:0 # List models you have access to aws bedrock list-foundation-models \ --by-inference-type ON_DEMAND \ --query 'modelSummaries[].modelId' --output text ``` ### Invoke Model (Converse API — preferred) ```python import boto3, json bedrock = boto3.client('bedrock-runtime', region_name='us-east-1') response = bedrock.converse( modelId='anthropic.claude-3-5-sonnet-20241022-v2:0', messages=[{'role': 'user', 'content': [{'text': 'Explain VPCs in 3 sentences.'}]}], system=[{'text': 'You are a helpful AWS expert.'}], inferenceConfig={'maxTokens': 512, 'temperature': 0.7, 'topP': 0.9}, ) print(response['output']['message']['content'][0]['text']) print(f"Input tokens: {response['usage']['inputTokens']}, Output: {response['usage']['outputTokens']}") ``` ### Streaming ```python response = bedrock.converse_stream( modelId='anthropic.claude-3-5-sonnet-20241022-v2:0', messages=[{'role': 'user', 'content': [{'text': 'Write a Python quicksort.'}]}], ) for event in response['stream']: if 'contentBlockDelta' in event: print(event['contentBlockDelta']['delta']['text'], end='', flush=True) ``` ### CLI Invocation ```bash aws bedrock-runtime converse \ --model-id anthropic.claude-3-haiku-20240307-v1:0 \ --messages '[{"role":"user","content":[{"text":"What is S3?"}]}]' \ --query 'output.message.content[0].text' --output text ``` ### Bedrock Agents ```bash # Create agent aws bedrock create-agent \ --agent-name my-ops-agent \ --foundation-model anthropic.claude-3-5-sonnet-20241022-v2:0 \ --instruction "You are an AWS operations assistant. Help users manage their cloud resources." \ --agent-resource-role-arn arn:aws:iam::123:role/BedrockAgentRole # Create action group (connects agent to Lambda) aws bedrock create-agent-action-group \ --agent-id --agent-version DRAFT \ --action-group-name AwsOps \ --action-group-executor lambda=arn:aws:lambda:us-east-1:123:function:aws-ops \ --api-schema payload=file://openapi-schema.json # Prepare and invoke aws bedrock prepare-agent --agent-id aws bedrock-runtime invoke-agent \ --agent-id --agent-alias-id TSTALIASID \ --session-id sess-$(date +%s) \ --input-text "List all running EC2 instances in us-east-1" \ outfile.txt ``` ### Knowledge Bases (RAG) ```bash # Create knowledge base with S3 data source aws bedrock create-knowledge-base \ --name prod-kb \ --role-arn arn:aws:iam::123:role/BedrockKBRole \ --knowledge-base-configuration type=VECTOR,vectorKnowledgeBaseConfiguration={embeddingModelArn=arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0} \ --storage-configuration type=OPENSEARCH_SERVERLESS,opensearchServerlessConfiguration={...} # Sync data source aws bedrock start-ingestion-job --knowledge-base-id --data-source-id # Retrieve + generate aws bedrock-agent-runtime retrieve-and-generate \ --input text="What is our incident response process?" \ --retrieve-and-generate-configuration '{ "type": "KNOWLEDGE_BASE", "knowledgeBaseConfiguration": { "knowledgeBaseId": "", "modelArn": "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-haiku-20240307-v1:0" } }' \ --query 'output.text' --output text ``` ### Bedrock Guardrails ```bash aws bedrock create-guardrail \ --name content-filter \ --content-policy-config '{ "filtersConfig": [ {"type": "SEXUAL", "inputStrength": "HIGH", "outputStrength": "HIGH"}, {"type": "VIOLENCE", "inputStrength": "MEDIUM", "outputStrength": "MEDIUM"}, {"type": "HATE", "inputStrength": "HIGH", "outputStrength": "HIGH"} ] }' \ --topic-policy-config '{ "topicsConfig": [{ "name": "FinancialAdvice", "definition": "Providing specific investment or financial advice", "type": "DENY" }] }' \ --word-policy-config '{"wordsConfig": [{"text": "competitor"}], "managedWordListsConfig": [{"type": "PROFANITY"}]}' ``` --- ## SageMaker ```bash # Create notebook instance aws sagemaker create-notebook-instance \ --notebook-instance-name ml-experiments \ --instance-type ml.t3.medium \ --role-arn arn:aws:iam::123:role/SageMakerRole \ --default-code-repository https://github.com/myorg/ml-notebooks # Training job aws sagemaker create-training-job \ --training-job-name my-training-$(date +%s) \ --algorithm-specification TrainingImage=763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.1.0-gpu-py310,TrainingInputMode=File \ --role-arn arn:aws:iam::123:role/SageMakerRole \ --input-data-config '[{"ChannelName":"training","DataSource":{"S3DataSource":{"S3Uri":"s3://my-bucket/data/train","S3DataType":"S3Prefix"}}}]' \ --output-data-config S3OutputPath=s3://my-bucket/models/ \ --resource-config InstanceType=ml.p3.2xlarge,InstanceCount=1,VolumeSizeInGB=50 \ --stopping-condition MaxRuntimeInSeconds=3600 \ --hyper-parameters lr=0.001,epochs=10,batch_size=32 # Wait for training aws sagemaker wait training-job-completed-or-stopped \ --training-job-name my-training-xxx # Deploy endpoint aws sagemaker create-model \ --model-name my-model-v1 \ --primary-container Image=763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:2.1.0-cpu-py310,ModelDataUrl=s3://my-bucket/models/output/model.tar.gz \ --execution-role-arn arn:aws:iam::123:role/SageMakerRole aws sagemaker create-endpoint-config \ --endpoint-config-name my-model-v1-config \ --production-variants Name=primary,ModelName=my-model-v1,InstanceType=ml.m5.xlarge,InitialInstanceCount=1 aws sagemaker create-endpoint \ --endpoint-name my-model-endpoint \ --endpoint-config-name my-model-v1-config # Invoke endpoint aws sagemaker-runtime invoke-endpoint \ --endpoint-name my-model-endpoint \ --content-type application/json \ --body '{"inputs": "classify this text"}' \ /tmp/prediction.json && cat /tmp/prediction.json ``` --- ## Amazon Comprehend ```python import boto3 comprehend = boto3.client('comprehend', region_name='us-east-1') text = "AWS is headquartered in Seattle. Jeff Bezos founded Amazon in 1994." # Detect entities response = comprehend.detect_entities(Text=text, LanguageCode='en') for entity in response['Entities']: print(f"{entity['Type']}: {entity['Text']} ({entity['Score']:.2f})") # Detect sentiment sentiment = comprehend.detect_sentiment(Text=text, LanguageCode='en') print(sentiment['Sentiment'], sentiment['SentimentScore']) # Key phrases phrases = comprehend.detect_key_phrases(Text=text, LanguageCode='en') # PII detection pii = comprehend.detect_pii_entities(Text="SSN: 123-45-6789, Email: user@example.com", LanguageCode='en') ``` --- ## Amazon Rekognition ```python import boto3 rekognition = boto3.client('rekognition') # Detect labels with open('image.jpg', 'rb') as f: response = rekognition.detect_labels( Image={'Bytes': f.read()}, MaxLabels=10, MinConfidence=80 ) for label in response['Labels']: print(f"{label['Name']}: {label['Confidence']:.1f}%") # Face detection response = rekognition.detect_faces( Image={'S3Object': {'Bucket': 'my-bucket', 'Name': 'face.jpg'}}, Attributes=['ALL'] ) # Content moderation response = rekognition.detect_moderation_labels( Image={'S3Object': {'Bucket': 'my-bucket', 'Name': 'photo.jpg'}}, MinConfidence=60 ) # Text in image (OCR) response = rekognition.detect_text( Image={'S3Object': {'Bucket': 'my-bucket', 'Name': 'sign.jpg'}} ) ``` --- ## Amazon Textract (Document OCR + Structure) ```python import boto3 textract = boto3.client('textract') # Synchronous (single page / small doc) with open('form.pdf', 'rb') as f: response = textract.analyze_document( Document={'Bytes': f.read()}, FeatureTypes=['FORMS', 'TABLES'] ) # Async (multi-page PDFs) response = textract.start_document_analysis( DocumentLocation={'S3Object': {'Bucket': 'my-bucket', 'Name': 'report.pdf'}}, FeatureTypes=['FORMS', 'TABLES'], NotificationChannel={'SNSTopicArn': '...', 'RoleArn': '...'}, OutputConfig={'S3Bucket': 'my-results-bucket'} ) job_id = response['JobId'] ``` --- ## Amazon Transcribe ```python import boto3, time transcribe = boto3.client('transcribe') # Start job transcribe.start_transcription_job( TranscriptionJobName='meeting-2024-01-15', Media={'MediaFileUri': 's3://my-bucket/audio/meeting.mp4'}, MediaFormat='mp4', LanguageCode='en-US', Settings={'ShowSpeakerLabels': True, 'MaxSpeakerLabels': 4} ) # Poll for completion while True: status = transcribe.get_transcription_job(TranscriptionJobName='meeting-2024-01-15') if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break time.sleep(10) print(status['TranscriptionJob']['Transcript']['TranscriptFileUri']) ``` --- ## Amazon Polly (Text to Speech) ```python import boto3 polly = boto3.client('polly') response = polly.synthesize_speech( Text='Hello! Welcome to AWS.', TextType='ssml', OutputFormat='mp3', VoiceId='Joanna', Engine='neural' # neural = higher quality ) with open('output.mp3', 'wb') as f: f.write(response['AudioStream'].read()) ``` --- ## Amazon Kendra ```bash # Create index aws kendra create-index \ --name prod-search \ --edition DEVELOPER_EDITION \ --role-arn arn:aws:iam::123:role/KendraRole # Add S3 data source aws kendra create-data-source \ --index-id \ --name docs-s3 \ --type S3 \ --configuration S3Configuration={BucketName=my-docs-bucket} \ --role-arn arn:aws:iam::123:role/KendraS3Role aws kendra start-data-source-sync-job --index-id --id # Query aws kendra query \ --index-id \ --query-text "How do I configure VPC peering?" \ --query 'ResultItems[].[DocumentTitle.Text,DocumentExcerpt.Text]' --output table ``` --- ## Amazon Q Developer Q Developer is an AI coding assistant integrated into IDEs and CLI. It uses your codebase context. ```bash # Install Q CLI pip install amazon-q-developer-cli # or via AWS Toolkit in VS Code / JetBrains # CLI usage q chat "explain this CloudFormation template" q chat --file template.yaml "what security issues does this have?" # Inline code suggestions: available in VS Code / JetBrains via AWS Toolkit extension # Enable: Settings → AWS → Amazon Q → Enable Q Developer # Q for command line (chat in terminal) q --context . "write a Python Lambda that reads from SQS and writes to DynamoDB" ``` --- ## Guardrails & Gotchas - **Bedrock model availability** — not all models available in all regions. Check console for your region. Cross-region inference available via inference profiles. - **Bedrock costs** — on-demand pricing per token. High-volume workloads should use Provisioned Throughput (commit to model units/month). - **Bedrock Agents Lambda** — your Lambda must return a specific response schema matching the OpenAPI spec. Test with `InvokeAgent` before wiring to prod. - **SageMaker endpoints never scale to zero** — use Serverless Inference (cold start) or Lambda + batch transform for sporadic workloads. - **Rekognition streaming** — video analysis uses Kinesis Video Streams, not S3. Different API path. - **Textract limits** — sync API max 10MB/3000 pages; use async + S3 for anything bigger. - **Comprehend batch** — single API calls max 25 documents. Use `start-entities-detection-job` for bulk. - **Knowledge Base embeddings** — changing the embedding model requires re-ingesting all documents. Plan ahead. - **Q Developer data privacy** — by default, Q may use your code to improve models. Enable "professional tier" for enterprise data privacy guarantees.