AWS offers over 25 distinct machine learning services, ranging from fully managed platforms like SageMaker to pre-built AI APIs that require zero ML expertise. This abundance of choice creates a paradox: teams often spend months evaluating options instead of building solutions, or worse, select the wrong service and face costly migrations later.
123456789101112import boto3 import json bedrock_runtime = boto3.client( service_name='bedrock-runtime', region_name='us-east-1' ) def invoke_claude_streaming(prompt: str, max_tokens: int = 1000): """Stream responses from Claude for better user experience""" body = json.dumps({ "anthropic_version": "bedrock-2023-05-31",
123456789101112from abc import ABC, abstractmethod from typing import Any, Dict, Optional import boto3 import json class MLServiceProvider(ABC): @abstractmethod def predict(self, input_data: Any) -> Dict: pass @abstractmethod def get_cost_estimate(self, request_count: int) -> float:
123456789101112import boto3 import json from datetime import datetime class BatchInferencePipeline: def __init__(self): self.s3 = boto3.client('s3') self.sagemaker = boto3.client('sagemaker') self.batch_bucket = 'ml-batch-inference' def create_batch_transform_job(self, model_name: str,
123456789101112import boto3 import time from dataclasses import dataclass from typing import Optional, List import logging logger = logging.getLogger(__name__) @dataclass class ServiceHealth: service_name: str is_healthy: bool
123456789101112import boto3 import json import hashlib from functools import lru_cache from typing import Optional import time class ProductionBedrockClient: def __init__(self, model_id: str = 'anthropic.claude-3-sonnet-20240229-v1:0', cache_ttl: int = 3600, max_retries: int = 3):