Cost Optimization

123456789101112
import boto3
from datetime import datetime, timedelta

def lambda_handler(event, context):
    ce = boto3.client('ce')
    sns = boto3.client('sns')
    
    # Get costs for last 7 days and previous 7 days
    end = datetime.now().strftime('%Y-%m-%d')
    start = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
    prev_start = (datetime.now() - timedelta(days=14)).strftime('%Y-%m-%d')
    

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{
  "lambdaARN": "arn:aws:lambda:us-east-1:123456789:function:ml-inference",
  "powerValues": [256, 512, 1024, 1536, 2048, 3008, 4096, 6144, 8192, 10240],
  "num": 100,
  "payload": {
    "model_id": "sentiment-classifier-v2",
    "text": "Sample input text for inference benchmarking",
    "return_probabilities": true
  },
  "parallelInvocation": true,
  "strategy": "cost",
  "balancedWeight": 0.5,

123456789101112
import boto3
from datetime import datetime

def apply_ml_cost_tags(resource_arn: str, context: dict) -> dict:
    """
    Apply standardized cost allocation tags to ML resources.
    Call this when creating any Lambda, SageMaker, or supporting resource.
    """
    
    required_tags = {
        # Business allocation
        'business-unit': context['business_unit'],  # e.g., 'search', 'recommendations'

123456789101112
import boto3
import json
from datetime import datetime, timedelta
from decimal import Decimal

def lambda_handler(event, context):
    ce = boto3.client('ce')
    sns = boto3.client('sns')
    
    # Get costs for last 7 days and previous 7 days
    end_date = datetime.now().strftime('%Y-%m-%d')
    start_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')

123456789101112
import boto3
import json
from datetime import datetime
import pytz

def lambda_handler(event, context):
    """
    Manages SageMaker endpoint schedules based on tags.
    Tag endpoints with:
    - Schedule: business-hours | always-on | custom
    - Timezone: US/Pacific | US/Eastern | UTC
    - CustomSchedule: {"start": "08:00", "end": "20:00", "days": [0,1,2,3,4]}

123456789101112
import boto3
import json
import hashlib
from functools import lru_cache
import redis

class CostOptimizedModelRouter:
    def __init__(self):
        self.sm_runtime = boto3.client('sagemaker-runtime')
        self.redis_client = redis.Redis(
            host='ml-cache.xxxxx.cache.amazonaws.com',
            port=6379,