Why is my SageMaker training job stuck in "InProgress" for 3 hours with no logs?

Usually means your training script crashed immediately but SageMaker doesn't know it yet. Classic AWS - lying to you about what's actually happening.Check CloudWatch logs:```bashaws logs describe-log-groups --log-group-name-prefix /aws/sagemaker/TrainingJobsaws logs get-log-events --log-group-name /aws/sagemaker/TrainingJobs/your-job --log-stream-name your-stream```90% of the time it's some bullshit like:- Missing Python dependencies in your container- Entry point script path is wrong- IAM role can't access S3 (always the IAM)Quick fix: Add MaxRuntimeInSeconds to kill stuck jobs:```pythonStoppingCondition={'MaxRuntimeInSeconds': 3600} # 1 hour max```

Bedrock is throwing 500 errors but worked fine yesterday. What changed?

First, check if AWS is having issues: https://status.aws.amazon.com/If AWS status is green, it's probably you hit a quota limit. Could also be regional model availability changed, prompts too long, or IAM permissions expired. Always check quotas first.Emergency debug:```python# Test with minimal requestresponse = bedrock.invoke_model( modelId='anthropic.claude-3-haiku-20240307-v1:0', # Cheapest model body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 10, "messages": [{"role": "user", "content": "hi"}] }))```If this fails, it's infrastructure. If it works, your original request is the problem.

My endpoint deployed successfully but returns ModelError on every request. How do I debug this?

Your inference code is broken. The endpoint deployed because the container started, but your model loading/prediction code has bugs.Check CloudWatch logs for the endpoint:```bashaws logs filter-log-events \ --log-group-name /aws/sagemaker/Endpoints/your-endpoint \ --start-time $(date -d '1 hour ago' +%s)000 \ --filter-pattern "ERROR"```Common issues:- Missing model files in /opt/ml/model/- Wrong Python version or missing dependencies- Model expects different input format- Memory issues loading large modelsTest with minimal payload:```pythonimport jsontest_payload = {"data": "test"}response = sagemaker_runtime.invoke_endpoint( EndpointName='your-endpoint', ContentType='application/json', Body=json.dumps(test_payload))```

How do I check if I'm hitting AWS service quotas?

```pythonimport boto3service_quotas = boto3.client('service-quotas')# Common AI/ML quotas that cause problemsquotas_to_check = [ ('sagemaker', 'L-1194D53C'), # ml.p3.2xlarge instances ('sagemaker', 'L-888C8DB6'), # Real-time endpoints ('bedrock', 'L-22C574D0'), # Claude requests per minute]for service, quota_code in quotas_to_check: try: response = service_quotas.get_service_quota( ServiceCode=service, QuotaCode=quota_code ) quota = response['Quota'] print(f"{quota['QuotaName']}: {quota['Value']} {quota.get('Unit', '')}") except Exception as e: print(f"Error checking {service}/{quota_code}: {e}")```

My multi-model endpoint randomly fails with OutOfMemoryError. Which model is causing it?

Multi-model endpoints share memory across all models. When one model uses too much RAM, others get killed.Debug by checking model sizes:```python# In your inference.py, add memory profilingimport psutilimport osdef model_fn(model_dir): model = load_your_model(model_dir) # Log memory usage after loading process = psutil.Process(os.getpid()) memory_mb = process.memory_info().rss / 1024 / 1024 print(f"Model loaded. Memory usage: {memory_mb:.1f} MB") return model```Quick fixes:- Reduce MaxModels in MultiModelConfig (limit concurrent models)- Use larger instance types (more RAM)- Move biggest models to separate endpoints- Add model unloading logic for unused models

IAM permissions are fucked and I don't know which policy is wrong. Help.

Use the IAM policy simulator to test specific actions:```bashaws iam simulate-principal-policy \ --policy-source-arn arn:aws:iam::123456789012:role/YourRole \ --action-names sagemaker:InvokeEndpoint \ --resource-arns arn:aws:sagemaker:us-east-1:123456789012:endpoint/your-endpoint```For emergency debugging, temporarily attach this overly permissive policy:```json{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": "*", "Resource": "*" }]}```If it works with this policy, the issue is permissions. Gradually restrict until you find the missing permission.**Never leave the * policy in production.**

My Bedrock requests work in us-east-1 but fail in eu-west-1. Same exact code.

Not all models are available in all regions. Check model availability:```pythondef check_model_availability(model_id, region): bedrock = boto3.client('bedrock', region_name=region) try: response = bedrock.list_foundation_models() available = [m['modelId'] for m in response['modelSummaries']] return model_id in available except: return Falsemodel = 'anthropic.claude-3-5-sonnet-20240620-v1:0'regions = ['us-east-1', 'us-west-2', 'eu-west-1']for region in regions: available = check_model_availability(model, region) print(f"{model} in {region}: {'Available' if available else 'NOT AVAILABLE'}")```Fallback strategy:```pythondef bedrock_with_fallback(prompt, preferred_regions=['us-east-1', 'us-west-2']): for region in preferred_regions: try: bedrock = boto3.client('bedrock-runtime', region_name=region) response = bedrock.invoke_model(...) return response except Exception as e: print(f"Failed in {region}: {e}") continue raise Exception("All regions failed")```

My endpoint auto-scaling isn't working. Traffic spikes and users get 503 errors.

Auto-scaling takes 3-5 minutes to spin up new instances. By then, users have already left.Fix the scaling policy:```python# Scale out faster, scale in slowersagemaker.put_scaling_policy( ResourceId='endpoint/your-endpoint/variant/your-variant', ScalableDimension='sagemaker:variant:DesiredInstanceCount', PolicyType='TargetTrackingScaling', TargetTrackingScalingPolicyConfiguration={ 'TargetValue': 50.0, # Scale at 50% CPU instead of 80% 'ScaleOutCooldown': 60, # Scale out after 1 minute 'ScaleInCooldown': 900, # Scale in after 15 minutes 'PredefinedMetricSpecification': { 'PredefinedMetricType': 'SageMakerVariantInvocationsPerInstance' } })```Or maintain minimum capacity:```pythonsagemaker.register_scalable_target( ResourceId='endpoint/your-endpoint/variant/your-variant', ScalableDimension='sagemaker:variant:DesiredInstanceCount', MinCapacity=2, # Always keep 2 instances running MaxCapacity=10)```

Batch transform job processes first 1000 files fine then starts failing randomly. Why?

Usually one of these:1. **Inconsistent file formats** - file #1001 has different columns than files 1-10002. **Memory leak** - your inference code accumulates memory over time3. **Large files** - some files are much bigger and cause timeouts4. **Encoding issues** - random file has weird charactersDebug by processing files individually:```python# Find the problem filedef debug_failed_files(input_s3_path, output_s3_path): # List all input files input_files = list_s3_files(input_s3_path) # List successful outputs output_files = list_s3_files(output_s3_path) successful_files = [f.replace('.out', '') for f in output_files] # Find missing files failed_files = [f for f in input_files if f not in successful_files] print(f"Failed files: {failed_files}") # Test each failed file individually for failed_file in failed_files[:5]: # Test first 5 try: # Download and inspect obj = s3.get_object(Bucket=bucket, Key=failed_file) content = obj['Body'].read() print(f"{failed_file}: {len(content)} bytes") # Check if it's valid JSON/CSV/whatever format you expect validate_file_format(content) except Exception as e: print(f"Problem with {failed_file}: {e}")```

My VPC-enabled SageMaker training job can't access S3 and keeps timing out.

VPC mode blocks internet access by default. You need VPC endpoints or a NAT gateway.Quick check:```bash# Do you have S3 VPC endpoint?aws ec2 describe-vpc-endpoints \ --filters "Name=service-name,Values=com.amazonaws.us-east-1.s3"# Do you have SageMaker API endpoint? aws ec2 describe-vpc-endpoints \ --filters "Name=service-name,Values=com.amazonaws.us-east-1.sagemaker.api"```Emergency fix (create S3 VPC endpoint):```bashaws ec2 create-vpc-endpoint \ --vpc-id vpc-12345678 \ --service-name com.amazonaws.us-east-1.s3 \ --route-table-ids rtb-12345678```Or use NAT gateway (costs more but simpler):```bash# Your private subnets need routes to NAT gatewayaws ec2 create-route \ --route-table-id rtb-private \ --destination-cidr-block 0.0.0.0/0 \ --nat-gateway-id nat-12345678```

How do I know if my AWS bill spike is from a runaway job vs normal usage?

Check CloudWatch billing metrics by service:```pythonimport boto3from datetime import datetime, timedeltacloudwatch = boto3.client('cloudwatch')# Get costs by service for last 24 hoursservices = ['SageMaker', 'Bedrock', 'EC2-Instance', 'S3']for service in services: response = cloudwatch.get_metric_statistics( Namespace='AWS/Billing', MetricName='EstimatedCharges', Dimensions=[ {'Name': 'ServiceName', 'Value': service}, {'Name': 'Currency', 'Value': 'USD'} ], StartTime=datetime.utcnow() - timedelta(days=1), EndTime=datetime.utcnow(), Period=3600, # 1 hour intervals Statistics=['Maximum'] ) if response['Datapoints']: latest_cost = response['Datapoints'][-1]['Maximum'] print(f"{service}: ${latest_cost:.2f}")```Look for sudden spikes. If SageMaker cost jumped from $10 to $500 overnight, you probably left a training job or endpoint running.Find expensive resources:```bash# Running training jobsaws sagemaker list-training-jobs --status-equals InProgress# Active endpoints aws sagemaker list-endpoints --status-equals InService# EC2 instances (check for accidentally created ones)aws ec2 describe-instances --filters "Name=instance-state-name,Values=running"```

Everything was working fine until I updated my IAM policy. Now nothing works. How do I rollback IAM changes?

Check CloudTrail for recent IAM changes:```bashaws cloudtrail lookup-events \ --lookup-attributes AttributeKey=EventName,AttributeValue=AttachRolePolicy \ --start-time $(date -d '24 hours ago' +%s) \ --end-time $(date +%s)```See what policies were modified:```bashaws cloudtrail lookup-events \ --lookup-attributes AttributeKey=EventName,AttributeValue=PutRolePolicy \ --start-time $(date -d '24 hours ago' +%s)```Rollback process:1. **Find the previous policy version** in CloudTrail events2. **Revert to working policy** (save the broken one first)3. **Test immediately** with a simple API callFor managed policies, check version history:```bashaws iam list-policy-versions --policy-arn your-policy-arnaws iam set-default-policy-version --policy-arn your-policy-arn --version-id v1```

My model accuracy suddenly dropped in production but works fine in development. What's different?

This is usually data distribution shift - your production data looks different than your training data.Quick checks:1. **Input preprocessing** - are you scaling/normalizing the same way?2. **Data types** - int vs float can break models silently3. **Missing features** - production might be missing columns your model expects4. **Encoding differences** - UTF-8 vs ASCII issues5. **Time zones** - datetime features can shift between environmentsDebug with data profiling:```python# Compare production inputs to training datadef profile_input_data(production_sample): print("Production data profile:") print(f"Shape: {production_sample.shape}") print(f"Data types: {production_sample.dtypes}") print(f"Missing values: {production_sample.isnull().sum()}") print(f"Numeric ranges: {production_sample.describe()}") # Compare to your training data stats # Flag significant differences```

My Lambda function calling Bedrock works locally but times out in AWS. Why?

Lambda cold starts + Bedrock cold starts = disaster. Your function is timing out waiting for the model to warm up.Solutions:1. **Increase Lambda timeout** to 5-10 minutes (max)2. **Use provisioned concurrency** for Lambda (costs more)3. **Keep Bedrock models warm** with scheduled pings4. **Add retry logic** with exponential backoff```pythonimport timedef lambda_handler(event, context): max_retries = 3 for attempt in range(max_retries): try: response = bedrock.invoke_model(...) return response except Exception as e: if attempt < max_retries - 1: wait_time = 2 ** attempt # 1, 2, 4 seconds time.sleep(wait_time) continue raise e```Also check Lambda memory allocation - Bedrock SDK needs decent memory (512MB+).

How do I get AWS support to actually help with AI/ML issues instead of sending me documentation links?

1. **Upgrade to Business or Enterprise support** - Basic support is useless for production issues2. **Provide specific error messages and AWS request IDs** from CloudTrail3. **Include exact reproduction steps** with code samples4. **Mention business impact** ("affecting 50,000 users" gets faster response)5. **Use severity levels correctly** - don't cry wolf with "urgent" for everythingFor faster help:- AWS ML community Slack- Stack Overflow with aws-sagemaker or amazon-bedrock tags- GitHub issues on aws-samples repositoriesAWS support is good for account limits and billing issues. For technical problems, the community often has better answers.

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AWS AI/ML Production Debugging: AI-Optimized Reference

Critical Failure Patterns

SageMaker Training Job Failures

UnexpectedStatusException Pattern

Primary Cause (90%): IAM role lacks S3 access permissions
Failure Impact: Training jobs fail silently with cryptic error messages
Detection Time: Can waste hours before proper logs are found
Fix Complexity: Low (10 minutes) if IAM issue, High (2+ hours) if VPC/networking

Critical IAM Policy Requirements:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject", "s3:PutObject", "s3:DeleteObject", "s3:ListBucket",
                "logs:CreateLogGroup", "logs:CreateLogStream", "logs:PutLogEvents"
            ],
            "Resource": ["arn:aws:s3:::your-bucket/*", "arn:aws:s3:::your-bucket", "*"]
        }
    ]
}

Training Jobs Stuck "InProgress"

Root Causes: Spot instance termination (60%), S3 cross-region access (25%), Docker container failure (15%)
Cost Impact: Can burn hundreds of dollars before detection
Emergency Fix: Add MaxRuntimeInSeconds: 3600 to prevent infinite billing

Bedrock Service Failures

ThrottlingException During Peak Hours

Default Quotas (Pathetically Low):
- Claude 3.5 Sonnet: ~8k tokens/min
- Nova Pro: ~10k tokens/min
- GPT-4 via Bedrock: ~12k tokens/min
Business Impact: User-facing features fail during demos/high traffic
Quota Increase Timeline: 2-5 business days via AWS support
Emergency Workaround: Multi-region failover + exponential backoff

Essential Retry Logic:

import time
import random

def bedrock_with_retry(bedrock_call, max_retries=5):
    for attempt in range(max_retries):
        try:
            return bedrock_call()
        except ClientError as e:
            if e.response['Error']['Code'] == 'ThrottlingException':
                wait_time = (2 ** attempt) + random.uniform(0, 1)
                time.sleep(wait_time)
                continue
            else:
                raise e
    raise Exception("Max retries exceeded")

ModelNotReadyException - Cold Start Hell

Latency Impact: 10-30 seconds for first request after idle
User Experience: Appears as broken application
Workaround Cost: ~$5/month to keep models warm
Implementation: Ping every 5 minutes with minimal request

SageMaker Endpoint Deployment Failures

EndpointCreationFailed with Useless Errors

Debug Priority: Always test on smallest instance (ml.t2.medium) first
Common Root Causes:
1. Model artifact corruption (30%)
2. Docker memory issues (25%)
3. IAM permissions (20%)
4. VPC blocking S3 access (15%)
5. Python dependency conflicts (10%)

Endpoint Returns ModelError Despite "InService" Status

Failure Indicator: Endpoint deployed successfully but all requests return 500
Primary Cause: Inference script bugs (90% of cases)
Debug Command: Check CloudWatch logs immediately, not endpoint status

Resource Requirements and Costs

Training Job Resource Planning

Spot Instance Risk: 40% chance of termination for jobs >30 minutes
Memory Requirements: Add 50% buffer to model size estimates
GPU Instance Quotas: Default limits prevent most real workloads
Cost Spike Risk: Failed jobs continue billing until manually stopped

Production Endpoint Sizing

Auto-scaling Latency: 3-5 minutes to spin up new instances
Memory Overhead: Multi-model endpoints require 2x model size in RAM
Minimum Viable Setup: 2 instances for any production workload
Cost vs Performance: ml.p3 instances 10x cost but 3x performance vs ml.m5

Critical Configuration Settings

SageMaker Training Configuration

# Production-safe training job configuration
sagemaker.create_training_job(
    TrainingJobName='job-name',
    StoppingCondition={'MaxRuntimeInSeconds': 3600},  # Prevent infinite billing
    EnableNetworkIsolation=False,  # Unless VPC is properly configured
    EnableManagedSpotTraining=False  # For mission-critical training
)

Multi-Model Endpoint Memory Management

'MultiModelConfig': {
    'ModelCacheSetting': 'Enabled',
    'MaxModels': 3  # Limit concurrent models to prevent OOM
}

Auto-scaling Configuration

# Scale aggressively, cost is secondary to uptime
sagemaker.put_scaling_policy(
    TargetTrackingScalingPolicyConfiguration={
        'TargetValue': 50.0,  # Scale at 50% CPU, not 80%
        'ScaleOutCooldown': 60,   # Scale out fast
        'ScaleInCooldown': 900,   # Scale in slow
    }
)

Regional Availability Matrix

Model/Service	us-east-1	us-west-2	eu-west-1	ap-southeast-1
Claude 3.5 Sonnet	✓	✓	✓	✗
Nova Pro	✓	✓	✗	✗
ml.p3.8xlarge	✓	✓	Limited	Limited

Emergency Debugging Commands

Immediate Status Check (30 seconds)

# AWS service health
curl -s https://status.aws.amazon.com/data.json | jq '.current_events'

# Running expensive resources
aws sagemaker list-training-jobs --status-equals InProgress
aws sagemaker list-endpoints --status-equals InService

Log Analysis (2 minutes)

# Recent SageMaker errors
aws logs filter-log-events \
    --log-group-name /aws/sagemaker/TrainingJobs \
    --start-time $(date -d '1 hour ago' +%s)000 \
    --filter-pattern "ERROR"

# Bedrock throttling patterns
aws logs filter-log-events \
    --log-group-name /aws/bedrock \
    --filter-pattern "ThrottlingException"

Quota Verification (1 minute)

# Critical quotas that cause production failures
aws service-quotas get-service-quota \
    --service-code sagemaker \
    --quota-code L-1194D53C  # ml.p3.2xlarge instances

aws service-quotas get-service-quota \
    --service-code bedrock \
    --quota-code L-22C574D0  # Claude requests per minute

VPC and Networking Requirements

VPC Endpoint Requirements for SageMaker

S3 VPC Endpoint: Required for training data access
SageMaker API Endpoint: Required for service communication
Alternative: NAT Gateway (more expensive but simpler)
Common Failure: Training jobs timeout after 30 minutes without proper endpoints

Security Group Rules for AI/ML

Outbound HTTPS (443): Required for API calls
Outbound HTTP (80): Required for some model downloads
Emergency Rule: Allow all outbound traffic initially, then restrict

Performance Thresholds and Breaking Points

SageMaker Limits

Training Job Timeout: Default no timeout leads to infinite billing
Multi-Model Memory: >1000 spans causes UI breakdown
Endpoint Auto-scaling: 3-5 minute delay causes user-visible failures
Batch Transform: Files >100MB cause random failures

Bedrock Performance Characteristics

Cold Start: 10-30 seconds for idle models
Token Limits: Vary by region and change without notice
Regional Failover: Essential for production reliability

Common Misconceptions

"SageMaker Handles Everything Automatically"

Reality: Requires extensive IAM configuration, VPC setup, and monitoring
Hidden Costs: Auto-scaling, data transfer, CloudWatch logging
Failure Modes: Silent failures due to permission issues

"AWS Error Messages Are Helpful"

Reality: 90% of errors require CloudWatch log analysis
UnexpectedStatusException: Means "something failed, figure it out yourself"
AccessDenied: Could be 15 different permission issues

"Default Settings Work in Production"

Reality: Default quotas prevent any serious workload
Auto-scaling: Default thresholds cause user-visible latency
Timeout Settings: Will cause infinite billing without limits

Emergency Recovery Procedures

Nuclear Options (Last Resort)

Delete and Recreate Endpoints: When configuration is corrupted
Reset IAM Roles: When permissions are completely broken
Multi-Region Failover: When primary region has issues

Recovery Timeline Expectations

IAM Permission Fixes: 10-15 minutes
Quota Increase Requests: 2-5 business days
Endpoint Recreation: 5-10 minutes
Training Job Restarts: 15-30 minutes depending on data size

Cost Impact During Outages

Running Training Jobs: Continue billing until manually stopped
Idle Endpoints: $50-500/day depending on instance type
Failed Batch Jobs: May process partial data and still charge

Decision Criteria for Implementation

When to Use SageMaker vs Bedrock

SageMaker: Custom models, fine-tuning, batch processing
Bedrock: Quick LLM integration, managed scaling, multiple model access
Cost Comparison: Bedrock 3-5x more expensive per token but simpler ops

Instance Type Selection

Development: ml.t2.medium for debugging (cheapest)
CPU Inference: ml.m5.large for simple models
GPU Training: ml.p3.2xlarge minimum for deep learning
Production Inference: ml.c5.xlarge for latency-sensitive applications

Multi-Region Strategy

Essential for Production: Single region will fail
Cost Impact: 2x infrastructure costs but prevents business disruption
Implementation Complexity: High, requires sophisticated load balancing

This reference prioritizes operational intelligence over theoretical knowledge, focusing on the failures that actually occur in production environments and the proven solutions that resolve them quickly.

Useful Links for Further Investigation

Emergency Resources When Everything's Broken

Link	Description
AWS Status Page	First place to check when everything's broken. Bookmark this. AWS won't tell you about outages via error messages.
SageMaker Service Quotas Documentation	Check your limits before they kill your training jobs. Default quotas are pathetically small.
Bedrock Service Quotas Documentation	Bedrock quotas are even worse. Request increases immediately.
Stack Overflow - amazon-sagemaker tag	Real engineers solving real problems. Search here before filing support tickets.
AWS ML Community Slack	Active community. Post emergencies in #troubleshooting channel.
AWS Cost Explorer	Find what's burning money during outages. Filter by service and time range.
IAM Policy Simulator	Test IAM permissions without breaking production. Essential for AccessDenied errors.

AWS AI/ML Production Debugging: AI-Optimized Reference

Critical Failure Patterns

SageMaker Training Job Failures

Bedrock Service Failures

SageMaker Endpoint Deployment Failures

Resource Requirements and Costs

Training Job Resource Planning

Production Endpoint Sizing

Critical Configuration Settings

SageMaker Training Configuration

Multi-Model Endpoint Memory Management

Auto-scaling Configuration

Regional Availability Matrix

Emergency Debugging Commands

Immediate Status Check (30 seconds)

Log Analysis (2 minutes)

Quota Verification (1 minute)

VPC and Networking Requirements

VPC Endpoint Requirements for SageMaker

Security Group Rules for AI/ML

Performance Thresholds and Breaking Points

SageMaker Limits

Bedrock Performance Characteristics

Common Misconceptions

"SageMaker Handles Everything Automatically"

"AWS Error Messages Are Helpful"

"Default Settings Work in Production"

Emergency Recovery Procedures

Nuclear Options (Last Resort)

Recovery Timeline Expectations

Cost Impact During Outages

Decision Criteria for Implementation

When to Use SageMaker vs Bedrock

Instance Type Selection

Multi-Region Strategy

Useful Links for Further Investigation

Emergency Resources When Everything's Broken

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