Deploying YOLO11 Object Detection to AWS SageMaker: A Complete Guide

Learn how to deploy the state-of-the-art YOLO11 object detection model to Amazon SageMaker AI for production-ready, real-time inference with GPU acceleration.

Introduction

Object detection is a critical component in many modern AI applications, from autonomous vehicles to security systems. In this comprehensive guide, I'll walk you through deploying the YOLO11 (You Only Look Once) model to Amazon SageMaker AI, enabling scalable, production-ready object detection in the cloud.

What you'll learn:

• Setting up the YOLO11 model with pre-trained weights
• Creating custom inference handlers for SageMaker
• Packaging and deploying models to GPU-accelerated endpoints
• Performing real-time inference with bounding box visualization
• Best practices for production deployments

Prerequisites:

• AWS account with SageMaker access
• Basic understanding of Python and PyTorch
• Familiarity with computer vision concepts

Building on AWS Best Practices

This guide builds upon the excellent AWS Machine Learning Blog post on hosting YOLOv8 by the AWS team, with significant updates and improvements for 2024-2025:

🚀 What's New and Improved:

1. Latest YOLO Version: Upgraded from YOLOv8 to YOLO11 (2024 release)

   • 22% fewer parameters with higher mAP scores
   • Enhanced feature extraction with improved backbone and neck architecture
   • Better optimization for both edge and cloud deployments

2. Production-Ready Inference Code: Enhanced custom handlers with:

   • Robust error handling with multiple fallback mechanisms
   • Class name caching for improved performance
   • Efficient batch processing with pre-allocated data structures
   • Type-safe class name resolution (dict/sequence support)
   • Layer fusion optimization with graceful degradation

3. Modern Technology Stack: Updated to current versions

   • PyTorch 2.6.0 (from 2.0.0) - better performance and features
   • Python 3.12 (from 3.10) - improved speed and security
   • Latest Ultralytics package with newest YOLO improvements

4. Advanced Visualization Pipeline: Professional-grade image processing

   • Coordinate scaling for accurate bounding boxes
   • Confidence percentage overlays
   • Multi-image batch processing with organized output management
   • Random color generation for visual distinction

5. Comprehensive Production Guidance: Enterprise-ready deployment

   • Security best practices (IAM, VPC, KMS, Model Cards)
   • Model versioning and governance strategies
   • Advanced monitoring with data capture configuration
   • Retry logic with exponential backoff
   • Detailed cost analysis with optimization strategies

6. Complete Cost Breakdown: Realistic budgeting scenarios

   • 24/7 vs. part-time usage cost comparisons
   • Storage and inference cost details
   • Multiple optimization strategies (serverless, spot instances, auto-scaling)
   • Endpoint lifecycle management techniques

7. Advanced Topics: Beyond basic deployment

   • Multi-model endpoints for variant testing
   • Custom training on domain-specific datasets
   • Video processing capabilities
   • Edge deployment with SageMaker Neo and IoT Greengrass
   • A/B testing with traffic splitting

8. Troubleshooting Guide: Common issues and solutions

   • Endpoint creation failures
   • Out of memory errors
   • Inference latency optimization
   • Detection accuracy tuning

If you're familiar with the AWS blog post, you'll find this guide takes the concepts further with the latest technology, production-hardened code, and comprehensive operational guidance for real-world deployments.

Why YOLO11 and SageMaker?

Why YOLO11 instead of YOLO12? While YOLO12 is now available, it's maintained primarily as a community model for benchmarking and research. For production deployments requiring stable training, predictable memory usage, and optimized CPU inference, YOLO11 remains the recommended choice from Ultralytics for enterprise use.

YOLO11 offers:

• State-of-the-art accuracy for object detection
• Real-time inference capabilities
• Pre-trained weights on COCO dataset (80+ object classes)
• Excellent balance between speed and accuracy
• Production-ready stability and optimization

Amazon SageMaker AI provides:

• Fully managed ML infrastructure
• Built-in support for PyTorch and popular frameworks
• GPU-accelerated instances for fast inference
• Auto-scaling and monitoring capabilities
• Easy deployment and management

Together, they create a powerful, production-ready object detection solution.

Architecture Overview

Our deployment architecture consists of several key components:

1. Model Preparation: Download YOLO11 pre-trained weights
2. Custom Inference Code: Create handlers for SageMaker integration
3. Model Packaging: Bundle weights and code into a tar.gz artifact
4. S3 Storage: Upload model artifacts to S3
5. SageMaker Endpoint: Deploy to GPU instance (ml.g4dn.2xlarge)
6. Real-time Inference: Send images and receive detection results

[YOLO11 Weights] → [Custom Inference Handler] → [Model Artifact]
                                                        ↓
                                                    [S3 Bucket]
                                                        ↓
                                              [SageMaker Endpoint]
                                                        ↓
                                              [Real-time Predictions]

Step 1: Environment Setup

First, let's set up our development environment with the required packages:

%pip install \
  "sagemaker==2.254.1" \
  "ultralytics>=8.3.0" \
  "opencv-python>=4.8.0" \
  matplotlib \
  boto3 \
  awscli -q

Key dependencies:

• sagemaker: AWS SDK for Amazon SageMaker AI operations
• ultralytics: Official YOLO11 implementation
• opencv-python: OpenCV for image processing and visualization
• boto3: AWS SDK for Python

Verify the installation:

import sys
import sagemaker

print("SageMaker version:", sagemaker.__version__)
print("Python:", sys.version)

📸 Screenshot Suggestion: Show the output of version checks and successful package installation

Step 2: Download YOLO11 Pre-trained Weights

YOLO11 comes in several variants (nano, small, medium, large, extra-large). We'll use the large variant (yolo11l.pt) for a good balance of accuracy and speed:

curl -O "https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt"

Model variants comparison:

• yolo11n.pt: Fastest, lowest accuracy (~3.2M parameters)
• yolo11s.pt: Balanced for edge devices (~9.4M parameters)
• yolo11m.pt: Medium accuracy and speed (~20.1M parameters)
• yolo11l.pt: High accuracy, moderate speed (~25.3M parameters) ✅
• yolo11x.pt: Highest accuracy, slower (~56.9M parameters)

💡 Tip: Choose your variant based on your accuracy requirements and inference latency constraints.

Step 3: Create Custom Inference Handler

Amazon SageMaker AI requires specific functions to handle the inference lifecycle. Here's our production-ready inference.py:

import os
import json
import time
import logging

import numpy as np
import cv2
import torch
from ultralytics import YOLO

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def model_fn(model_dir):
    """Load and prepare YOLO model for inference."""
    logger.info("Loading YOLO model from %s", model_dir)

    weights_name = os.getenv("YOLO_MODEL", "yolo11l.pt")
    weights_path = os.path.join(model_dir, weights_name)

    if not os.path.exists(weights_path):
        raise FileNotFoundError(f"Model weights not found: {weights_path}")

    # Load YOLO11 model
    model = YOLO(weights_path)

    # Move model to GPU if available
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logger.info("Using device: %s", device)
    model.to(device)

    # Try to fuse layers for inference speedup (may not work for all models)
    try:
        model.fuse()
        logger.info("Model layers fused successfully")
    except Exception as e:
        logger.warning("Could not fuse model layers: %s", e)

    model.eval()

    # Cache class names for use in output_fn
    model.class_names = model.names

    # read default conf from env, fallback to 0.25
    model.conf_thres = float(os.getenv("YOLO_CONF", "0.25"))

    return model


def input_fn(request_body, request_content_type):
    """Decode image from request body."""
    if request_content_type not in ("image/jpeg", "image/png"):
        raise ValueError(f"Unsupported content type: {request_content_type}")

    # Decode image bytes
    img_array = np.frombuffer(request_body, dtype=np.uint8)
    img = cv2.imdecode(img_array, flags=cv2.IMREAD_COLOR)

    if img is None:
        raise ValueError("Failed to decode image; invalid image bytes")

    return img


def predict_fn(input_data, model):
    """Run inference on input image."""
    logger.info("Executing predict_fn from inference.py ...")

    start = time.perf_counter()
    with torch.no_grad():
        results = model(input_data, conf=getattr(model, "conf_thres", 0.25))
    elapsed = (time.perf_counter() - start) * 1000
    logger.info("Inference completed in %.2f ms", elapsed)

    return results


def output_fn(prediction_output, content_type):
    """Format prediction results as JSON."""
    detections = []

    # Prediction_output is a list of Ultralytics Results objects
    for result in prediction_output:
        # Get class names (prefer result.names, fallback to model.class_names)
        names = getattr(result, "names", None) or \
                getattr(getattr(result, "model", None), "names", None) or \
                getattr(getattr(result, "model", None), "class_names", None)

        # Check if names is dict or list/tuple once
        names_is_dict = isinstance(names, dict)
        names_is_seq = isinstance(names, (list, tuple))

        # Process boxes if available
        if hasattr(result, "boxes") and result.boxes is not None:
            boxes_data = result.boxes.data
            if boxes_data is not None and len(boxes_data) > 0:
                # Convert to numpy once (more efficient than per-item conversion)
                boxes_np = boxes_data.cpu().numpy()

                # Pre-allocate list for better performance
                num_boxes = len(boxes_np)
                detections_batch = []

                for box_data in boxes_np:
                    x1, y1, x2, y2, conf, cls_id = box_data[:6]
                    cls_id = int(cls_id)

                    # Map class id -> label string
                    if names_is_dict:
                        label = names.get(cls_id, str(cls_id))
                    elif names_is_seq and 0 <= cls_id < len(names):
                        label = names[cls_id]
                    else:
                        label = str(cls_id)

                    detections_batch.append({
                        "box": [float(x1), float(y1), float(x2), float(y2)],
                        "confidence": float(conf),
                        "class_id": cls_id,
                        "label": label,
                    })

                detections.extend(detections_batch)

    return json.dumps({"detections": detections})

Key handler functions:

1. model_fn(): Loads the YOLO model, moves it to GPU, and optimizes it with layer fusion
2. input_fn(): Decodes incoming image bytes (JPEG/PNG) into OpenCV format
3. predict_fn(): Performs inference with timing metrics
4. output_fn(): Formats detections as JSON with bounding boxes, confidence scores, and labels

Environment variables for configuration:

• YOLO_MODEL: Model weights filename (default: yolo11l.pt)
• YOLO_CONF: Confidence threshold (default: 0.25)
• TS_MAX_RESPONSE_SIZE: Maximum response size (20MB)

Step 4: Package Model Artifacts

We need to create a requirements.txt for dependencies and package everything:

# Create requirements.txt
with open('requirements.txt', 'w') as f:
    f.write('ultralytics>=8.3.0\n')
    f.write('opencv-python>=4.8.0\n')

# Organize files
os.makedirs('code/', exist_ok=True)
shutil.move('inference.py', 'code/')
shutil.move('requirements.txt', 'code/')

Now create the model artifact (tar.gz):

import tarfile
import sagemaker

# Package model weights
model_name = "yolo11l.pt"
artifact_path = "model.tar.gz"

with tarfile.open(artifact_path, "w:gz") as tar:
    tar.add(model_name, arcname=model_name)

# Upload to Amazon S3
session = sagemaker.Session()
bucket = session.default_bucket()
model_s3_path = session.upload_data(
    path=artifact_path,
    bucket=bucket,
    key_prefix="pytorch_models"
)

print("Uploaded model artifact to:", model_s3_path)

Verify the contents:

tar -ztvf model.tar.gz | sort

📸 Screenshot Suggestion: Show the Amazon S3 upload confirmation and bucket structure

Step 5: Deploy to Amazon SageMaker AI Endpoint

Now for the exciting part—deploying our model to a GPU-accelerated endpoint:

from sagemaker.pytorch import PyTorchModel
from sagemaker.deserializers import JSONDeserializer
from datetime import datetime

# Configure PyTorch model
pytorch_model = PyTorchModel(
    model_data=model_s3_path,
    role=role,
    framework_version="2.6.0",
    py_version="py312",
    entry_point="inference.py",
    source_dir="code",
    env={
        "TS_MAX_RESPONSE_SIZE": "20000000",
        "YOLO_MODEL": "yolo11l.pt",
        "YOLO_CONF": "0.25",
    },
)

# Deploy to endpoint (takes 4-6 minutes)
instance_type = "ml.g4dn.2xlarge"
endpoint_name = "yolov11-pytorch-" + datetime.utcnow().strftime("%Y-%m-%d-%H-%M-%S-%f")

predictor = pytorch_model.deploy(
    initial_instance_count=1,
    instance_type=instance_type,
    endpoint_name=endpoint_name,
    deserializer=JSONDeserializer(),
)

print(f"Endpoint deployed: {endpoint_name}")

Instance selection:

• ml.g4dn.2xlarge: Single NVIDIA T4 GPU (16GB), 8 vCPUs, 32GB RAM
• Cost: ~$0.94/hour (on-demand pricing)
• Perfect for real-time inference with moderate throughput

📸 Screenshot Suggestion: Show the Amazon SageMaker AI console with the endpoint being created, then in "InService" status

Step 6: Monitor Deployment in Amazon SageMaker AI Console

While the deployment is in progress, you can monitor it in the AWS Console:

1. Navigate to Amazon SageMaker AI → Endpoints
2. Find your endpoint by name
3. Watch the status change from Creating → InService
4. Check the Monitoring tab for Amazon CloudWatch metrics

📸 Screenshot Suggestion: Show the Amazon SageMaker AI endpoint dashboard with key metrics like invocation count, model latency, and instance utilization

Key metrics to monitor:

• ModelLatency: Time taken for inference
• OverheadLatency: Time for pre/post-processing
• Invocations: Number of prediction requests
• ModelSetupTime: Initial model loading time

Step 7: Real-time Inference with Visualization

Let's test our endpoint with sample images! First, prepare your test images in a sample_images/ directory.

from sagemaker.predictor import Predictor
from sagemaker.serializers import IdentitySerializer
from sagemaker.deserializers import JSONDeserializer
import cv2
import random
import glob
import os

# Connect to deployed endpoint
predictor = Predictor(
    endpoint_name="your-endpoint-name",
    sagemaker_session=session,
    deserializer=JSONDeserializer(),
)
predictor.serializer = IdentitySerializer(content_type="image/jpeg")

# Process images
base_dir = "sample_images"
out_dir = "sample_images_output"
os.makedirs(out_dir, exist_ok=True)

image_paths = sorted(glob.glob(os.path.join(base_dir, "*.jpg")))

for image_path in image_paths:
    # Read and resize image
    orig_image = cv2.imread(image_path)
    image_height, image_width, _ = orig_image.shape

    resized_image = cv2.resize(orig_image, (300, 300))
    payload = cv2.imencode('.jpg', resized_image)[1].tobytes()

    # Get predictions
    result = predictor.predict(payload)

    # Draw bounding boxes
    for det in result.get("detections", []):
        x1, y1, x2, y2 = det["box"]
        conf = det["confidence"]
        label = det["label"]

        # Scale coordinates back to original image size
        x_ratio = image_width / 300
        y_ratio = image_height / 300
        x1, x2 = int(x_ratio * x1), int(x_ratio * x2)
        y1, y2 = int(y_ratio * y1), int(y_ratio * y2)

        # Random color for each detection
        color = (random.randint(10, 255),
                 random.randint(10, 255),
                 random.randint(10, 255))

        cv2.rectangle(orig_image, (x1, y1), (x2, y2), color, 4)
        cv2.putText(
            orig_image,
            f"{label} ({int(conf * 100)}%)",
            (x1, y1 - 10),
            cv2.FONT_HERSHEY_SIMPLEX,
            1,
            color,
            2,
            cv2.LINE_AA,
        )

    # Save annotated image
    base_name = os.path.basename(image_path)
    name, ext = os.path.splitext(base_name)
    out_path = os.path.join(out_dir, f"{name}_detected{ext}")
    cv2.imwrite(out_path, orig_image)
    print(f"Saved: {out_path}")

📸 Screenshot Suggestions:

1. Image Library: Show a grid/gallery of your sample input images (5-6 different scenes)
2. Before/After Comparison: Show a split-screen or side-by-side comparison of an original image and the same image with detected objects and bounding boxes
3. Detection Results: Show 2-3 different examples with various objects detected (people, cars, animals, etc.) with confidence scores visible

Understanding the Results

The model returns detections in JSON format:

{
  "detections": [
    {
      "box": [145.2, 210.8, 432.6, 589.3],
      "confidence": 0.92,
      "class_id": 0,
      "label": "person"
    },
    {
      "box": [520.1, 180.4, 680.9, 420.7],
      "confidence": 0.87,
      "class_id": 2,
      "label": "car"
    }
  ]
}

Detection fields:

• box: Bounding box coordinates [x1, y1, x2, y2]
• confidence: Detection confidence score (0.0-1.0)
• class_id: Numeric class identifier from COCO dataset
• label: Human-readable class name (e.g., "person", "car", "dog")

COCO dataset includes 80 classes:

• People: person
• Vehicles: bicycle, car, motorcycle, bus, truck, etc.
• Animals: cat, dog, horse, bird, etc.
• Objects: chair, bottle, laptop, cell phone, etc.

Performance Considerations

Latency Breakdown

Typical inference times on ml.g4dn.2xlarge with YOLO11l:

Component	Time
Image decoding	5-10ms
Model inference	30-50ms
Post-processing	5-10ms
Total latency	40-70ms

Optimization Tips

1. Batch Processing: For higher throughput, process multiple images in batches

results = model([image1, image2, image3], conf=0.25)

2. Adjust Confidence Threshold: Lower threshold = more detections, higher false positives

# More conservative (fewer detections)
predictor.env["YOLO_CONF"] = "0.4"

# More aggressive (more detections)
predictor.env["YOLO_CONF"] = "0.15"

3. Choose the Right Instance:

• ml.g4dn.xlarge: Budget option, single GPU
• ml.g4dn.2xlarge: Recommended, good balance ✅
• ml.p3.2xlarge: Higher performance, V100 GPU

4. Enable Auto-scaling: Handle variable traffic

from sagemaker import AutoScaler

auto_scaler = AutoScaler.attach(endpoint_name)
auto_scaler.scale(
    min_instances=1,
    max_instances=5,
    target_value=70.0,  # Target invocations per minute
)

Cost Analysis

Let's break down the costs for running this solution:

Endpoint Costs

• Instance: ml.g4dn.2xlarge @ $0.94/hour
• Monthly (24/7): ~$680/month
• Monthly (8 hours/day): ~$227/month

Storage Costs

• Amazon S3 storage: $0.023/GB/month
• Model artifact: ~100MB = $0.002/month (negligible)

Inference Costs

• First 1M requests: Free (Amazon SageMaker AI Free Tier)
• Additional requests: Minimal compute cost (already covered by instance)

Cost Optimization Strategies

1. Use Amazon SageMaker AI Serverless Inference for sporadic traffic
2. Enable auto-scaling to scale down during low usage
3. Use Amazon EC2 Spot Instances for non-production workloads (70% savings)
4. Set up endpoint lifecycle management to stop instances when not needed

# Delete endpoint when not in use
predictor.delete_endpoint()

# Recreate when needed
predictor = pytorch_model.deploy(...)

Production Best Practices

1. Monitoring and Logging

Enable Amazon CloudWatch logs and metrics:

from sagemaker.model_monitor import DataCaptureConfig

data_capture_config = DataCaptureConfig(
    enable_capture=True,
    sampling_percentage=100,
    destination_s3_uri=f"s3://{bucket}/data-capture"
)

2. Error Handling

Implement retry logic and fallbacks:

import time
from botocore.exceptions import ClientError

def predict_with_retry(predictor, payload, max_retries=3):
    for attempt in range(max_retries):
        try:
            return predictor.predict(payload)
        except ClientError as e:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
                continue
            raise

3. Security

• Use AWS IAM roles with minimal required permissions
• Enable Amazon VPC endpoints for private subnet deployment
• Encrypt model artifacts with AWS KMS
• Use Amazon SageMaker AI Model Cards for governance

4. Model Versioning

Track model versions and configurations:

model_package = pytorch_model.register(
    content_types=["image/jpeg"],
    response_types=["application/json"],
    inference_instances=["ml.g4dn.2xlarge"],
    model_package_group_name="yolo11-models"
)

Cleanup

Don't forget to delete resources to avoid ongoing charges:

# Delete the endpoint
predictor.delete_endpoint(delete_endpoint_config=True)

# Delete model
sagemaker_client = boto3.client('sagemaker')
sagemaker_client.delete_model(ModelName=pytorch_model.name)

# Optional: Delete S3 artifacts
s3_client = boto3.client('s3')
# s3_client.delete_object(Bucket=bucket, Key='pytorch_models/model.tar.gz')

Verify deletion in AWS Console:

1. Amazon SageMaker AI → Endpoints → (should be empty)
2. Amazon SageMaker AI → Models → (should be empty)
3. Amazon S3 → Your bucket → (optional cleanup)

Next Steps and Advanced Topics

Ready to take your deployment further? Consider these enhancements:

1. Multi-Model Endpoints

Deploy multiple YOLO variants (nano, small, large) on a single endpoint:

from sagemaker.multidatamodel import MultiDataModel

mdm = MultiDataModel(
    name="yolo-multi-model",
    model_data_prefix=f"s3://{bucket}/multi-models/",
    ...
)

2. Custom Training

Fine-tune YOLO11 on your custom dataset:

from ultralytics import YOLO

model = YOLO("yolo11l.pt")
model.train(data="custom_dataset.yaml", epochs=100)

3. Video Processing

Process video streams frame-by-frame or with batching

4. Edge Deployment

Deploy to edge devices using Amazon SageMaker AI Neo or AWS IoT Greengrass

5. A/B Testing

Test different model variants with traffic splitting:

predictor.update_endpoint(
    initial_instance_count=1,
    instance_type=instance_type,
    variant_name="AllTraffic",
    initial_weight=70  # 70% traffic to this variant
)

Troubleshooting Common Issues

Issue 1: Endpoint Creation Fails

Symptom: Endpoint stuck in "Failed" state

Solutions:

• Check Amazon CloudWatch logs: /aws/sagemaker/Endpoints/{endpoint_name}
• Verify IAM role has required permissions
• Ensure Amazon S3 model artifact is accessible
• Check instance type availability in your region

Issue 2: Out of Memory Errors

Symptom: Model fails to load or crashes during inference

Solutions:

• Use a larger instance type (e.g., ml.g4dn.4xlarge)
• Switch to a smaller YOLO variant (yolo11m or yolo11s)
• Reduce batch size if processing multiple images

Issue 3: Slow Inference

Symptom: High latency (>500ms per image)

Solutions:

• Ensure GPU acceleration is working (check logs for "cuda")
• Reduce image resolution before sending
• Enable layer fusion in model_fn
• Use a faster YOLO variant (yolo11n or yolo11s)

Issue 4: Low Detection Accuracy

Symptom: Missing objects or incorrect classifications

Solutions:

• Lower confidence threshold: YOLO_CONF=0.15
• Use a larger model variant (yolo11x)
• Ensure proper image preprocessing
• Fine-tune on domain-specific data

Conclusion

Congratulations! You've successfully deployed a production-ready YOLO11 object detection model to AWS SageMaker. You now have a scalable, GPU-accelerated endpoint capable of real-time inference on images.

Key takeaways:

• ✅ YOLO11 provides state-of-the-art object detection
• ✅ Amazon SageMaker AI simplifies model deployment and management
• ✅ GPU instances (ml.g4dn) offer excellent price/performance
• ✅ Custom inference handlers enable full control over the pipeline
• ✅ Production best practices ensure reliability and cost-efficiency

What we accomplished:

1. Downloaded and packaged YOLO11 pre-trained weights
2. Created custom Amazon SageMaker AI inference handlers
3. Deployed to a GPU-accelerated endpoint
4. Performed real-time inference with visualization
5. Implemented monitoring and cleanup procedures

This deployment pattern can be adapted for other computer vision tasks like image classification, semantic segmentation, or pose estimation. The principles of custom handlers, model packaging, and SageMaker deployment remain consistent.

Resources and References

Official Documentation

• Ultralytics YOLO11 Docs
• Amazon SageMaker AI Developer Guide
• PyTorch on SageMaker

Related AWS Blog Posts

• Hosting YOLOv8 PyTorch Model on Amazon SageMaker AI
• Deploying PyTorch Models at Scale Using TorchServe

Code Repository

• Complete Notebook and Code (update with your repo)

Community

• Ultralytics Discord
• AWS ML Community
• SageMaker Examples GitHub

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Keywords: YOLO11, Object Detection, AWS SageMaker, PyTorch, Computer Vision, Machine Learning, Deep Learning, GPU Inference, Real-time Detection, MLOps