Integrating Custom Models into Your Annotation Pipeline

Integrating Custom Models into Your Annotation Pipeline

Modern machine learning workflows demand flexibility in model integration, especially when dealing with specialized domains and custom architectures. Organizations increasingly need to leverage their existing models while taking advantage of robust annotation platforms. This guide explores how to effectively integrate custom models into your annotation pipeline using Encord's enterprise-grade platform, enabling automated pre-labeling while maintaining full control over your model deployment and inference processes.

Integration Architecture Overview

The foundation of successful model integration lies in designing a scalable and maintainable architecture. Encord's annotation platform provides multiple integration patterns to accommodate different deployment scenarios and security requirements. Whether you're working with on-premise models or cloud-based solutions, the platform's flexible architecture supports seamless integration while maintaining data security and performance.

The core integration architecture consists of three primary components:

• Model Serving Layer - Handles model deployment and inference

• API Gateway - Manages authentication and request routing

• Prediction Handler - Processes model outputs and converts them to annotation format

Organizations can choose between several deployment patterns:

• Direct Integration - Models deployed within your infrastructure make API calls to Encord
• Hosted Integration - Models deployed on Encord's secure cloud infrastructure
• Hybrid Approach - Combination of local and cloud-hosted models

When selecting an integration pattern, consider factors like data privacy requirements, inference latency needs, and existing infrastructure investments. For teams working with sensitive data, the physical AI solutions offering provides additional security controls while maintaining full integration capabilities.

API Setup and Authentication

Setting up secure API access requires proper authentication configuration. Encord uses API keys and optional IP whitelisting to ensure only authorized systems can access the model integration endpoints.

To configure API authentication:

from encord import EncordClient

# Initialize client with API key
client = EncordClient(api_key="your_api_key")

# Configure additional security options
client.configure(
    ip_whitelist=["10.0.0.0/24"],
    request_timeout=30
)

The platform supports both synchronous and asynchronous inference patterns. For high-throughput workflows, the asynchronous API enables batch processing while providing status monitoring:

# Submit batch inference request
job_id = client.submit_batch_inference(
    model_id="custom_model_1",
    data_ids=["img_001", "img_002"],
    callback_url="https://your-callback-endpoint"
)

# Check job status
status = client.get_job_status(job_id)

Model Serving Options

Encord provides flexible options for serving your custom models. The data-agents feature enables automated pre-labeling while supporting various model deployment scenarios:

Self-Hosted Deployment

For teams requiring complete control over model infrastructure, self-hosted deployment allows models to run within your environment while still integrating with Encord's annotation workflow. This approach is particularly valuable for organizations with strict data governance requirements or specialized hardware needs.

Managed Cloud Deployment

Alternatively, Encord can host your models in a secure, scalable cloud environment. This option eliminates infrastructure management overhead while providing:

• Automatic scaling based on inference demand

• Built-in monitoring and logging

• High availability across multiple regions

• Regular security updates and maintenance

Hybrid Deployment

Many organizations opt for a hybrid approach, combining self-hosted models for sensitive data with cloud-hosted models for general tasks. The platform's multimodal capabilities support seamless integration across different deployment patterns.

Handling Model Predictions

Converting model predictions into annotation format requires careful consideration of coordinate systems, confidence thresholds, and label mapping. The platform provides built-in converters for common formats while allowing custom conversion logic when needed.

def convert_predictions(model_output):
    annotations = []
    for prediction in model_output:
        annotation = {
            "label": prediction["class"],
            "confidence": prediction["score"],
            "bbox": normalize_coordinates(prediction["bbox"])
        }
        annotations.append(annotation)
    return annotations

The active learning system can incorporate model predictions to optimize the annotation workflow:

• Automatically pre-label high-confidence predictions

• Flag uncertain predictions for human review

• Track model performance over time

• Identify edge cases requiring additional training

Error Handling and Monitoring

Robust error handling and monitoring are essential for production model integration. Encord provides comprehensive monitoring tools to track:

• Model inference latency

• Prediction accuracy

• API response times

• Error rates and types

• Resource utilization

When errors occur, the platform implements automatic retries with exponential backoff:

def handle_inference_request(image_data, retry_count=3):
    for attempt in range(retry_count):
        try:
            predictions = model.predict(image_data)
            return predictions
        except TemporaryError as e:
            if attempt == retry_count - 1:
                raise
            time.sleep(2 ** attempt)

Example: Integrating a Custom Detection Model

Let's walk through integrating a custom object detection model:

• Prepare the model endpoint:

from fastapi import FastAPI
app = FastAPI()

@app.post("/predict")
async def predict(data: Dict):
    image = preprocess_image(data["image"])
    predictions = model.predict(image)
    return convert_predictions(predictions)

• Configure the integration in Encord:

client.register_model(
    name="custom_detector",
    endpoint="https://your-model-endpoint/predict",
    input_format="image/jpeg",
    output_format="bounding_box"
)

• Enable automated pre-labeling:

project.enable_prelabeling(
    model_id="custom_detector",
    confidence_threshold=0.8,
    review_required=True
)

Conclusion

Successful integration of custom models into your annotation pipeline requires careful consideration of architecture, security, and monitoring. Encord's platform provides the flexibility and tools needed to seamlessly incorporate your models while maintaining high-quality annotations and efficient workflows.

To get started with custom model integration:

• Evaluate your deployment requirements
• Configure secure API access
• Implement proper error handling
• Enable monitoring and tracking
• Test with a subset of data before full deployment

Ready to enhance your annotation pipeline with custom model integration? Explore Encord's enterprise platform to learn more about our comprehensive computer vision and multimodal AI solutions.