Inference services in an AI company refer to the deployment of machine learning models into production to perform real-time or batch predictions (inferences) on new data. These services allow clients to use the AI models you’ve fine-tuned or developed without requiring them to have deep technical knowledge about model training or infrastructure.
Packaging: Export models in formats like TensorFlow SavedModel, ONNX, or PyTorch’s TorchScript.
Versioning: Maintain control for updates, rollbacks, and debugging.
Containerization: Use Docker for easy cross-platform deployment.
TensorFlow Serving: Scalable serving for TensorFlow models.
TorchServe: PyTorch model-serving with API and deployment options.
ONNX Runtime: Inference for models from various frameworks.
Triton Inference Server: Optimizes GPU-based inference for models from TensorFlow, PyTorch, etc.
Cloud: Use AWS, Google Cloud, or Azure for managed model deployment.
On-Premise: Deploy with Kubernetes and Docker on internal infrastructure.
Edge: Use TensorFlow Lite or ONNX Edge for real-time device inference.
REST/gRPC APIs: Set up endpoints for real-time inference requests.
Batch Inference: Process large datasets asynchronously.
Model Optimization: Use quantization, pruning, and knowledge distillation for faster inference.
Hardware Acceleration: Leverage GPUs, TPUs, and specialized FPGA/ASIC hardware.
Auto-scaling: Dynamically adjust instances based on demand.
Load Balancing: Evenly distribute inference requests.
Real-time Monitoring: Track performance metrics and detect model drift with tools like Prometheus.
Authentication: Secure APIs using OAuth, API keys.
Data Privacy: Encrypt data in transit and at rest.
Audit Logs: Ensure detailed logs for compliance.
Custom API Integration: Easy-to-use APIs for seamless client integration.
SDKs: Develop SDKs in Python, Java, or JavaScript for client-side access.
Optimize Resources: Reduce resource usage for efficient cost management.
Batch vs Real-time: Use batch processing for cost-effective, non-urgent tasks.