.. _ai-model-deployment: AI Model Deployment and Monitoring =================================== Guide to deploying and monitoring AI models in production with Buildly. .. contents:: Table of Contents :local: :depth: 2 Overview -------- Deploy AI models as scalable, production-ready services within the Buildly ecosystem. This guide covers deployment strategies, monitoring, and maintenance of AI models. .. note:: **Coming Soon:** Detailed guides for AI model deployment are being developed. Check back for comprehensive documentation. Deployment Strategies --------------------- Deployment Options ~~~~~~~~~~~~~~~~~~ **Available Options:** - Docker containers for model serving - Kubernetes for scalable deployments - Serverless functions for occasional inference - Edge deployment for low-latency requirements **Coming Soon:** - Step-by-step deployment guides - Performance optimization techniques - Scaling strategies - Cost optimization Model Serving ------------- Inference Endpoints ~~~~~~~~~~~~~~~~~~~ .. code-block:: python # Example model serving endpoint # Coming Soon: Complete implementation from rest_framework.views import APIView from rest_framework.response import Response class ModelPredictionView(APIView): """ AI model prediction endpoint """ def post(self, request): # Load model # Process input # Return prediction pass **Coming Soon:** - RESTful API design for ML models - Batch prediction endpoints - Streaming inference - gRPC support Containerization ---------------- Docker for ML Models ~~~~~~~~~~~~~~~~~~~~ **Coming Soon:** - Dockerfile templates for ML models - Multi-stage builds for ML applications - GPU support in containers - Model artifact management Performance Optimization ------------------------ Inference Speed ~~~~~~~~~~~~~~~ **Coming Soon:** - Model optimization techniques - Quantization strategies - Caching mechanisms - Load balancing Monitoring ---------- Production Monitoring ~~~~~~~~~~~~~~~~~~~~~ **Coming Soon:** - Prediction latency tracking - Model accuracy monitoring - Resource utilization - Error rate analysis Alerting Systems ~~~~~~~~~~~~~~~~ **Coming Soon:** - Alert configuration - Performance degradation detection - Anomaly detection - Incident response Model Updates ------------- Continuous Deployment ~~~~~~~~~~~~~~~~~~~~~ **Coming Soon:** - Model versioning in production - Blue-green deployments - Canary releases - Rollback procedures A/B Testing ~~~~~~~~~~~ **Coming Soon:** - A/B testing framework - Traffic splitting - Performance comparison - Statistical analysis Resources --------- **Tools and Frameworks:** - TensorFlow Serving - TorchServe - ONNX Runtime - Seldon Core **Video Resources:** - `Buildly YouTube Channel `_ - AI model deployment and MLOps tutorials - `OpenBuild YouTube Channel `_ - Production ML system guides **Next Steps:** - :doc:`/ai/ml-pipelines` - Building ML pipelines - :doc:`/deployment/docker` - Container deployment .. note:: Comprehensive AI model deployment documentation is coming soon. Join the Buildly community for updates and early access to examples.