AI Model Deployment and Scaling

About Course

This advanced course focuses on the critical transition of AI models from experimental development to robust, scalable production environments. Students will acquire
advanced architectural knowledge, practical MLOps operational practices, and deep technical skills essential for deploying, managing, and maintaining high performance, reliable, and secure AI systems across complex real-world applications, from cloud to edge computing

Design resilient, scalable architectures for deploying diverse AI models (deep learning, traditional ML) across cloud, hybrid, and edge environments.
Implement end-to-end CI/CD and continuous training (CT) pipelines for machine learning models using industry-standard tools like Jenkins, GitLab CI, and Kubeflow Pipelines.
Apply advanced optimization techniques—quantization, pruning, and hardware acceleration—to significantly enhance model performance, reduce latency, and minimize resource utilization across diverse deployment targets.
Develop and implement comprehensive monitoring, alerting, and observability systems for AI models in production, detecting data drift, concept drift, and performance degradation with tools like Prometheus, Grafana, and MLflow.
Formulate and execute robust strategies for managing model versioning, updates, rollbacks, and A/B testing, ensuring seamless transitions and minimal downtime in live systems.
Address complex scalability challenges inherent in high-volume, real-time AI applications through distributed inference, load balancing, and auto-scaling solutions on platforms like Kubernetes.

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1. ML Deployment Fundamentals
Explore the complete machine learning system architecture and the AI model deployment lifecycle, from research to production. Understand various serving patterns, including synchronous RESTful APIs for real-time inference, asynchronous batch processing, and hybrid approaches. Analyze diverse deployment environments such as public clouds (AWS, Azure, GCP), private data centers, edge devices (e.g., NVIDIA Jetson, Raspberry Pi), and on-premises infrastructure. Evaluate critical infrastructure considerations for AI workloads, including GPU provisioning, Docker containerization, and Kubernetes orchestration.
2. MLOps and CI/CD for AI
Dive into MLOps principles and best practices for automating and streamlining the ML lifecycle. This module covers robust model versioning strategies (e.g., Git, DVC), setting up a centralized model registry, and implementing automated testing frameworks for ML systems, including data validation, model performance tests, and robustness checks. Learn to design and build continuous training (CT) pipelines that trigger retraining based on data drift or performance thresholds. Master deployment automation using Infrastructure as Code (IaC) tools like Terraform and Ansible for ML environments. An introduction to feature stores (e.g., Feast) for consistent feature management and serving is also included.
3. Scalable Model Serving
Master industry-standard model serving platforms and frameworks like TensorFlow Serving, PyTorch Serve, and NVIDIA Triton Inference Server. Design efficient and secure API endpoints for ML services, considering microservices architecture and OpenAPI specifications. Implement advanced load balancing techniques, auto-scaling mechanisms (e.g., Horizontal Pod Autoscaler in Kubernetes), and distributed inference patterns to handle high request volumes. Explore caching strategies (e.g., Redis) and message queues (e.g., Kafka, RabbitMQ) for asynchronous processing to optimize throughput and reduce latency.
4. Model Optimization
Learn advanced optimization techniques to enhance model efficiency. This includes model quantization (e.g., 8-bit, 4-bit fixed-point for reduced memory and faster inference), model pruning (removing redundant weights or neurons), and various compression methods (e.g., sparsification, Huffman coding). Understand knowledge distillation, where a smaller "student" model learns from a larger "teacher" model. Explore hardware acceleration using specialized chips like GPUs, TPUs, and custom AI accelerators. Focus on model-specific optimizations and balancing the trade-off between model accuracy and deployment performance across different hardware configurations.
5. Monitoring and Maintenance
Establish key metrics for comprehensively monitoring model performance (e.g., precision, recall, F1-score, MAE) and system health (e.g., latency, throughput, resource utilization). Implement data drift and concept drift detection algorithms (e.g., ADWIN, DDM) to identify shifts in input data or target variable relationships. Develop robust A/B testing frameworks for comparing model versions in production. Design comprehensive logging, tracing, and auditing systems for ML pipelines using tools like the ELK stack or Open Telemetry. Master automated retraining triggers based on performance degradation, strategies for rapid model debugging in production, and robust alert and incident response systems for AI failures.
6. Advanced Deployment Scenarios
Examine specialized deployment contexts and cutting-edge paradigms. This includes practical approaches to edge and IoT deployment (e.g., deploying models on embedded systems or smart sensors with TensorFlow Lite or ONNX Runtime). Explore federated learning architectures for privacy-preserving model training and deployment across distributed devices. Understand multi-model serving systems (e.g., ensemble models, model routers). Cover online learning and incremental updates for models trained on continuous data streams. Discuss strategies for global model distribution using CDNs and distributed model registries. Finally, delve into deployment in highly regulated environments (e.g., finance, healthcare) and essential security considerations for AI systems, including adversarial attacks and data privacy compliance.

Students will design, implement, and fully deploy an end-to-end MLOps pipeline for a selected real-world machine learning application. This comprehensive project will encompass the entire lifecycle, from data ingestion and model training to scalable deployment on a cloud platform (e.g., AWS SageMaker, Azure ML, Google Cloud AI Platform), continuous monitoring for performance and drift, and automated updating mechanisms. The final deliverable will be a fully functional, production-ready AI system accessible via an API, accompanied by a comprehensive technical report detailing architectural decisions, implementation choices, operational procedures, a post-mortem analysis of potential issues, and a future roadmap for expansion.

AI Model Deployment and Scaling

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AI Model Deployment and Scaling

AI Model Deployment and Scaling

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What Will You Learn?

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