AI Model Deployment and Scaling

About Course

This course offers a comprehensive exploration of the
critical processes and technologies required to
transition AI models from development environments to
robust, scalable, and maintainable production systems.
Students will gain practical, hands-on experience with
industry-standard MLOps practices, containerization,
cloud infrastructure, and performance optimization. The
primary objective is to equip participants with the skills
to build reliable AI solutions that deliver tangible
business value at scale.

Master the end-to-end machine learning deployment lifecycle, encompassing continuous integration, delivery, and monitoring (CI/CD/CM) for AI systems.
Design and implement highly available, fault-tolerant architectures optimized for real-time and batch AI inference.
Effectively utilize containerization technologies like Docker and orchestration platforms such as Kubernetes for efficient model serving.
Apply core MLOps principles with leading tools like MLflow and Kubeflow to automate model versioning, training, and deployment pipelines.
Develop comprehensive monitoring strategies for deployed AI models, enabling proactive detection of data drift, concept drift, and performance
degradation.
Optimize AI deployments for superior cost-efficiency, low-latency performance, and enhanced security across diverse cloud and edge environments.

Course Content

From Research to Production: Bridging the Gap
This module examines the unique challenges of operationalizing AI models, transitioning them from experimental notebooks to robust production systems. Students will gain a thorough understanding of the full ML lifecycle— experimentation, development, deployment, monitoring, and iterative improvement—and explore common deployment patterns such as microservices, serverless functions, and batch processing. The module also introduces core MLOps principles and their critical importance for ensuring reliability and reproducibility.

Model Serving Architectures and APIs
Learn to package and serialize models for efficient deployment using industry-standard formats like ONNX and Pickle. Students will design and implement high-performance RESTful and gRPC APIs for real-time inference, leveraging frameworks such as FastAPI and Flask. The module differentiates between batch inference and real-time inference patterns and explores specialized model servers like TensorFlow Serving, TorchServe, and NVIDIA Triton Inference Server. Considerations for edge device deployment and IoT integration are also covered.

Scaling AI Systems with Cloud and Containers
Master horizontal and vertical scaling strategies essential for AI workloads. Implement effective load balancing for ML services and explore caching mechanisms to optimize performance. This module delves into distributed inference patterns using message queues (e.g., Kafka) and distributed computing frameworks (e.g., Ray). Students will learn to utilize cloud-native services like AWS SageMaker Endpoints, Azure ML Endpoints, and GCP AI Platform Prediction for automated scaling, alongside exploring hardware acceleration with GPUs and TPUs.

MLOps and CI/CD for AI Pipelines
Implement robust version control for datasets, models, and code using essential tools like Git and DVC (Data Version Control). Design and automate continuous integration and delivery (CI/CD) pipelines for ML systems with platforms such as Jenkins, GitLab CI, or GitHub Actions. This module covers automated model testing, the use of model registries (e.g., MLflow, Vertex AI Model Registry), and model governance. Students will also learn to implement infrastructure as code (IaC) for ML environments using tools like Terraform or CloudFormation.

Monitoring, Observability, and Maintenance
Establish comprehensive monitoring protocols for deployed AI models, tracking critical metrics like prediction latency, error rates, and resource utilization. Implement robust data drift and concept drift detection mechanisms. This module explores A/B testing and canary deployments for new model versions and focuses on setting up robust logging, alerting, and observability for AI systems using tools like Prometheus, Grafana, and the ELK Stack. Students will also design automated retraining strategies and develop effective incident response plans for production ML systems.

Production Optimization and Security
Apply advanced model optimization techniques, including quantization (e.g., INT8), pruning, and knowledge distillation, to significantly reduce model size and improve inference speed. This module emphasizes resource efficiency and cost management strategies in cloud environments (e.g., serverless compute, spot instances) and covers the implementation of high availability and disaster recovery patterns. Crucially, it addresses critical security considerations for deployed AI, including authentication, authorization, data encryption, and vulnerability management.

Capstone Project
Students will design and implement a comprehensive MLOps pipeline for a chosen machine learning application, such as a real-time recommendation engine, an image classification API, or a fraud detection system. This project will encompass automated data ingestion and model training, containerized deployment, A/B testing, and robust monitoring. The ultimate goal is to create a fully operational, scalable, and production-ready system that incorporates continuous integration, automated testing, version control for all artifacts, and comprehensive observability. Students will deliver a detailed technical report outlining their architecture, implementation choices, performance metrics, and a plan for ongoing maintenance and improvement.

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AI Model Deployment and Scaling

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AI Model Deployment and Scaling

AI Model Deployment and Scaling

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