Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Building a high-performing AI model is only part of the journey. To deliver real business value, models must be deployed, optimized, monitored, and integrated into production environments. This course equips you with the practical knowledge and tools required to move fine-tuned AI models from development to real-world deployment. You will begin by exploring model packaging, inference pipelines, APIs, and deployment architectures used to serve AI models efficiently. Next, you will learn how to deploy models using containers, cloud platforms, and scalable serving frameworks while optimizing latency, throughput, and resource utilization. Finally, you will explore production monitoring, model versioning, security, and continuous deployment practices to ensure deployed AI systems remain reliable, secure, and maintainable over time. By the End of This Course, You Will Be Able To: - Deploy fine-tuned AI models using modern serving frameworks and deployment workflows. - Apply model optimization techniques to improve inference performance and scalability. - Analyze production deployments using monitoring, logging, and version management practices. - Evaluate deployment architectures to select reliable and secure solutions for AI applications. Designed for AI engineers, machine learning practitioners, software developers, and MLOps professionals, this course provides the practical skills needed to successfully deploy and manage production-ready AI models.

Syllabus

Practical Foundations for AI Model Deployment

This module revisits essential deep learning concepts before introducing model compression techniques for efficient AI inference. Learners explore transformer inference, quantization, knowledge distillation, and performance benchmarking to optimize models for speed, memory usage, and deployment efficiency.

Responsible AI and Model Evaluation

This module introduces the principles of responsible AI, including bias detection, fairness evaluation, bias mitigation, model transparency, and AI governance. Learners gain practical experience in evaluating model fairness and creating model cards to document AI systems responsibly.

Serving, Deploying, and Monitoring Fine-Tuned Models

This module focuses on deploying fine-tuned AI models into production using FastAPI, Docker, and MLflow. Learners explore model serving, containerization, versioning, performance monitoring, drift detection, and lifecycle management to build reliable and maintainable AI applications.