AI Engineering & MLOps: Build Production-Ready AI Systems

About this course

This course contains the use of artificial intelligence. AI is no longer only about building or using models. In real companies, AI systems must work inside production environments. They need APIs, data pipelines, vector databases, RAG architectures, prompt design, monitoring, logging, security controls, deployment processes, cost tracking, and reliable infrastructure.

This course teaches AI Engineering and MLOps from a DevOps perspective. It is designed to help you understand how modern AI systems are planned, designed, deployed, monitored, optimized, and secured in real-world environments also

Throughout the course, you will learn the core foundations of artificial intelligence, machine learning, MLOps, LLMOps, prompt engineering, prompt caching, embeddings, vector databases, RAG systems, model quantization, AI infrastructure, observability, AIOps, AI security, governance, and production risk management.

The focus of this course is not deep mathematics or academic model training. Instead, the course focuses on practical engineering thinking. You will learn how AI applications are structured, why many AI projects fail, how latency and cost should be considered, why monitoring is different for AI systems, how RAG systems can be improved, and why security and governance are critical for production-ready AI.This course is especially useful for DevOps Engineers, Cloud Engineers, Site Reliability Engineers, Platform Engineers, Backend Engineers, Software Engineers, and technical professionals who want to understand AI systems from a production and infrastructure point of view.

If you already have experience with cloud platforms, Kubernetes, CI/CD, monitoring, logging, infrastructure, APIs, or production systems, this course will help you connect those skills with the AI world. You will see how your existing DevOps and engineering background can become a strong advantage in AI Engineering and MLOps.

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