AI Engineering & MLOps Build Production-Ready AI Systems

Learn AI Engineering, MLOps, LLMOps, RAG, vector databases, deployment, monitoring, optimization, and AI security.
What you'll learn

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Understand the core concepts of AI, machine learning, generative AI, and large language models from an engineering perspective.
Clearly explain the differences between DevOps, MLOps, and LLMOps, and understand where each fits in modern production systems.
Understand how production AI systems are designed, including model serving, inference, scalability, reliability, and infrastructure concerns.
Learn the fundamentals of prompt engineering, including system prompts, few-shot prompting, structured prompting, and common prompt design mistakes.
Understand prompt caching, latency optimization, cost optimization, and the practical trade-offs involved in running AI systems efficiently.
Learn how embeddings, vector databases, and retrieval pipelines work, and understand the foundations of RAG systems and why many RAG implementations fail.
Understand model quantization, inference efficiency, and the trade-offs between accuracy, memory usage, speed, and infrastructure cost.
Identify realistic AI use cases in software and DevOps environments, and understand how AI can be integrated into real-world enterprise systems.
Build a clear mental roadmap for transitioning from DevOps into AI Engineering, MLOps, or AI platform roles.
Requirements

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No prior AI or machine learning experience is required.
A basic understanding of IT, software, or DevOps concepts will be helpful.
Familiarity with topics such as Linux, cloud, containers, CI/CD, or Kubernetes is a plus, but not mandatory.
Description

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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 teachesAI 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 forDevOps 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.
By the end of this course, you will have a clear foundation in AI Engineering, MLOps, and LLMOps. You will understand the main components of production-ready AI systems and how they work together. You will also gain a practical mindset for thinking about reliability, scalability, cost, security, monitoring, and continuous improvement in modern AI applications.
This course is a strong starting point for anyone who wants to move toward AI Engineering, strengthen their technical career, or understand how modern AI systems are built and operated in production.
Who this course is for

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Technical professionals who want a clear and practical understanding of concepts such as LLMOps, RAG, embeddings, prompt engineering, model quantization, and AI infrastructure.
Homepage

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