Software engineering practices for deploying and operating AI systems.
MCP explained for AI engineers: architecture, clients, servers, tools, resources, prompts, transports, authorization, security, real-world use cases, and how to build your first MCP integration.
AI agents explained from the ground up: how agents plan, use tools, manage context, take actions, handle failures, and how developers can build reliable agentic AI systems.
Machine Learning Engineer vs AI Engineer explained clearly: responsibilities, skills, tools, projects, career paths, and how to choose the right role.
A step-by-step AI engineer guide covering programming, ML, deep learning, LLMs, projects, deployment, and career preparation.
A practical AI engineer skills guide covering software engineering, ML, LLMs, RAG, evaluation, deployment, systems, and problem solving.
Learn what AI engineers do, the skills they need, what they build, and how to start a practical AI engineering career.
Learn how AI engineers build production systems with models, APIs, data, RAG, evaluation, deployment, security, and observability.
Understand AI, machine learning, deep learning, and generative AI, then follow a practical path to build a strong AI foundation.
Learn how LLMs work, including tokens, transformers, inference, RAG, fine-tuning, evaluation, agents, and production LLM engineering.
Learn machine learning from fundamentals to projects: models, evaluation, overfitting, algorithms, Python, and a practical learning path.
