Forward Deployed AI Engineering: Discovery to Production

About this course

This course contains the use of artificial intelligence.

Forward-deployed AI engineering is the discipline of turning an attractive prototype into a reliable system that changes how an organization works. This course follows that complete journey: discovery, scoping, architecture, agent design, enterprise context, orchestration, evaluation, security, deployment, governance, adoption, and sustainable ownership.

You will work through a continuous fictional enterprise case, NILE-TECH, and make the decisions a forward-deployed engineer must make in practice. You will learn when a deterministic workflow is safer than an autonomous agent, how to design tool and context contracts, how to recover from failures, when to require human authority, and how to prove that a release is ready.

The course is vendor-neutral. It explains durable patterns that apply across model providers and production platforms, while using current concepts such as structured outputs, MCP, retrieval, long-term memory, OpenTelemetry, NIST AI RMF, ISO/IEC 42001, and OWASP risks. The emphasis is not on calling an API once. It is on producing evidence that a system is useful, secure, observable, economical, and maintainable.

Across ten modules and a complete capstone, you will build a practical mental model for forward-deployed delivery. Each module ends with a knowledge check, and the NILE-TECH capstone brings discovery, architecture, implementation, evaluation, security, deployment, operations, and executive communication together.

By the end, you will be able to explain not only how an enterprise AI system works, but why its design is appropriate, what could go wrong, how it is controlled, and what evidence supports its release.

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