Forward Deployed Engineering Discovery To Production

Run a discovery interview that surfaces the real problem, then write the one-page brief and out-of-scope list the engagement is measured against
Build agents and retrieval over messy permissioned customer data, and prove they work with an eval set written before you tune anything
Instrument an LLM system with OpenTelemetry, defend an SLO with an error budget, and pass an enterprise security and data-residency review
Deploy into a customer's VPC and hand over a system their team can run, then diagnose and fix the real barrier to adoption

Requirements

You can write Python and call a REST API. You do not need to be fluent
Basic Git and command line. Docker installed for the deployment section
An account with any major LLM provider — every lab fits inside a free tier
No machine learning background required. You will integrate and evaluate models, not train them

Description

This course contains the use of artificial intelligence.The demo went well. Six weeks later nothing is in production, the customer has gone quiet, and nobody can say exactly what went wrong.Closing that gap is the job. A forward deployed engineer embeds with an enterprise customer, works out what the real problem is, builds inside their environment and their constraints, and stays until the thing is running and used. Part engineer, part product manager, part consultant. The role pays what it does because very few people are all three at once, in front of a customer, under time pressure.This course teaches that work end to end. It is built around one project rather than a playlist of topics. In Section 3 you pick a messy document set you did not create — a mailbox export, a folder of scanned PDFs, a filings archive. You frame the problem, prototype against it, build an eval set, instrument it, take it through a security review, deploy it behind a private endpoint and hand it over with a runbook. Nothing restarts. By Section 11 you hold the two artifacts hiring managers actually screen for: a system that runs with monitoring and measurable quality, and a written brief an executive would read.The technical half is current. Workflows against agents and the five composable patterns. Retrieval over data with real permissions attached, not a tutorial corpus. Evaluation as a discipline — offline and online, LLM-as-judge and where it lies to you, trajectory against outcome for agents. OpenTelemetry tracing with GenAI span conventions. SLOs and error budgets. Prompt injection, excessive agency, and the OWASP and NIST vocabulary a customer's security team will use on you. Containers, rolling updates, and the private-networking patterns that decide whether a deployment is allowed to exist.The other half is the part engineers under-invest in and then lose deals over. How to run a discovery interview that surfaces what someone did last week instead of what they say they want. How to write a one-page brief with an explicit out-of-scope list — the section most people skip and the one that saves the engagement. How to demo the customer's before and after rather than your features. How to diagnose why a shipped system is not being used, and fix the actual barrier instead of running more training.Some honesty about scope. This is not a course on training models; you will integrate and evaluate them, not build them from scratch. It assumes you can already write Python and call an API, and it skips the fundamentals to spend the time on the things that are hard to learn from documentation. If you have never deployed anything, start elsewhere and come back.The course is twelve sections and eighty-seven lectures, and it moves in four movements. Sections 1 to 3 are discovery, scoping and deciding under constraint: how to interview an operator so they tell you what they did last week rather than what they want, the assumption log and interface catalogue, the shadow interfaces nobody documents, and the decision records that make a choice defensible six months later. Sections 4 to 6 are integration: HTTP 200 is not business correctness, idempotency at protocol and business level, the dual-write problem and the transactional outbox, timeout budgets, circuit breakers, and deploying into someone else's cloud behind a private endpoint.Sections 7 to 9 are the part most people think is the whole job — retrieval over permissioned data, agents and tool design, evaluation and observability. Sections 10 to 12 are the part that decides whether any of it ships: the security review, prompt injection and blast radius, adoption, handover, and a capstone that carries one engagement from discovery to handoff.Twelve of those lectures are hands-on, and each ships a downloadable bundle that runs on your machine with no cloud account: the code, the fixtures, a README, and a file showing the exact output a correct run produces. You will profile a deliberately messy customer extract and quantify what is actually wrong with it. Build an idempotent client with a dedup state machine and watch duplicates vanish. Run an outbox through a real SIGKILL mid-publish and prove nothing was lost or duplicated. Stand up retrieval over a corpus with permissions attached, then watch a naive retriever leak across a boundary and a filtered one not. Several of them break on purpose, because diagnosing the break is the lesson.Three ideas recur and are worth naming, because they are what the course is really about. First, that a demo which works proves almost nothing — the interesting question is what happens at the ninetieth percentile, on the worst input, when a dependency is slow. Second, that authority is a design decision: what a system may do, and what it may do without a human, are two separate grants, and collapsing them is why security reviews refuse things wholesale. Third, that shipped is not adopted, and the barrier is usually something you built rather than something the users lack.You will also leave with the two artifacts that get you hired. A system that runs, with traces and an eval suite and a number you measured before and after. And a written brief an executive would actually read. Section 12 is explicit about this: a screener spends four minutes deciding whether to book a call, and ten repositories give them nothing to hold on to, while a two-page engagement write-up with a measured outcome and six decision records naming the option you rejected gives them a sentence they can repeat.The evaluation material deserves a specific mention, because it is where most courses wave. You will write an eval set before you tune anything — twenty cases drawn from real queries, cases the team says are hard, and cases that should refuse because no answer exists. Then you will watch a change improve the headline score and make the system worse: abstention lowers raw correctness and is kept anyway, because a user who cannot tell which ten per cent is wrong pays the checking cost on all of it. Ninety per cent accuracy with no confidence signal can deliver zero time saved, and that arithmetic is the reason adoption fails more often than latency is.A word on who this is not for. If you have never shipped anything to production, the course will make sense and will not change what you can do — the material assumes you have felt the difference between code that works and code that survives a Tuesday. If you want to train models, this is the wrong course; you will integrate and evaluate them here, never build them. And if you are looking for framework tutorials, the frameworks change faster than any course can track, which is why this one teaches the constraints underneath them instead.On tooling and dependencies: the course teaches patterns rather than products. You will meet OpenTelemetry, OWASP and NIST vocabulary, containers and private networking, because those are what a customer's security team and platform team will speak. But nothing here is tied to a vendor you must buy, and every lab runs against local stand-ins so the material outlives whichever provider you are using this year.Every lab runs on a free tier. Every figure quoted in a lecture comes from code that ships with the course, and where a result was inconvenient it is reported as measured.
Software and ML engineers with a couple of years of production experience moving into a customer-facing role.,Solutions architects who want to ship the code rather than hand off the design.,Engineers at AI-native companies whose deployments keep stalling between the pilot and production.,Consultants and systems integrators building an enterprise AI delivery practice.

Homepage

https://www.udemy.com/course/forward-deployed-engineering-discovery-to-production/

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