Forward Deployed Engineering Certification
Become a Forward Deployed Engineer (FDE), one of the most critical roles at companies like OpenAI and Anthropic. Embed with a business, deploy an AI system into its real workflow, make sure it's adopted, and bring the learnings back to your product.
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20 students per class, on average
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Forward Deployed Engineers (FDEs) make AI work in the real world
AI models demo perfectly in controlled environments, then break on messy corporate data, undocumented workflows and strict compliance. That last mile is where AI wins or dies. The AI companies setting the pace, like Anthropic, Palantir and OpenAI, send a new kind of engineer to own it: the Forward Deployed Engineer (FDE).
The Forward Deployed Engineering Certification turns strong engineers into AI Forward Deployed Engineers. We teach engineers the half of the job they don't have yet: the product depth and consulting skills to make AI customized, necessary and adopted inside someone else's business. That means diagnosing the real problem, embedding in the workflow, earning trust and access, building in their environment, driving adoption, and closing the feedback loop back to your product.
By the end, you'll graduate with a complete deployment dossier and a graded practical on a case you haven't seen: proof that you can take AI from a customer's problem to a system their team runs without you.
What you’ll learn
Diagnose the customer's problem and define the AI solution
Map sponsor, user and owner, find what the business will actually pay for, and define where your AI product lands and why.
Embed in the customer's workflow
Shadow the people doing the work across their systems, map the real path, and cut steps before you automate a single one.
Earn trust and access
An access boundary a security team will sign: identity, least privilege, human approvals, logging, kill switch and data egress.
Build and adapt the AI system in their environment
Connect one step to the customer's systems through a tool adapter, set ground truth before it acts, and keep a trace and an eval check an operator can re-run next week.
Drive adoption with a staged rollout
Move a team from shadow to supervised, measure cycle time, errors and rework, and write the incident path before go-live.
Handover and the feedback loop
A runbook, a named owner and an end date for the customer, plus the field learnings that make your core product better for everyone.
Who this course is for
Software, AI/ML and Data Engineers
who want to go from shipping features for a product team to owning an AI deployment inside a customer's business, end to end.
Solutions, Implementation and Integration Engineers
and technical consultants who already sit with customers and want to move from configuring and advising to building and shipping production AI in the customer's environment.
Technical PMs and Founders
who will lead AI deployments, or want the Forward Deployed PM path, and can meet the technical bar.
Prerequisites
This is not an entry-level course
You should have shipped code to production and work comfortably with Git and GitHub. This is not an introduction to LLMs, RAG or Python.
Advanced AI knowledge expected
You should already point a coding agent like Claude Code, Cursor or Codex at a real repo, and know how LLM applications work: prompts, tools and evals. Module 4 runs on your own machine, so you'll need Python 3 and a coding agent on a paid plan or with API credit.
No other certification is required. The course uses three ideas it doesn't re-teach: the Agent Line and the Trust Ladder from Agentic Workflows & Loops and evals as the spec from AI Evals. If they're new to you, start there. If you've never pointed a coding agent at a repo, take Claude Code for PMs first.
It is FOR YOU if you ...
want to be the engineer who makes AI work inside a real business, and the one your product team listens to.
Not sure if this certification is right for you?
Schedule a callWhat's included
Live sessions
Live build labs with your AI Product Leader.
Course materials
Full access to materials and recordings, anytime.
AI tools access
$50k+ worth of AI tools, included with Unlimited Membership.
Instructor feedback
Direct, live feedback from your Silicon Valley instructor.
Workflows & templates
The most popular AI tool workflows, templates, and prompt packs.
Certification
Industry-recognized credential on completion.
Reimbursement kit
Support and resources to get your employer to cover the cost.
Alumni community
Exclusive access to a network of 10k+ alumni.
Unlimited membership
$416
$466/mo, billed annually
- Access to all 12 live instructor-led courses
- Weekly live AI workshops
- Free access to $50k+ worth of AI tools
Buy individual course
$2,999
Forward Deployed Engineering Certification Curriculum
01Diagnose the customer's problem and define the AI solution1 live session1 lab1 artifactHook: "What does this business actually need, and where does AI earn its place?"Show Details
Pre-work: create your repo from the course template and open the Marlow case
Map sponsor, user and owner, then score the engagement on five fit tests
Engagement Diagnostic Lab: interview Dana, the sponsor, then score the fit test
Fit Test: five tests scored from the customer’s files, then a one-paragraph go or stop recommendation, before anyone writes code.
Session recording
02Embed in the customer's workflow1 live session1 lab1 artifactHook: "What does the work really look like when you sit inside it?"Show Details
Map the real path across five systems and score each step on five criteria
Workflow Redesign Lab: interview Priya, the relationship manager, then cut the path
Workflow Cut: every step marked keep, delete or human, before a model is named
Session recording
03Earn trust and access1 live session1 lab1 artifactHook: "What will the security team sign, and what should the AI never do?"Show Details
The Agent Line: which decisions an AI system may own
Identity, least privilege, human approvals, logs, kill switch and data egress
Access Boundary Lab
Access Boundary: the irreversible action left off the tool list, a named signer, and an off switch that doesn't need the model's consent
Session recording
04Build and adapt the AI system in their environment1 live session1 lab1 artifactHook: "What's the smallest system that changes how this team works next Tuesday?"Show Details
Evals are the spec
Ground truth before an open agent, one system of record, a trace an operator can re-run
In-Environment Build Lab: your agent drafts a credit file section via a five-tool adapter
Eval check: run the checker on your draft and its trace, pass or fail
Thin Deployment: one working step, its full trace, and a check anyone can re-run
Optional extension between sessions: wire one read-only tool to a system you can reach at your own company
Session recording
05Drive adoption with a staged rollout1 live session1 lab1 artifactHook: "How does a team that never asked for this come to rely on it?"Show Details
The Trust Ladder
Shadow, assisted, supervised: measure cycle time, errors and rework, not tokens
Rollout Ladder Lab
Rollout Ladder: current rung, the metric for the next one, a one-page incident path
Session recording
06Handover and the feedback loop1 live session1 lab1 artifact1 graded practicalHook: "Can the business run it without you, and what should your product learn?"Show Details
The handover pack, the end date, and the platform note that closes the loop
Handover Lab
Handover Pack: runbook, named owner, checks, access review, kill switch, end date, platform note
Sponsor readout: ten minutes in pairs, with a peer or an AI playing Dana, the sponsor.
Graded practical: a case you haven't seen, written by you in 40 minutes in the final session
Session recording
07Final project: run the Marlow engagement end-to-end6 artifacts1 repo1 practicalCertificationShow Details
Your repo: all six artifacts assembled into one deployment dossier
Forward Deployed Engineering certification review, graded by your instructor
Sponsor readout of your handover, spoken to a peer playing Dana
If your employer allows it, repeat the six files on a real workflow in a second private repo between sessions.
Certificate issued on pass
Upcoming start dates
Select the cohort that fits your schedule to reserve your seat. You’ll choose your first class now, but you can confirm your final plan at checkout.
December 2026
1 cohort
Forward Deployed Engineering
Dec 1 – Dec 17
Tuesdays and Thursdays
10:00 AM – 12:00 PM
(America / Los Angeles)
January 2027
1 cohort
Forward Deployed Engineering
Jan 23 – Feb 6
Saturdays
7:30 AM – 12:00 PM
(America / Los Angeles)
Nothing that works? Join the waitlist or request a private cohort
Explore our flexible plans
Single Certification
Ideal for: Focused learners who want to master a specific skill
$2,999
- 1 live, instructor-led certification course
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Ideal for: Product managers ready to build broad expertise across multiple disciplines
$3,999
/per year
- 1 live, instructor-led certification course
- On-demand access to all courses
- Free access to $50k+ worth of AI tool
- Weekly live workshops
Unlimited Membership
Ideal for: Product professionals seeking full career transformation
$4,999
/per year
- 12 live, instructor-led certification courses
- On-demand access to all courses
- Free access to $50k+ worth of AI tool
- Weekly live workshops
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Upfront payment discount
Pay for your education upfront and get a discount.
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Enroll today and spread your payments over time with region-based installment options. Plan availability and terms vary depending on your country.
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Use your company’s budget for professional development or continuing education to fund your education with us.
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Enjoy 10% discount on single certifications courses for groups of 3 to 10 people from the same company. Discount available only for unlimited membership
Hear it from our alumni
"The biggest shift was getting comfortable turning rough ideas into working prototypes — actually shipping things, not just planning them."
Vikrant Shukla
Sr Product Manager at Salesforce
Thank you Product School for helping bring genAI concepts down to earth. We covered technical basics such as RAG and embedding and tokenization and Product-focused ideals such as defensive and offensive AI strategies.
Deepak Ravi
Product Manager, AI & Data at New York Life
"Through the certification, I learned a lot about AI, myself, and how to keep current with the rapid changes in the industry. No doubt that I will be a better product leader, and I look forward to building our AI PM community."
Josh Gray
Global Monetization Product at Tiktok
"This program strengthened my understanding of how successful AI products are built — not just with strong models, but with the right balance of workflow integration, trust, evaluation, and operational impact."
Debolina Das
Sr Product Manager at Amazon
"It was the right time to pick up this course when AI tools like Claude Code are changing how PMs work — and this program gave me a practical, hands-on foundation from vision to launch."
Clinton Gonsalves
Product Manager at JPMC
"Product School helped me develop critical product skills to master my job and get a product promotion. The case studies and real examples shared by my instructor helped me put into practice the frameworks that I learned and drive impact in my work."
Paulina Mwangi
Sr Director of Product at Salesforce
"Claude Code gave me a better overview of the entire process — system, roadmap, strategy — and freed up mental space to focus on the quality of the work."
Jenny Kan
Product Strategy Lead at ServiceNow
"Product School helped me learn the skills that I needed to break into product, and now I work on Product Strategy for Google Play."
Shilpi Verma
Product Lead at Google
"This certification covers everything you need to know about Product Management to jumpstart and land your first PM job. It isn’t all theory, you get to apply everything you learn to your own project and the instructors provide feedback to help you improve."
Medha Ghatikesh
Product Manager at Meta
Train the whole product org
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Group discounts starting at 3 seats
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Team progress reporting for managers
Frequently asked questions
What is the Forward Deployed Engineering Certification?
Product School's Forward Deployed Engineering Certification is a live, instructor-led FDE course that trains experienced engineers to become AI Forward Deployed Engineers. In six live sessions (12 hours) you run a full customer AI deployment, from diagnosing the problem to handing over a working system, then prove it on a case you haven't seen in a graded practical.
What is a Forward Deployed Engineer (FDE)?
A Forward Deployed Engineer (FDE) is a software engineer who embeds inside a customer's organization to build, integrate and ship production-ready AI on that customer's own data and infrastructure. The role was pioneered by Palantir and is now one of the most critical roles at AI companies like Anthropic and OpenAI.
What does a Forward Deployed Engineer do?
Forward Deployed Engineers work inside the customer's team and systems, map the workflows off-the-shelf AI can't handle, and build production AI that fits them. They also act as the feedback loop, bringing what they learn in the field back to their own product team.
How is a Forward Deployed Engineer different from a software engineer or a product manager?
A software engineer builds one capability for many customers; an FDE makes the product work for one customer, end to end. A PM decides what to build for the whole customer base; the FDE brings back the real-world signal that tells the PM what belongs in the core product. Some companies blend the two roles into a Forward Deployed PM. An FDE trained here speaks the same language as the PMs Product School already trains.
How do I become a Forward Deployed Engineer?
Most Forward Deployed Engineers start as software, AI or solutions engineers and add the skills the role demands beyond coding: diagnosing customer problems, embedding in workflows, passing security reviews, driving adoption and feeding learnings back to the product. Product School's Forward Deployed Engineering Certification teaches those skills through a full customer engagement and a graded practical.
What skills does a Forward Deployed Engineer need?
Strong engineering plus product and consulting skills: customer problem diagnosis, workflow mapping, security and access design, building AI on a customer's systems and data, staged rollout and adoption, and handover. The certification covers all six, one per module.
Why is the Forward Deployed Engineer role growing so fast?
AI's last-mile problem. Models that demo well break on messy data, undocumented workflows and strict compliance, and customer data often can't leave the customer's environment, so companies send the engineer to the data.
Is there a Forward Deployed Engineer certification?
Yes. Product School's Forward Deployed Engineering Certification is a live, instructor-led FDE certification. You earn a shareable credential after passing an instructor-graded final project and a graded practical.
Do I need to be an engineer to take this course?
It's built for engineers who have shipped code to production. Technical PMs and founders are welcome if they've shipped production code too and already use a coding agent on a real repo. If that's not you yet, start with Claude Code for PMs or Vibe Coding.
Which tools and AI models does the course use?
The course is tool-agnostic. You bring the coding agent you already use, such as Claude Code, Cursor or Codex, and the methods apply to any frontier AI model. Modules 1 to 3, 5 and 6 work in the browser on GitHub. Module 4 needs a clone of your repo, Python 3, and your coding agent on a paid plan or with API credit.
What will I build in the Forward Deployed Engineering Certification?
A deployment dossier for Marlow, a regional bank, in your own GitHub repo. Six files: a fit test, a workflow cut, an access boundary, a thin deployment with its trace and eval check, a rollout ladder and a handover pack. Along the way you interview Dana, the sponsor, and Priya, the relationship manager, and in the last session you present your handover to a peer playing Dana. Then you write a 40-minute graded practical on a case you haven't seen.
How long is the Forward Deployed Engineering Certification?
Six live sessions of two hours each, 12 live hours over about three weeks, with labs done in class.
Can I use my own company's workflow?
Yes, and if you're employed, we recommend it. In class everyone works on Marlow, so the room shares one case. Between sessions you can repeat the same six files on a workflow at your company, in a second private repo, and use that story in your readout. If you can't use a real workflow, Marlow is your project. The graded practical is the same for everyone.
How much does the Forward Deployed Engineering Certification cost?
$2,999 as a single certification, or included in the Unlimited Membership with access to all live certification courses.
Which courses should I take before this one?
None is required. The course assumes three ideas from other certifications: the Agent Line and the Trust Ladder from Agentic Workflows & Loops, and evals as the spec from AI Evals. If those are new, take those modules first. If you haven't used a coding agent on a real repo, start with Claude Code for PMs.