Enterprise AI Engineering: From Idea to Production
Emilio Taylor
Build & Deploy AI Agents
Most AI tutorials stop at the demo. This course starts there.
Building an AI prototype has never been easier. Turning one into a capability an enterprise can safely own, operate, and scale is much harder.
The challenge doesn’t stop with prompts or models. Production AI requires architecture, data, security, workflow design, observability, governance, cost management, and clear ownership after launch.
In this course, we’ll follow an AI solution from idea to production—evaluating the opportunity, selecting the right solution pattern, building the experience, and designing the controls and operating model around it.
You’ll still build. But you’ll also learn to make the decisions surrounding the build: what belongs in AI, where boundaries should exist, how solutions graduate into production, and what happens on Day 2.
By the end, you’ll have more than a working AI solution. You’ll have a repeatable framework for taking AI from experimentation to an enterprise capability.
What you’ll learn
Learn to take AI from idea to production—designing, building, governing, and operating solutions the enterprise can safely scale.
Evaluate problems before choosing AI
Define measurable outcomes and constraints
Distinguish AI, automation, and traditional software
Select the right solution pattern
Define data, model, tool, and system boundaries
Design for security, identity, and integration
Turn architecture into a functional solution
Connect models, enterprise data, APIs, and tools
Use modern AI-assisted engineering workflows
Design failure, fallback, and retry paths
Add structured outputs, guardrails, and observability
Test reliability before release
Move solutions from experiment to approved capability
Apply controls appropriate to risk and scale
Define ownership, approval, and graduation paths
Establish Day-2 ownership and support
Monitor quality, usage, cost, and performance
Create reusable patterns that enable teams to build safely
Learn directly from Emilio
Emilio Taylor
Author of Vibe Engineering | Founder, Visao + Helix | AI Systems for RevOps
Who this course is for
Technology & AI leaders CTOs, VPs, directors, and technical leaders responsible for turning AI experimentation into production capabilities.
Architects & engineering leads Solution, AI engineering leads designing how models, data, apps, APIs, and enterprise controls fit together.
Engineers building enterprise AI Software and AI engineers who can build prototypes but need the patterns required for launch to production.
Prerequisites
Technical fluency with modern applications
You should understand how applications connect frontends, APIs, data, cloud services, and external systems. Deep expertise in any one stack
Basic understanding of AI and LLMs
You should be familiar with concepts such as prompts, models, APIs, and AI-assisted applications. We’ll build from these fundamentals into e
Willingness to work hands-on
This isn’t a strategy-only course. You’ll inspect architecture, work with code and configuration, troubleshoot systems, and build working AI
What's included
Hands-on enterprise AI build
Apply the concepts by designing and building a working AI solution throughout the course.
Enterprise AI blueprint
Leave with a reusable framework for taking AI from opportunity through architecture, production, and Day-2 ownership.
Templates & reference patterns
Get practical architecture patterns, readiness checklists, and decision frameworks you can reuse with your own teams.
Maven Guarantee
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Course syllabus
Week 1
Apr
12
Day 1 — Foundation + Setup
Week 2
Apr
19
Day 2 — Intelligence + Orchestration
Frequently asked questions
Maven for Teams
Reimbursement
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Everything L&D needs: email template, receipts, and certificate of completion.
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