Kaiser Permanente

Bank of America

Wells Fargo

Google

Crispus Roshan

AI Data Engineer @ Meta | Founder @ Stackle | Ex Google, BofA, Kaiser

Boost your Career in 60 Minutes: Data Engineering with AI

HSBC

Cris Roshan

Sr. Data Engineer | Tutor @ Stackle

Join our live demo and learn how data engineering works inside teams and why clean data powers AI.

How data engineering fits into the modern AI workflow

Learn the complete Data pipeline process as we build together. See how raw data turns into useful insights.

How real pipelines work in companies

See every step of a  data pipeline, from collecting data to cleaning, organizing, and loading it so teams can use it

Step-by-step guide to create a working Data Pipeline

You leave with frameworks for real organizational change, the kind you can start putting to work the week you get back.

3x CPTO | CEO, ChatPRD | Host, How I AI

LaunchDarkly

Color

Optimizely

Claire Vo

3x CPTO, Founder ChatPRD, and Host of How I AI. Expert on AI transformation.

Principal Engineer | Prev. Dir of Eng

Atlassian

Zach Davis

Principal Engineer who implemented AI across a 100+ person org at LaunchDarkly.

Cohort 3

Cohort 4

Everyone wants their team to work AI-native, but change doesn't come from buying licenses and individual heroics.🚨 Leaders: you must restructure your team operates or you will not be able to drive AI transformation beyond shallow tool adoption.This is a executive-level intensive for CPOs/CTOs, VPs, and staff level engineers, designers, and product managers, especially leaders being tasked with accelerating AI in your team.Taught by <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/clairevo/">Claire Vo</a> (3x CPTO/Host of How I AI) and <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/zach-m-davis/">Zach Davis</a> (Principal Eng @ LaunchDarkly), this course is by and for experienced EPD operators.After two days, you'll leave with data-backed assessments, specific strategies, technical examples, and an elite peer group for accountability and inspiration.100s of execs have taken the learnings from this course back to their teams.Here's what they love: "Clear frameworks, strong faculty, sharp peer group, and—most importantly—practical enough that I walked out with a plan for how to accelerate AI transformation in my org. Rare to find an exec course that respects your time and assumes you're already past the 101. Highly recommended."Join the next cohort to immediately accelerate your team's AI transformation.

Learn how to jump from AI tool adoption to team impact and product velocity.

Know where AI creates real leverage, and where it doesn't 

Prepare for and lead the hard conversations

Redesign your operating model for the full EPD org

Understand what makes a codebase ready for agents

Build a culture that actually adopts

VPs of Engineering and CTOs who need a real answer when the board asks "what's our AI strategy?", not just "we bought licenses."

CPOs, VPs of Product, and Design leaders driving AI change across the whole org as prototyping goes nearly free and roles blur.

Staff and Principal Engineers tapped to "figure out AI" for the org, who need both the playbook and the leverage to make it work.

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Principal Data Scientist @ Ontra 

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Head of Data @ Ontra AI | Ex-LinkedIn

LinkedIn

Pinterest

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Data Science Leader |Ex-Microsoft

Microsoft

eBay

Grammarly

Superhuman

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Cohort 6

As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship. Analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.-&gt; <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Join AI Builders Slack Community!!</a> &lt;-What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build end-to-end, metric specs, deep dives, experiment plans, stakeholder readouts.The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Don't let AI analytics turn your data team into the QA department

Co-founder @ AI Analyst Lab 

Co-founder @ AI Analyst Lab | Head of Data | Ex-LinkedIn, Pinterest, Meta

Co-founder @ AI Analyst Lab

Co-founder at AI Analyst Lab | Ex-Stripe, Nextdoor, PwC, Appfolio

Cohort 1

<blockquote><blockquote><blockquote>Most AI analytics rollouts start the same way. Someone connects an agent to the warehouse and announces self-serve. Week one is exciting. By week three, five teams have five retention numbers, every answer gets forwarded to the data team with "is this right?", and your analysts spend their week checking work they didn't do.</blockquote></blockquote></blockquote>The model was fine. What was missing was everything around it: modeled tables, written definitions, tests with known answers, and people who own them. In one vendor benchmark, the same model went from 54% to 100% accuracy once the definitions and business rules were written down.That work is organizational, so it lands on you. Who owns the definition of an active user? Who approves a KPI change? What do you tell legal, security, and finance? What do you promise your exec at day 30, and what would make you stop?This workshop is three hours on those questions. No code, no setup. We cover what enterprise AI analytics takes, the myths that sink it, how to govern metrics as a standing process, how to judge the tools you already own, and how to set a strategy with milestones your leadership will believe.You leave with a 90/180-day plan you started in the room.

Go from scattered AI experiments to a written plan, a governance process, and answers for every team that will push back.

Explain what enterprise AI analytics takes, and where rollouts fail

Govern metrics and context as a standing workstream

Judge your data foundations and the tools you already own

Write an AI analytics strategy your leadership will believe

Build a 90/180-day roadmap with milestones that mean something

Answer the pushback before it comes

Heads of data asked to "do something with AI." You own the numbers, so you'll own the wrong answers. You want a plan before a pilot.

Analytics managers whose team already checks AI answers. You need definitions, owners, and a process to stop the "is this right?" pings.

CTOs and engineering leaders weighing AI data tools. You want to know what to control, what must exist first, and when to buy new.

Learn to turn ambiguous customer problems into production AI systems, and become the engineer who can own outcomes end to end.

DeepTech Entrepreneur, Technology Leader

Oracle

Yatra

Apparel Group

ANKUR NARANG

Three decades building HPC and AI systems processing billions of transactions

AI & ML Leader, Venture Partner - DCX

Adda247

Hike

Mobileum

Kush Khurana

AI product and deep-tech builder, Visiting Professor at Ashoka University

BTech & MTech IIT Mumbai. AI Lead

Tata Consultancy Services

Unify

Madhusudan Kumar

BTech & MTech IIT Mumbai. AI Lead with solid Agentic Experience

AI companies don’t just need engineers who can build models or agents. They need people who can take an ambiguous customer problem, understand the workflow, design the right AI solution, integrate it with existing systems, deploy it reliably, and prove business value.That is the job of a Forward Deployed AI Engineer.Most AI courses teach you how to use a model, build an agent, or work with a framework. This course teaches you how to own the full journey from customer discovery to production deployment.You’ll learn how to work through messy data, legacy APIs, unclear requirements, security constraints, unreliable AI behavior, changing stakeholder needs, cost, latency, and adoption challenges.Through hands-on builds, architecture exercises, enterprise scenarios, and a capstone, you’ll learn to operate at the intersection of AI engineering, software architecture, customer discovery, product thinking, and business outcomes.The goal: become the engineer who can make AI actually work inside a real organization.

Become the AI engineer who turns real customer problems into production systems

Turn ambiguous customer problems into solvable AI opportunities

Architect production-grade enterprise AI systems

Build AI solutions that work beyond the demo

Evaluate, secure, and operate reliable AI applications

Own customer deployment, adoption, and measurable business value

Operate end-to-end like a Forward Deployed AI Engineer

Software, AI/ML, or data engineers who want to move from building components to owning end-to-end customer AI deployments.

Solutions architects, technical consultants, and implementation engineers moving into customer-facing AI and Forward Deployed roles.

Technical product managers, founders, and startup engineers who need to turn ambiguous business problems into deployable AI systems.

Boost your Career in 60 Minutes: Data Engineering with AI

What you'll learn

Why this topic matters

You'll learn from

Cris Roshan