A10i-labs

Fine-tuning Small Language Models: dataset to deployment

Hamid Bagheri

AI Eng Leader, PhD CS | GenAI/LLMs | 20+ yrs software, data science, AI/ML

Write a dataset specification for a domain task, prompt templates, and instruction style.

Define the task, dataset spec, and prepare training data

1B to 7B model in Colab, locally, or a single GPU environment

Run a parameter-efficient fine-tuning

Quick offline eval, regression checks, and a simple acceptance rubric (quality, safety, latency, cost).

Evaluate and decide if the model is shippable

Stable outputs, input/output contract, and basic observability.

Deploy as base model and adapter with a production contract

Build AI-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more

AI Builder/Educator (6 Million Students)

Hugo Bowne-Anderson

AI & data engineer, consultant, educator of 6+ million students (ex-Yale)

AI Builder with 15 years of experience

Stefan Krawczyk

Agentforce; Ex-Stitch Fix; 13+ Years building / productionizing data, ML, Agents

Cohort 4

Cohort 5

Final cohort: 300+ engineers from Meta, Netflix, Amazon &amp; more have taken this course. Join them for the last run.If you’re a software engineer or data scientist, chances are you’ve built an impressive proof of concept (POC) that showcases the potential of LLMs.But these POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.”It doesn’t have to be this way.With the right principles, processes, &amp; workflows, you can build LLM applications that work in production, without overcomplicating pipelines or chasing trends. This course focuses on the iterative development, evaluation, and debugging practices that make AI systems production-ready.You'll build with state-of-the-art tools like RAG, fine-tuning, MCP, context engineering, and agents, but the emphasis is on building scalable, robust systems, ensuring you leave with the mindset and skills to adapt as the technology evolves.Learn how to design, test, &amp; deploy generative AI applications, and iterate rapidly.Create AI systems that solve real problems, deliver real value, &amp; scale with confidence.

Master the patterns and processes that underpin successful GenAI app development

Move beyond POCs and build production-ready AI systems

Build State-Of-The-Art AI Agents

Accelerate your development process with best-in-class "Evals" & LLMs Judges

Develop hands-on expertise by building GenAI and LLM apps

Showcase Tangible Results

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Automate and amplify institutional knowledge through AI agents that embody your cultural DNA

Cohort 17

Principal Director, Handshake

Lev Gorelov, MS

Co-founder and Principal Director of Handshake, a generative AI software studio

CEO, Handshake

Jeremy Kirshbaum

Founder of generative AI consultancy Handshake

Cohort 16

Cohort 18

The Problem: Your AI applications produce generic, surface-level outputs. You're solving complex, context-specific problems, but results don't reflect your organization's hard-won expertise.The Solution: We teach evergreen frameworks for building AI systems that learn from your unique data and workflows, with instructors available on-demand as your strategy and implementation consultants.What makes your organization valuable isn't raw information — it's the proprietary logic that mints insight from the data. We teach you to encode this logic into AI systems and scale your expertise authentically. You'll learn to orchestrate product development across domain experts, developers, and UX designers, transferring best practices into a product only your organization could build.We've spent half a decade embedding expertise into AI with knowledge-driven organizations like Visit California, Give.org, and Institute for the Future. We operated as strategic consultants, not just developers. For the price of a course, you're hiring us in the same capacity - consulting, scoping, iterating - to ensure you walk away with a prototype that solves real problems and a clear roadmap to production.

Transform Your Organization's Expertise Into AI-Powered Competitive Advantage

Encode Your Organization's Expertise Into AI Systems

Put Theory Into Practice With Support From Experienced Strategists and Builders

How To Build a Working Prototype You Can Actually Deploy

Lead Cross-Functional Teams

Cut Through the Hype and Identify Real Opportunities

Executive Leaders who need a framework to convert their company's domain expertise and workflows into a scalable, competitive AI asset

Project/Product Managers who need a framework to bridge domain knowledge with technical teams to scale expertise with AI

Consultants who want to guide clients in building differentiated, insight-driven and context-aware AI solutions

"Despite being a student in Harvard’s Master’s in Data Science program, I've struggled to understand the concepts that make LLMs so impressive. This class gave me the answers." - Tony "Ishan takes a black box, rips it apart to understandable compartments, and clearly communicates what is going on. Highly recommend." - Joe, CFO ______________________________________________________________________ The current cohort completed recording on January 17th, 2025 but you can still enroll to participate asynchronously: •&nbsp;Get lifetime access to all recorded sessions, including interactive Q&amp;A segments•&nbsp;Connect with peers in our dedicated private Discord community•&nbsp;Master the material through comprehensive quizzes and training resources•&nbsp;Receive direct instructor support via email or Discord whenever you need help•&nbsp;The option to attend a future live cohort for free______________________________________________________________________ Why This Course Stands Out: You already know how vital AI is to your career—it's in every conversation, project, and strategy session. But diving into AI feels daunting with limited time for self-study or complex programming. That’s where this course comes in. Skip the endless prep work and gain an intuitive, hands-on understanding of AI and transformers in just a few hours—without needing an advanced degree. No other course offers such an interactive, fast, and in-depth technical understanding of AI using an actual GPT-2 model, all using an easy-to-learn spreadsheet interface. ______________________________________________________________________ Perfect for Busy STEM Professionals:Whether you're a developer, technical manager, or executive, this course demystifies AI and teaches you exactly how Large Language Models (LLMs) like GPT-2 work, with no prerequisites beyond a STEM background. It’s ideal for: • Software/Web Developers seeking an easy introduction to machine learning. • Science &amp; Tech Professionals navigating AI transformations in their fields. • Technical Sales/Marketing crafting messaging for AI products. • Product/Project Managers overseeing LLM-powered solutions. • Executives/Leaders guiding AI strategy in their organizations. ______________________________________________________________________ What You’ll Learn:Using a unique spreadsheet implementation of GPT-2 (the predecessor to ChatGPT), we’ll break down each model component with crystal-clear explanations. In just a few sessions, you’ll be able to: • Understand how LLMs work—as quickly as possible. • Explain every key component—tokenization, embeddings, attention, and more. • Gain AI literacy—so you can confidently lead and manage AI-driven teams. • Build a foundation for further AI projects—and even more advanced learning. ______________________________________________________________________ Why Spreadsheets?You don’t need to be a coder or a calculus expert to understand AI—just the right approach. This course uses an innovative, no-code, spreadsheet-based method to teach LLMs. It's fast, interactive, and designed for busy professionals like you. By breaking down complex AI principles into easy-to-follow spreadsheet exercises, you’ll gain a lasting, foundational understanding of how large language models work. This is knowledge you can carry across into any model, any programming language, and any project. ______________________________________________________________________ Hands-On Learning, No Programming Required: By the end of the course, you’ll have a working, detailed understanding of how transformers power LLMs like ChatGPT, Claude, and Llama. Plus, you'll have a spreadsheet tool you can explore and tweak to deepen your understanding further. ______________________________________________________________________ See it for Yourself: Still unsure? Watch the FREE lightning lesson:"Inside the Brain of an LLM: How it Works in 30 Minutes" to see how accessible AI learning can be: https://maven.com/p/eeea6f/inside-the-brain-of-an-llm-how-it-works-in-30-minutes ______________________________________________________________________ Join the Community of Learners: • “I could’ve read 20 papers on this and not gotten nearly the understanding I got from this hands-on course.”— Class Participant from Adobe • “Highly recommend this course for anyone wanting to build a foundation in LLMs. Ishan’s interactive approach makes complex concepts incredibly clear.”— Matt, Business Intelligence Analyst ______________________________________________________________________ Practical Details: • Course Access: All sessions are recorded and available to stream after the course concludes, so you’ll never miss a thing.• Reimbursement: Your company may cover this course if you're building or transitioning into AI at work. ______________________________________________________________________ New for this cohort! We've dropped the requirement for Excel. We have a browser based alternative. Some exercises may use Google Sheets and/or Python.

Skip the mountains of prep work and learn how AI works in just a few hours

Course syllabus

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Tuesdays & Thursdays

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May 7, 2022

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Weekly projects

Course schedule

Executives, engineering managers, and product managers leading and coordinating technical AI teams leveraging LLMs

Developers without formal training in machine learning that want to quickly understand the LLM technology in their projects

A gentle introduction to LLMs for professionals pivoting their career to AI from traditional software engineering or other STEM fields

Anyone who can read a spreadsheet that is motivated and deeply interested in how the next wave of computing works

Who is this course for

What you’ll get out of this course

This course breaks down complex AI concepts into easy-to-follow steps using Excel, making it accessible for anyone with a STEM background.

Understand how LLMs work as quickly as possible

Precisely understand in detail the architecture and function of the GPT2 model, including tokenization, embeddings, attention, multi-layer perceptron, and layernorm.

Recognize and explain every component of an LLM

You'll gain a solid understanding of how LLMs function, enabling you to confidently engage with engineers and technical stakeholders.

AI Literacy for managing technical teams

Build a foundation that equips you with the essential knowledge to explore advanced AI topics, undertake AI projects, or pursue further coursework.

Prepare yourself for further exploration, projects, or coursework in artificial intelligence

Master AI Concepts with a Real LLM Implemented Entirely in a Spreadsheet. No Complex Math. Just Clarity. 

Jan 8—17, 2025 (On-demand)

Ishan Anand

Unlock a personalized learning experience.  Dates and times will be customized to the participant.

1-on-1 Premium Version 

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

February 2026 (please delete)

Feb 2026

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Product Manager AI&ML @ Google

Diego Granados

Senior Data, ML and GenAI Scientist

SHI International

National Association of REALTORS

George Zoto

Senior NLP/AI Scientist

December 2025

“Does it work?”... “Is it good enough?”... “Can we ship it?”...How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?How do you choose the right metrics, interpret fuzzy results, and make a confident decision?This course gives you a framework to do just that.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Eliminate uncertainty in shipping AI features

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

PMs leading AI features, growth, or platform initiatives

PMs who partner with ML teams and want to set evaluation standards

PMs who need to make clear “ship or hold” calls without doing the engineering