Deloitte

FlixBus

The Linux Foundation

Data Science Retreat

ODSC

Daniel Voigt Godoy

Amazon best-selling author, Instructor @ Linux Foundation/Data Science Retreat

Is Your LLM Task Worth Fine-Tuning?

CrossLend

Use a simple rubric to decide whether your workflow is worth a small open-weight fine-tuning test.

Score your LLM task for fine-tuning fit

Avoid wasted GPU time by learning when to tune behavior, when to retrieve knowledge, and when a better prompt is enough.

Separate fine-tuning from prompting and RAG

Identify narrow workflow steps where a fine-tuned smaller model may reduce cost, latency, or dependency.

Spot small-model opportunities

"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

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

May 7, 2022

Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).

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 

Build a full AI evals suite in 3 hours on a real use case

Founder @Lovelaice | 10 years in Product

Finastra

Trading Central

Printify

Madalina Turlea

Product leader with 10+ years in platform and SaaS products | Founder Lovelaice

Founder @ Lovelaice | 2x founder & CTO

Teleclinic

Nilo

SIXT Group

Freeletics

Catalina Turlea

2x founder & CTO | 14+ years in tech | Founder @Lovelaice

June

<blockquote>Whether you're building your first AI feature or exploring what AI could do in your product, there's a moment every PM hits: you look at the AI output and think "this seems okay?" but you don't have a structured way to know for sure.You've heard about evals. You know vibe checking isn't enough. But where do you actually start?This workshop takes you from that starting point to a working evaluation suit, built on a real AI feature use case, in 3 hours.You'll write a production system prompt, run it across multiple LLMs, read and annotate real AI responses to understand where they break, find failure patterns through error analysis, define automatic evals based on what you found, and measure whether your improvements actually worked.You'll leave with hands on experience on building your own evals suite, failure taxonomy, and a repeatable process to evaluate and improve any AI feature from here.Every participant works hands-on annotating AI responses, writing evals and running improvement iterations. We've run 1,500+ AI experiments with product teams and this workshop is the compressed version of what we've seen work.</blockquote>

Design, build, validate and iterate on an AI feature, hands-on

Understand the most important components of building AI features at scale

Build an evaluation suite to scale your testing

Learn how to identify failure patterns. Then iterate on both prompt and evals

The PM who wants hands-on experience on how to build high performing AI features

The PM who is overwhelmed by the technical tools and process around building AI

The product lead who wants improve the iteration speed or wants to scale manual testing with automatic evals

Shift from ad hoc AI efforts to disciplined, structured, workflow-driven collaboration before significant investment.

Workshop Lead: Netflix, Google, LinkedIn

Google

Netflix

Microsoft

LinkedIn

Paul Bryan

I help organizations design and develop AI products that deliver business value.

Cohort 1

July 2026 Cohort

<blockquote>AI applied to the wrong workflows leads to chaos. This course helps Enterprise teams launch successful AI pilots by aligning on workflows, roles &amp; responsibilities, risks, guardrails, and AI functional requirements before major investments.Industry reports suggest that over half of enterprise AI pilots never scale to production. Many pilots stall because the workflows were not ready for AI.This course addresses the real bottleneck: cross-functional alignment on key details before committing to technical debt.You will leave with:<ul><li>An AI Workflow Canvas: A structured way to identify where AI can move the needle in your organization</li><li>An AI Workflow Scorecard: A shared evaluation framework your cross-functional team can use to assess value, feasibility, data readiness, and risk</li><li>A Thin-Slice Prototype: Storyboards, scene cards, and an action flow diagram that aligns stakeholders across PM, DS, UX, Eng, and Biz on exactly what the AI system will do, feasibility and risk, and where humans are needed in the loop.</li></ul></blockquote>

Identify High-ROI Workflows Ready for AI

Identify high-friction workflows suitable for Enterprise AI

Prioritize AI initiatives using structured evaluation criteria

Clarify roles and decision rights before pilot launch

Surface feasibility and governance risks early

Make AI behavior visible to cross-functional stakeholders

Define readiness criteria before committing to a pilot

AI product team members (PM, UX, DS, ENG) who need a shared framework to align on workflows, feasibility, and responsibility before building

Product and business leaders accountable for Enterprise AI outcomes who must prioritize workflows and reduce risk before committing budget

AI governance, legal, and risk stakeholders who need structured ways to surface constraints, guardrails, &amp; ownership early in AI initiatives

Learn to achieve reliable AI outcomes without prompt packs, system prompts, or agent tricks—use structured inputs for consistent results.

Creator, AIgogy Framework™ | Advisor

Rockford University

Silali Banerjee

Creator of AIgogy Framework™ for reliable, structured AI outcomes

July2026Cohort

Pretty much anyone using an AI chatbot has gone through the frustration of a response that isn't exactly what they wanted. Problems include hallucinated, drifted, generic, and inconsistent output. Sometimes all of these happen; other times, one or more.People try prompt improvisation, prompt tweaking, prompt packs, prompt libraries, trial and error, tries and retries. Sometimes it works; other times, it does not.Too many details in a prompt cause prompt bloating and do not solve the problem. Adding context helps, but too much context creates problems.AI agents and skills do not solve this problem with open-ended work either.People give up, thinking LLMs are probabilistic and cannot work better.That is not true.It may never be as deterministic as systems in terms of consistency and reliability, but it can be mostly consistent and reliable.Similarly, users can create a path with an LLM using the right framework. Users can guide their request through the LLM toward their desired outcome by using the right framework.Learn to use this science-backed, proven framework.It is based on research (<a target="_blank" rel="noopener noreferrer nofollow" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618</a>) and has been tested in practice.<hr>

User's framework for moving beyond trial-and-error prompting with any LLM.

Achieve More Reliable AI Outcomes

Reflect Your Voice and Working Style

Embed Your Judgment and Standards

Reduce Rework and Increase Leverage

Leaders (Decision Owners) Leaders accountable for outcomes who need AI to reflect their judgment without reviewing or correcting every output

Operators (Execution Owners) Operators responsible for consistent delivery who need AI to produce aligned outputs without repeated rework

Knowledge Workers (Expertise Carriers) Experts who need AI to apply their domain knowledge and standards, not generate generic outputs.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Product managers who drive strategy, development, and launch of products.

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building, training, and deploying machine learning models — from classical ML to deep learning.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.