Shreyans Bhansali 

Build an AI powered recommendation system (in one hour)

Google

Venmo

Socratic

Shreyans Bhansali

Co-founder / CTO at Maven. Formerly Google and 1st engineer at Venmo.

Transform any type of data into powerful representations that capture deep semantic meaning and relationships.

The magic of vector embeddings

Learn how to compare apples to oranges - discover techniques to meaningfully relate users, content, products, and more.

Connect different data types in a universal space

Take your knowledge from theory to practice by implementing a real recommendation system that can scale and adapt.

Build a production-ready recommendation engine

Learn the latest AI Search skills with the authors of "AI-Powered Search"

AI Search Expert | Founder | Author

Lucidworks

Presearch

Optimized AI Conference

CareerBuilder

Searchkernel

Trey Grainger

Founder, Searchkernel. Author, "AI-Powered Search". 18+ years leading search.

Principal Search Engineer | Author

Shopify.com

OpenSource Connections

Reddit

Wikipedia

Yelp

Doug Turnbull

Principal Search Engineer, Author of "AI-Powered Search" & "Relevant Search".

March-2026

July 2026

AI Search is among of the most in-demand AI Skills. AI systems need RAG, and implementing great retrieval can often have a bigger impact than prompt engineering for improving your AI and Agentic systems. Most search systems barely scratch the surface of what's possible. While others struggle with irrelevant results and frustrated users, you'll master the cutting-edge techniques that power the world's most successful search experiences—from Reddit and Spotify to the Fortune 500.Why This Course ExistsThe Problem: Many traditional search systems are stuck in the 2010s. But modern AI courses hyper focus on text embeddings and ignore many of the fundamentals of information retrieval. The result? Engineers who can't bridge the gap between amazing demos and production systems that actually work.Our Solution: Stop downloading random embeddings from Hugging Face or OpenAI and hoping for the best. Learn to systematically analyze your users' actual intent, integrate domain context, and build search that truly understands what people (and agents) need—not just what they type. This course helps you build cutting-edge AI Search systems that actually work.Ready to level-up your AI Search skills?

Level up your Agentic Search & Retrieval skills with leading AI Search experts

Optimize your Agentic Search and Retrieval Augmented Generation (RAG) systems

Go beyond the query to understand intent

Fill in holes in your modern information retrieval knowledge

Improve relevance with crowdsourced ranking models built from click-stream data

Optimize AI-Powered Search Models for Production

Build ranking features & automate judgements with click models & Active Learning

Examples will be taught in Python, it's a good idea to have basic Python familiarity under your belt

Basic Python Programming

For those new to search/retrieval, we provide an optional intro session covering core matching & ranking concepts (BM25, vector spaces, etc)

(Included Complimentary with the Course): Search Fundamentals

AI Engineers optimizing search ranking to measurably improve user search satisfaction

Search Practitioners that want to learn to systematically improve search relevance

Technology leaders hoping to plan search experiments to maximize clicks and conversions

Learn to design and evaluate a production-ready media recommender: metrics, architecture, cold start, and drafting an effective roadmap.

Principal ML Engineer @ Disney, ex-Meta

The New York Times

The Walt Disney Company

ESPN

Columbia University

Katerina Zanos

Principal ML Engineer @ Disney, x-Meta, x-NYT

Cohort 1

Cohort 2

Build a production-ready Recommender System Blueprint for a media surface of your choice. This will be an artifact you can take back to work to guide implementation, align stakeholders, and make smarter roadmap calls.Most teams working on recommender systems don’t struggle because they lack algorithms. They struggle because they lack a practical, end-to-end playbook for shipping.You might know the building blocks (embeddings, retrieval, ranking, bandits), but still feel stuck on the hard parts:<ul><li>which metrics actually reflect what product wants</li><li>how to architect the system from logging to serving</li><li>what model to choose under real constraints</li><li>how to handle cold start and sparse data</li><li>and how to prove ML is worth it over heuristics</li></ul>I’ve built recommendation systems in production across news, feeds, and sports over the last 10 years (NYT, Meta, ESPN). This course turns that experience into a clear, reusable process you can apply to your own product without needing FAANG-scale infrastructure. By the end, you’ll be able to confidently design and evaluate production-ready recommenders, defend trade-offs, and use your Blueprint as a durable reference for future iterations.

Design and evaluate a media recommender system that ships to production.

Build a real-world mental model of recommenders (not just algorithms)

Design metrics and success criteria your team will actually align on

Architect the system: make the right calls on retrieval, ranking, and serving

Handle cold start with pragmatic strategies

Know when ML beats heuristics and how to prove it

You are able to interpret (not compute) common product metrics like retention/engagement and discuss tradeoffs.

Metric + product reasoning at a basic level

You can follow an architecture diagram and understand concepts like services/APIs, latency, reliability constraints.

Ability to follow system-level thinking

We will be working with specs, diagrams, and decision frameworks (not code).

Comfort making structured decisions from imperfect data

Senior ML Engineers &amp; Data Scientists who can build models but want sharper judgment on what to build next and how to defend it

Software Engineers transitioning into ML who want to think in systems + measurement, not just training code.

Tech Leads who need an end-to-end playbook to review designs, align metrics with product, and avoid expensive detours.

Build concept prototypes to drive faster decision making cycles 

Principal PM at Yelp, ⭐ 4.9 on Maven

eSpark

Codeverse

Priya Mathew Badger

Principal PM for Yelp's AI Assistant, Lenny's Community Top 25 contributor 

Learn how to build functional prototypes to drive faster feedback cycles

AI prototyping is the #1 skill PMs need to learn, but most are still learning how to apply it to their product work.As a Principal PM at Yelp, I built repeatable AI prototyping workflows to explore ideas faster and drive leadership buy-in.<a target="_blank" rel="noopener noreferrer nofollow" class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://www.youtube.com/watch?v=wDA6DslBeqk">See my How I AI episode with Claire Vo →</a><h2>The Program</h2>This one-day live workshop takes you from ad hoc experimentation to building prototypes that drive better and faster product decisions.Get hands-on with AI prototyping as a core discovery skill: turning rough ideas into functional prototypes and driving faster feedback cycles. You leave with a working prototype that you can use for the next idea on your roadmap or job interviews."This was a very valuable course that not only teaches the basics of prototyping that you can immediately apply, but also instills the right mindset to work with the AI tools." — LK, Staff PM at Pinterest ⭐⭐⭐⭐⭐<h2>What's included</h2><ul><li>4 hours of live instruction with repeatable workflows</li><li>Experience prototyping with multiple tools (Claude, Magic Patterns, and Google AI Studio) </li><li>20+ learning modules and custom Skills you keep for reference</li><li>Lifetime recording access</li><li>Private AI Prototyping Slack community</li><li>1 free month of Magic Patterns </li></ul>

You will leave with a prototype you can use for team alignment and user feedback

How to turn rough ideas into functional prototypes using AI tools

Get a repeatable workflow you can run for any roadmap area or interview process 

Use prototypes to drive faster internal alignment and leadership buy-in

Individual Contributor PMs looking to master how to build functional prototypes and use them to get faster feedback and alignment

Product leaders (GPM/Director/VPs) interested in leveraging AI prototyping for vision and team upskilling

Job Seeking PMs who need to learn prototyping for interviews with AI-first product companies

Master a proven system to create clear, persuasive presentations in half the time using AI as your strategic thinking partner.

Ex-JPMorgan VP | Presentation Expert

JPMorgan Chase & Co.

Commerzbank

PwC UK

MaryBeth Hazeldine

Presentation psychology expert with 35 years training Fortune 500 professionals

Cohort 4

Cohort 5

You know presentations matter. They shape decisions, win clients, and move careers forward. But creating them? That's exhausting.You spend hours wrestling with structure, rewriting messaging that doesn't land. By the time you're done, you're not even confident it will work.AI promises to help, but most people end up with generic outputs and robotic messaging. The tool saves time on the wrong things.Here's what's needed: a strategic approach combining AI efficiency with presentation psychology—the clarity, structure, and persuasion that makes people act.This course teaches exactly that. You'll use AI not as a shortcut, but as a thinking partner. You'll learn frameworks like AVP (Action-Value-Proof) and messaging psychology that transform unclear ideas into compelling narratives.You'll build presentations faster—yes. But more importantly, you'll build them better. Clearer structure. Stronger messaging. More impact.By the end, you'll have a repeatable workflow that cuts creation time in half while dramatically improving quality. No more staring at blank slides. No more second-guessing. Just confident, persuasive presentations—every time.

Master AI-powered presentations that persuade, save time, and win decisions

Structure presentations that are clear, logical, and impossible to ignore

Craft persuasive messaging that resonates and drives action

Build a repeatable AI workflow that saves 10+ hours per week on presentations

Turn data and complex information into stories people actually understand

Professionals who create presentations regularly and want to save hours while delivering clearer, more persuasive, impactful results

Managers, consultants, and client-facing roles who need to influence stakeholders and communicate complex ideas with confidence

Ambitious professionals who want to stand out by mastering communication skills that position them as clear, strategic business leaders

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

Introduction: Building Recommendations with Embeddings

Understanding Embeddings: A Simple Analogy

Technical Setup: Postgres, Python, and PgVector

Step 1: Creating and Storing Course Embeddings

Application 1: Finding Similar Items

Application 2: Building a Semantic Search Engine

Application 3: Generating User Recommendations

Improvements, Limitations, and Trade-offs

Q&A: Solving the Cold Start Problem

Q&A: Incorporating Explicit User Feedback

Q&A: Handling Negative Signals in Vector Space

Q&A: System Drawbacks and Final Thoughts

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.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

Build an AI powered recommendation system (in one hour)

What you'll learn

Why this topic matters

You'll learn from

Shreyans Bhansali