Cohort 1

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

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

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

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

You’ll learn how to design, evaluate, and ship reliable AI systems, becoming a PM who can lead beyond demos.

Ex-Google | Prof UCLA & UMN | Founder

Google

Walmart

Gallup

UCLA

University of Minnesota

Hamza Farooq

Founder | Ex-Google |
Adjunct UCLA & UMN, SCU | Venture Partner

AI Product Builder | Startup Advisor

Aishwarya Ashok

Agentic AI for Product Managers

Master the what, why, and how of Generative AI by building Enterprise-Grade Agents that go beyond the basics and actually move the needle

Coursera

We’re living through one of the most transformative periods in the history of computing — and Generative AI is at the heart of it. From content creation and personalized customer support to autonomous decision-making and intelligent search, GenAI is rapidly redefining how businesses operate, how knowledge is accessed, and how software is built. This course is roughly divided in two parts: 1. No Code tools for building AI Agents2. Low code approaches for building more complex Agents You've likely experienced this firsthand: vendors promising AI solutions that will transform your business overnight, countless products with overlapping capabilities, and the nagging question: "Which of these actually solves our unique problems?" Our startup has worked with organizations across fintech, supply chain, consulting, and manufacturing sectors, and we've consistently encountered the same challenge: businesses know AI matters, but struggle to identify concrete use cases that deliver measurable ROI. This course addresses precisely that gap—helping you cut through the noise to identify, build, and deploy GenAI applications that create actual business value. Building GenAI Agents for Enterprise Use is designed to give you a laser-focused and in-depth view of real-world GenAI applications. Designed for Business Leaders, Not DevelopersWhat sets this course apart is our relentless focus on the "what" rather than just the "how." While many courses dive directly into technical implementation, we prioritize use case identification, business value assessment, and strategic deployment considerations that matter to non-technical leaders. You don't need a programming background to succeed here. For business professionals who want to understand the capabilities without getting lost in the code, we've designed an experience that emphasizes concepts, applications, and outcomes. This course is one of the top-rated Gen AI Course on Maven, with over 1000 professionals trained from companies across tech, finance, government, and healthcare. It has been successfully offered at Stanford Continuing Studies and the UCLA Anderson School of Management for their Master’s in Business Analytics programs. Designed for Executive leaders, Data Scientists, Machine learning engineers, and Product Managers, the course demystifies the Gen AI landscape by blending deep technical concepts with practical enterprise deployment strategies. This is not another LangChain how-to or framework tour. Instead, this course teaches you to build modern GenAI systems from first principles, grounded in real-world constraints like scale, guardrails, multi-agent workflows, and domain-specific adaptation. 👤 Who This Course is For This course is designed for: - Product Managers looking to integrate GenAI into their roadmaps with clarity and confidence - Technical Leaders and Architects who want to build scalable and safe GenAI systems - Startup Founders &amp; Innovation Teams aiming to create competitive GenAI-powered products - Data Scientists &amp; ML Engineers who want to go beyond notebooks and deploy impactful GenAI apps - Consultants &amp; Enterprise Strategists exploring GenAI adoption and implementation strategies for clientsAnyone who wants to learn how Gen AI can be used 📚 Course ModulesModule 1: The World of Generative AI — Why Leaders Must Understand ItUnderstand the GenAI revolution and what it means for industries, infrastructure, teams, and the future of software. We break down the LLM ecosystem, the commoditization of models, and the real moats: data and architecture. Module 2: Building Blocks of no Code AgentsWhy do most enterprise search and Agentic solutions disappoint users? Discover the fundamental components that transform basic document search into contextual intelligence systems. Get hands-on with embeddings, chunking strategies, and the architecture of RAG systems that actually return relevant information. Connect RAG to Agents to interact with end users Module 3: Enterprise RAG with Guardrails—Moving Beyond PrototypesWhy do systems that work in demos fail in production? Learn how to transform proof-of-concepts into enterprise-grade solutions with semantic caching, optimized inference, and essential safety controls. Implement trustworthy systems that match your organization's risk tolerance and compliance requirements. Module 4: Agents and Their Ecosystem — Vertical AI AgentsWhat are agents really? Go beyond CrewAI and Autogen to build your own agent orchestration framework using AgentPro. Focus on vertical agents like Data Analyst Bots, Research Assistants, and more. Module 5: Teaching LLMs Your Organization's Language Through Fine-TuningWhy do generalist models struggle with your industry's terminology? Learn practical approaches to adapting models to your domain's vocabulary, processes, and knowledge structures. Implement LoRA fine-tuning and knowledge graph integration techniques that ground AI in your organizational context. Module 6: Capstone Discussion WeekWorkshop your final project ideas, get peer and instructor feedback, and finalize your architecture and approach for your custom RAG or Agent system. Module 7: Demo DayPresent your fully built, working system to the cohort. From agent demos to enterprise RAG deployments — this is where theory meets execution. Why This Course Is Different?The GenAI landscape is cluttered with tools, frameworks, and platforms—each promising to be the silver bullet. Our approach isn't about teaching you specific tools that will be outdated in six months. Instead, we focus on the enduring principles of identifying high-value use cases and building solutions that address actual business needs. Our startup has guided organizations through the process of moving from AI curiosity to deployment, and we've distilled those lessons into this course. You'll learn to look beyond the marketing hype to determine which problems in your organization are genuinely suited for Gen AI solutions. This Course Delivers Results Where Others Don'tUnlike theoretical courses that leave you wondering how to apply concepts, or technical deep-dives that ignore business realities, this program bridges the gap between possibility and practicality.We've designed every session to answer the question: "How do I use this to create actual value in my organization?" Whether you're looking to enhance customer experiences, streamline operations, or unlock insights from unstructured data, you'll complete this course with concrete tools to transform GenAI from a buzzword into a business advantage. ✅ What You'll Get (Outcomes, Action-Oriented) By the end of this course, you’ll be able to: 🧠 Understand the GenAI Landscape: Cut through the noise and get a clear grasp of how LLMs, RAG, and Agents fit into real-world enterprise workflows. 🛠️ Build Production-Ready AI Agents: Learn how to design and deploy AI Agents that go beyond simple Python scripts — handling reasoning, memory, retrieval, and tool use. 💼 Deliver Business Impact: Map Agent use-cases to measurable ROI — from automating internal knowledge retrieval to customer service, market analysis, and more. 🧪 Customize &amp; Scale Your Solutions: Fine-tune domain-specific models, design feedback loops, and deploy with guardrails. 🚀 Capstone + Demo Day: Work on a real-world Agent project and showcase it to a private network of peers, builders, and mentors.

What can Agents do for me?

Product Managers looking to integrate GenAI into their roadmaps with clarity and confidenceYou want to cut through the jargons and actually

Technical Leaders and Architects who want to build scalable and safe GenAI systems

Startup Founders &amp; Innovation Teams aiming to create competitive GenAI-powered products

Data Scientists &amp; ML Engineers who want to go beyond notebooks and deploy impactful GenAI apps

Who is this course for

If you haven't written code before, no worries at all. We will provide you with everything you need to get started.

Just be willing to learn, we will take of the rest

Actionable Business Value, Not Just Technical Knowledge Outcome

Develop a structured approach to evaluate which business problems in your organization are genuinely suited for GenAI solutions. Stop wasting resources on use cases that look good in demos but fail to deliver ROI.

A Framework for AI Use Case Identification

Create actual, functioning GenAI applications using our provided code templates and step-by-step guidance. You'll build and deploy real systems—not just learn concepts.

Practical Experience Building Working Systems

Connect with professionals facing similar challenges across diverse sectors. Previous cohorts have formed lasting relationships that continue to provide value long after the course ends.

A Network of Cross-Industry Peers

Develop the ability to assess AI vendor claims and capabilities objectively. Learn which questions to ask and what metrics actually matter when evaluating solutions.

Vendor-Neutral Evaluation Skills

Build a clear, phased approach to GenAI implementation for your organization that aligns with business objectives and accounts for technical constraints.

Strategic Implementation Roadmap

<ol><li>Introduction to NLP: covers what NLP is, its history, applications and challenges.</li><li>NLP Techniques: covers common techniques such as tokenization, part-of-speech tagging, named entity recognition and sentiment analysis with examples of their use.</li><li>NLP Tools: introduces popular NLP tools such as NLTK, spaCy with examples of how to use them </li></ol>

Introduction to Natural Language Processing

<ol><li>Transformers Foundational Knowledge: covers fundamental concepts of transformers, including self-attention, multi-head attention, and positional encoding.</li><li>Introduction to Fundamental Concepts of ML System Design: covers the basics of designing machine learning systems, including data collection and preprocessing, model selection and training</li></ol>

Foundational Knowledge of Transformers & ML System Design

<ol><li>Introduction to Information Retrieval Systems: covers the basic concepts and goals of information retrieval systems and their importance in various applications.</li><li>FAISS: introduces Facebook's AI Similarity Search (FAISS) library, a popular open-source library for efficient similarity search and clustering of dense vectors, and explains how it works.</li></ol>

Retrieval Systems

<ol><li>Deployment on a Serverless Inference: discusses the benefits of serverless computing and how to deploy the semantic search model on a serverless platform like Huggingface</li><li>Preprocessing of Hotel Data</li><li>Evaluation of the Model: discusses the different evaluation metrics used for semantic search models and how to measure the performance of the model</li></ol>

Building a search engine from bare bones and deploying it on Huggingface using server-less inference

<ol><li>Introduction to Knowledge Graph: covers the basics of knowledge graphs, including their architecture, data modeling, and how they are used in real-world applications like search engines and recommendation systems.</li><li>Generative AI: introduces the concept of generative AI and how it is used to generate new data, such as text, images, and music. </li></ol>

Knowledge Graph, Generative AI, Alpaca,Llama and Dolly 2.0 and World of langchain on larger corpus of Data

<ul><li>Understand the difference between prompt engineering and fine tuning</li><li>Introducing bits and bytes</li><li>Fine tuning GPT3</li></ul>

Fine tuning Models using PeFT and QLoRa

Teams get to present their learnings through a capstone project

Demo Day

Course syllabus

Data Scientist & Career Coach at Data Sistah

What people are saying

Excited to have you here

We will go through each module during this class

Wednesday: Module Teaching

Students will spend time building projects with their team members or individually

Weekly projects

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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