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Learn how to identify AI opportunities, design AI products, and launch scalable AI solutions that deliver real business value.

AI Instructor at School of AI

Melina Chen

AI product leader guiding companies from ML prototype to production AI products

Founder at School of AI

Vivian Aranha

Founder who builds enterprise AI systems and trains global AI leaders.

Cohort 2

Cohort 3

This course is completely asynchronous and available to view at your own convenience. There will be no Live sessions.AI is transforming every industry, yet many organizations struggle to turn AI technology into real products that deliver business value. Teams often build impressive models but fail to create products that users trust, adopt, and benefit from.This AI Product Management Bootcamp teaches you how to bridge the gap between business strategy, machine learning capabilities, and real product design. Instead of focusing only on theory, you will learn how successful AI products are identified, designed, and launched.You will learn how to identify high-impact AI opportunities, translate business problems into machine learning solutions, design AI-powered user experiences, and define evaluation metrics that measure real product success.Whether you are a product manager, startup founder, consultant, or technical professional, this program gives you practical frameworks to design, build, and launch AI-powered products with confidence.

AI Product Management Bootcamp: Design & Launch AI Products

Identify High-Value AI Opportunities

Translate Business Problems into AI Solutions

Design AI Product Architecture

Design Trustworthy AI Product Experiences

Define Metrics for AI Product Success

Build and Launch an AI Product Strategy

Product Managers PMs who want to learn how to design and launch AI-powered products and lead AI initiatives within their organizations.

Startup Founders Building AI startups who need frameworks for identifying use cases, designing AI solutions, &amp; launching AI products.

Engineers Transitioning to Product Roles ML engineers or developers who want to move into AI product strategy and product leadership roles.

Learn to build and deploy real AI apps using LLMs, RAG, and agents—transforming from AI user to production-ready AI engineer.

Founder & AI Instructor at School of AI

AI architect with 2.2M+ learners, building enterprise-grade agentic systems

Cohort 4

Cohort 5

This course is completely asynchronous and available to view at your own convenience. There will be no Live sessions.AI is transforming software development, but many professionals struggle to move beyond tutorials and actually build real AI applications. Most courses focus on theory or simple prompts, without teaching how to design complete AI systems.This bootcamp bridges that gap.In three intensive days, you will learn the core capabilities of modern AI engineering: working with LLM APIs, prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and deployment. Instead of just learning concepts, you will build real applications step by step—from your first AI-powered app to a document-aware RAG system and an autonomous AI agent.By the end of the program, you will have designed, built, and deployed a production-ready AI application. You’ll leave with practical skills, a portfolio project, and the confidence to build intelligent AI systems for real-world use cases.

Build Production-Ready AI Apps with RAG, Agents, and Real Deployment

Build Your First AI Application with LLM APIs

Master Prompt Engineering for Reliable AI Outputs

Design and Build a Retrieval Augmented Generation (RAG) System

Build AI Systems with Tools and External Data

Create Autonomous AI Agents that Perform Tasks

Deploy a Production-Ready AI Application

Software Developers Transitioning into AI Engineering Want to build AI-powered applications using LLM APIs, RAG systems, and agent workflows.

Data Scientists Expanding into AI Application Development Want to move beyond models and build real AI products and deploy production systems

Tech Professionals and Builders Creating AI Products PMs, founders, and engineers who want to build and launch practical AI applications.

By the end of this bootcamp, you’ll leave with a compelling portfolio and a clearer story for interviews.

Principal Designer@Microsoft, ex-Spotify

Microsoft

Spotify

Boston Consulting Group (BCG)

Samsung

Yan Liu

Principal Designer@Microsoft, Ex-Spotify, BCG, Samsung, Top 1% Mentor Worldwide

Cohort 1

AI is changing what hiring teams look for. It’s no longer enough to say you’re interested in AI, you need proof that you can think through an opportunity, shape the product direction, and build something real.In this 4-week bootcamp, you’ll create a compelling portfolio with real AI projects, and learn how to use it in applications and interviews.Perfect for designers, PMs, and career transitioners who want to break into AI-related product roles with stronger proof of work.👉Outcomes<ul><li>a compelling portfolio website</li><li>a real AI product project you can show</li><li>a clearer story for applications and interviews</li><li>a more confidence positioning yourself in the AI job market</li></ul><h2></h2>Don’t just learn AI. Build something that helps you get hired.🔥Early bird🔥Enroll today and get 20% off by using the code: YANDESIGN

Build a compelling portfolio - and use it to land your first AI job (in 4 weeks)

1️⃣ A real AI capstone product worth putting in your portfolio

2️⃣ Product strategy that feels credible in interviews

3️⃣ High-taste product experience

4️⃣ A working prototype you can demo

5️⃣ A strong portfolio case study that helps you get interviews

6️⃣ A sharper interview story

Product DesignersYou have strong design fundamentals and want a clear path to land on AI jobs.

Product ManagersYou know strategy, but need proof of execution. This bootcamp helps you turn ideas into sth tangible to show in interviews.

Career TransitionersYou want to land on AI-related roles, but don’t know where to start. This course gives you a concrete project to anchor.

Design, build, deploy, and operate production-ready AI systems using LLMs, RAG, agents, APIs, and AI Ops practices.

AI is moving from experimentation to real-world systems, yet most professionals face a major challenge: knowing how to move from models and prompts to production-ready AI applications.Many courses teach isolated concepts—prompting, machine learning theory, or model APIs—but very few explain how modern AI systems are actually built, deployed, and managed.This 7-Day AI Engineer Bootcamp is designed to close that gap.In this hands-on program, you will learn how AI engineers design and build real systems using LLMs, embeddings, retrieval-augmented generation (RAG), AI agents, APIs, and production deployment patterns.Instead of focusing only on theory, the course walks you through the complete AI engineering lifecycle: data, prompts, architecture, agents, deployment, monitoring, and scaling.By the end of the program, you will build a complete AI system, such as a customer support agent, research assistant, or knowledge bot, and understand how to deploy and manage AI applications in real environments.If you want to move beyond experimenting with AI and start building real AI products, this bootcamp will give you the frameworks, tools, and hands-on experience to do it.

Build Production-Ready AI Systems in 7 Days — From LLMs to Agents

Build AI-Powered Applications with LLMs

Implement Retrieval-Augmented Generation (RAG)

Create Autonomous AI Agents

Deploy AI Systems as APIs

Operate Production AI Systems

Design End-to-End AI Architectures

Software Engineers exploring AI development Developers who want to build real AI systems using APIs, agents, and modern AI frameworks.

Product managers and technical founders Professionals who want to understand how AI products are designed, built, and deployed.

Data professionals transitioning into AI engineering Analysts &amp; data scientists who want to expand into building end-to-end AI applications.

Design real AWS systems and pass SAA-C03 with confidence—think like a Solutions Architect, not just memorize services.

AI & Cloud Certification Experts

Data Science Academy

Training 10,000+ students in AI, ML, and AWS globally

Most AWS courses teach services in isolation. The exam—and real jobs—don’t.The biggest challenge learners face is not understanding what AWS services do, but knowing when to use them, why to use them, and how to combine them into real-world architectures. This gap is exactly why many candidates struggle to pass the SAA-C03 exam or feel unprepared in real engineering roles.This course is designed to solve that problem.You will learn AWS the way Solutions Architects actually work—through real systems, real trade-offs, and real decision-making. Instead of memorizing features, you will build intuition around architecture patterns like high availability, scalability, cost optimization, security, and fault tolerance.We focus heavily on scenario-based learning, which mirrors the actual exam format. Every concept is tied to practical use cases such as web applications, data pipelines, microservices, and event-driven systems. By the end, you won’t just recognize services—you’ll confidently design solutions.Whether your goal is to pass the certification, transition into cloud roles, or strengthen your architecture skills, this course gives you both exam readiness and real-world capability.

Build real AWS architectures and pass SAA-C03—no fluff, just real systems

Design real-world AWS architectures

Pass the SAA-C03 exam with confidence

Master AWS service selection & trade-offs

Build scalable & highly available systems

Design secure and cost-optimized architectures

Think and operate like a Solutions Architect

Aspiring cloud engineers seeking a structured path to learn AWS, build real systems, and pass the SAA-C03 certification.

Developers and data professionals who want to design scalable AWS systems and move into architecture-level roles.

Certification-focused learners preparing for SAA-C03 who need scenario-based practice and real-world understanding.

Apply Proprietary AI Product frameworks in AI Product lifecycle for structured thinking: concepts + cases + skill   building from CXO's lens

 3x Head of Product, 2x CEO, 1x GM

The Home Depot

U.S. Army

Cadence

Unilever

Stanford University

Rocky: $1.3B P&L at Home Depot

3x Head of Product |
2x CEO |
1x GM |
Led $1.3 Bn Product dept at Home Depot


In the age of AI, you are a Product Manager/Leader concerned about (a) KEEPING your job, (b) ADAPTING to new Product lifecycle, (c) PROVING your productivity, &amp; (d) EVOLVING your Product career.What if you had access to the tools, skills, &amp; structured thinking required to meet business goals by bringing customer delight and competitive edge that keeps your Managers impressed in AI era?Learn the concepts of AI, Master 4 proprietary AI Product frameworks™ that tie directly with what C suite expects from Product Managers in AI era, and build an AI Product . This 16 hours LIVE course covers concepts + case studies + skill application:(1) AI Product skills that CEO's expect from PM's in AI era(2) AI Product Lifecycle that CPO/CPTO's expect PM's to follow in AI era(3) AI Product Mindset that COO's expect PM's to demonstrate for a long term career(4) AI Productivity that CFO's expect PM's to prove 10x productivity(5) AI Product building with AI that CHRO expects from PM's by solving a LIVE business problem

Meet AI Product goals using AI Product Frameworks™ in AI product lifecycle 

VISIONARY™ framework for Product skills  in AI era (from CEO's lens)

MAVERICK™ framework for Product lifecycle in AI era (from CPO/CPTO's lens)

TRANSFORM™ framework for Product mindset in AI era (from COO's lens)

POWERHOUSE™ framework for Product toolkit in AI era (from CFO's lens)

Live Capstone Project (From CHRO's lens)

We cover what a Product Manager with no technical degree can achieve in AI era with Product mindset. You need access to $20 paid Claude plan

Non technical background with openness to learning & access to paid Claude plan

You have practical experience with atleast 1 Product lifecycle in digital era & have tinkered with AI tools (eg, ChatGPT, Claude, Gemini)

You know the basics of product management lifecycle

You are interested in solving problems that directly align with business outcomes leveraging AI & not chase hype with flashy AI tools

Interested in solving business problems  via AI & not chasing hype with AI tools

(1) Early career Product Managers, (2) Mid career Product Leaders, (3) Product Executives, (4) You work with Product teams

You were a Product Manager (PM) in digital era (1997 - 2022) &amp; are trying to transform your PM career in AI era (2023 - )

You tinker with AI tools for PM's, face information overload, &amp; are seeking to learn skills that C suite want PM's to demonstrate in AI era

Go from AI beginner to AWS AI practitioner—build real skills, understand systems, and pass certification with confidence.

AI & AWS Certification Bootcamp

Building real-world AI & AWS systems with hands-on training

AI is no longer optional—it’s becoming a core skill across engineering, product, data, and business roles. But most learners struggle with one major problem: they either learn theory without practical application, or they explore tools without understanding how everything fits together—especially on AWS.This course solves that gap.Instead of overwhelming you with scattered concepts, this bootcamp gives you a clear, structured path to understand how AI actually works on AWS—from foundation models and prompt engineering to real AI services like Bedrock, SageMaker, Rekognition, and Lex.At the same time, this course is aligned with the AWS Certified AI Practitioner exam, helping you build both practical skills and certification confidence. You’ll understand architecture patterns, common exam traps, and how to approach scenario-based questions effectively.If you’ve been trying to break into AI, transition into cloud AI roles, or simply make sense of AWS’s rapidly evolving AI ecosystem—this course gives you a focused, hands-on, and career-relevant path forward.

Break into AI on AWS and pass certification with real hands-on skills

Understand AI concepts and AWS AI ecosystem

Work with foundation models and generative AI

Write effective prompts for reliable AI outputs

Build simple AI applications on AWS

Use AWS AI services for NLP, vision, and chatbots

Prepare for AWS Certified AI Practitioner exam

Beginners and non-technical learners who want to break into AI using AWS with a structured, hands-on path

Cloud, data, or software professionals preparing for AWS AI Practitioner certification and practical AI skills

Product managers, analysts, and tech enthusiasts who want to apply generative AI and AWS tools in real work scenarios

Go from basic ML knowledge to building, deploying, and managing real-world ML systems on AWS with confidence.

AI & ML Engineering Experts

Building real-world AI, ML & MLOps systems used in production

Most machine learning courses stop at theory or basic model building. But in the real world, companies don’t just need models — they need end-to-end ML systems that scale, deploy, and deliver impact.You may understand algorithms, but:<ul><li>You’re unsure how to move from notebooks to production</li><li>AWS services like SageMaker, Glue, and pipelines feel overwhelming</li><li>You don’t know how to design scalable, cost-efficient ML architectures</li><li>Certification prep feels disconnected from real-world skills</li></ul>In just 14 days, you’ll go from understanding ML concepts to confidently designing and implementing production-grade ML workflows on AWS.You’ll learn how to:<ul><li>Structure the entire ML lifecycle from data ingestion to deployment</li><li>Use core AWS services like S3, Glue, Athena, and SageMaker effectively</li><li>Train, tune, and deploy models using real engineering workflows</li><li>Build ML pipelines, monitoring systems, and retraining strategies</li><li>Apply MLOps best practices used in real companies</li><li>Optimize for performance, scalability, and cost</li><li>Prepare for the AWS Machine Learning Engineer Associate certification</li></ul>By the end of this course, you won’t just “know ML” — you’ll understand how to build and operate ML systems like an engineer.

Master AWS ML engineering to build real, production-ready AI systems

Design end-to-end ML systems on AWS

Build and prepare high-quality ML datasets

Train, tune, and evaluate ML models effectively

Deploy ML models into production environments

Implement MLOps and monitoring pipelines

Optimize, secure, and prepare for certification

Learners with basic ML knowledge who want to transition into real-world ML engineering roles on AWS.

Professionals who can build models but want to learn deployment, MLOps, and production systems.

Engineers looking to add AI/ML capabilities and build scalable ML systems using AWS services.

Create your own production-ready AI application in 6 weeks

Teaching AI and Data to 100k+ students 

DataTalksClub

Manning Publications

OLX Group

Alexey Grigorev 

15 years of experience. Teaching AI and Data to 100k+ students

This course takes you from core concepts to production-grade AI systems through hands-on, project-focused modules.<ol><li>LLMs &amp; RAG Learn Large Language Models and Retrieval-Augmented Generation. Build conversational agents using the OpenAI SDK and create data-processing pipelines. Outcome: A RAG pipeline with real data.</li><li>Agentic Flows + MCP Add agentic behavior with function calling, using libraries like PydanticAI, and Agents SDK. Expose tools via MCP. Outcome: A capable, tool-using agents.</li><li>Testing &amp; Evaluation Improve through testing and offline evaluation. Use LLMs as judges to compare approaches. Learn tools like Evidently and LangWatch. Outcome: A thoroughly tested and evaluated assistant.</li><li>Monitoring &amp; Guardrails Use Grafana, Pydantic Logfire and OpenTelemetry for observability and safety. Outcome: Real-time monitoring.</li><li>Use Cases Create two agents: a website generator and a code reviewer. Learn about other use cases.</li><li>Capstone Build an end-to-end AI application with your data. Outcome: A portfolio-ready project.</li></ol>Hackathon: Collaborate on real-world problems.By the end, you will: build, evaluate and monitor a smart assistant; create deep research and coding agents; have a polished portfolio project

Build your own Agentic AI Assistant

🛠️ Build a Fully Functional AI Assistant from Scratch

🤖 Add Agentic Behavior to Your AI Systems

🔧 Use Testing and Evaluation to Improve Prompts and Results

📈 Monitor Your Application 

🔧 Build 8+ Projects 

🎓 Ship a Capstone AI Project — Start to Finish

Coding

We will rely on these tools when building the assistant

Python, Git, Docker, command line

We will use OpenAI for building the AI agent

OpenAI key or alternative

Data Scientists and ML Engineers proficient at coding who want to integrate AI into their projects

Software Engineers curious about LLMs who want to build AI applications

AI Enthusiasts who are stuck at the tutorial phase and want to create something end-to-end

AI Fundamentals

Apply AI in your role

Career Development

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

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AI Fundamentals

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AI Fundamentals

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