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How to think through AI problems end-to-end: scoping, debugging, and knowing when to pivot.

ML Engineer with 10+ years of experience

Hugging Face

DataRobot

Contextual AI

Snowflake

Snorkel AI

Rajiv Shah

Enterprise AI veteran: Shipping AI to production, with clarity and humor

Pilot Cohort

Summer 2026

AI Problem Framing is to AI teams what System Design is to software engineers and Product Sense is to PMs. Whether you're building, evaluating, or leading AI work, this is the foundational thinking skill that separates results from rework.--🚨 Summer cohort: June 2 – June 26 🚨Winter Cohort is finishedUse Code: Earlybird for 20% if you sign up by April 30th---What you get:🔄 The Loop, a 5-step framework you'll use to think through every AI project🧑‍🏫 Live sessions and office hours where you can bring your real problems📊 Access to 200+ case studies, the largest collection of AI reframing examples📋 Production-ready checklists for RAG, Forecasting, GenAI🎥 Lifetime access to all recordings---This course is for you if:• Your AI works in demos but fails in production• You've spent months on a model only to realize you solved the wrong problem• You lead AI work but came from engineering, product, or PMYou'll learn from 200+ AI failures in 4 weeks. You gain years of experience and recognize the scars.

Most AI fails from bad framing, not bad models. Learn to fix that.

Frame AI problems right the first time

Diagnose what's actually broken

Own the problem, not just the solution

Steer your team past expensive mistakes

We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.

You understand what AI/ML is at a conceptual level

Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.

You've worked on or led AI projects in some capacity

This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.

No coding during the course; frameworks for thinking, not tutorials

Engineers who can build AI but want to master outcome engineering: knowing what to build, not just how

Building RAG, agents, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.

Managers who need to evaluate AI/ML proposals, set realistic milestones, and know when to escalate

By the end of 4 weeks, you'll have the skills, the methodology, the tools and the hands-on experience to lead AI initiatives at your company

Co-founder @ Lovelaice, Product Leader

Madalina Turlea

Co-founder @Lovelaice, 10+ years in Product

Founder @Lovelaice | 14+ years in Tech

Catalina Turlea

Founder @Lovelaice, 14+ years leading Tech

February 2026

March 2026

A 4-week hands-on course where Product Managers learn to test prompts, benchmark models, write evals and build business cases for AI features using real data on your real use-case.Most PMs are stuck between two extremes: either waiting for engineering to figure it out, or shipping features that fail in production because they only tested on two happy cases.There's a better way.This course teaches you a systematic methodology for building AI features that actually work - before you write a single line of production code.You'll learn how to test prompts across multiple models, evaluate results like a pro, and make data-backed decisions about what to ship.This isn't theory.You'll run real experiments on your own product ideas using the same approach that's helped teams catch critical failures before their customers did, discover that cheaper models often outperform expensive ones, and ship AI features with confidence instead of crossed fingers.By the end of 4 weeks, you'll have the skills, the methodology, the tools and the hands-on experience to lead AI initiatives at your company - not just participate in them.

Stop guessing. Start testing. Ship AI with confidence.

How to integrate AI into your product

Build a eval pipeline for your AI features

You want to be in the top 5% of PMs who actually understand how to build with AI

You're tired of waiting for engineering to lead AI initiatives.You feel pressure to "do something with AI" but don't know where to start

You've shipped an AI feature but aren't confident it's working well (or your customers are finding the bugs)

Build, deploy, and scale real AI systems—transforming you from learner to job-ready AI Engineer.

Alpha

Beta

Charlie

Delta

Echo

Foxtrot

Golf

Hotel

Train Models. Build Systems. Deploy AI

Vivian Aranha

AI educator and engineer who builds and teaches production-ready AI systems.

India

Many people learn AI by watching tutorials or training models in isolation—but struggle when asked to build, evaluate, deploy, and maintain real AI systems. This course exists to close that gap.The critical challenge today isn’t learning algorithms—it’s turning AI knowledge into production-ready systems. Engineers often know Python or machine learning concepts but lack confidence in data pipelines, model evaluation, deep learning frameworks, and deployment workflows. As a result, projects stay in notebooks and portfolios don’t reflect real-world skills.This bootcamp is designed with empathy for that struggle. It takes you step by step from Python fundamentals to advanced deep learning and MLOps, always anchored in hands-on projects that mirror real engineering work. You won’t just train models—you’ll engineer complete AI solutions, tune them, deploy them, and understand how they behave in production.By the end, you don’t just “know AI.” You can build, explain, and ship AI systems with confidence.Learn on your schedule with this self-paced course featuring pre-recorded sessions. Enjoy lifetime access, so you can revisit and reinforce your learning whenever needed.

Learn AI Engineering by Building Real Systems End-to-End

Build end-to-end AI systems

Master core machine learning techniques

Engineer features that improve model performance

Develop deep learning models confidently

Deploy and operationalize AI models

Think and work like a professional AI Engineer

Engineers and developers who want to transition into real-world AI engineering roles.

Data scientists seeking stronger system-building, deployment, and MLOps skills.

Students and professionals building an AI portfolio for jobs, freelancing, or startups.

Empower your team with real advice and hands-on skills to build privacy into today's AI workflows and systems. 

Author of Practical Data Privacy

Katharine Jarmul

Author of Practical Data Privacy, Specialist in AI/ML Systems

Cohort 1

Ensure your AI platforms and products are ethically sound and legally compliant. This intensive course equips you with the essential skills for modern AI privacy engineering, offering a blend of cutting-edge best practices and practical implementation strategies. Through live sessions, collaborative design workshops, and a capstone project simulating a real-world challenge, you'll gain expertise in defining privacy, assessing and mitigating risks, implementing pseudonymization and input sanitization, and integrating guardrails into AI workflows. This course is designed for mid-career and senior-level software engineers, data engineers, machine learning engineers, and privacy professionals seeking to enhance their expertise in a rapidly evolving field. Participants will work collaboratively in multidisciplinary teams, mastering not only technical skills but also crucial concepts in architecture, risk assessment, and mitigation prioritization. With the increasing use of sensitive data in AI products, these skills are in-demand and future-proof.

Build Privacy Engineering into AI/ML Systems

Identify actionable privacy risks in AI systems

Evaluate and implement reasonable controls

Architect privacy in AI workflows

Evaluate and test systems with a focus on privacy

You should be familiar with Python and able to use normal Python data libraries. You are willing to adapt code and build out AI workflows.

Data, Software and Machine Learning Engineers (Target Audience)

You have technical chops and either want to expand them or integrate them into your work with AI/ML systems

Privacy Professionals

You are comfortable in technical conversations and want to increase your knowledge of AI privacy risk.

Privacy and Risk Leadership

Data, Software and Machine Learning Engineers

Technical Privacy Professionals (Privacy Engineers)

Privacy Leadership (technical-oriented)

Understand how AGI systems are engineered and gain the ability to design intelligent agents, architectures, and aligned AI systems.

AI Systems Engineer & Educator

Data Science Academy

AI systems engineers building agents, AI platforms, and AGI-focused courses.

Artificial intelligence is rapidly evolving from simple models to complex systems capable of reasoning, planning, and autonomous decision-making. While many courses focus on machine learning algorithms or prompt engineering, very few explain how general intelligence systems are actually designed and engineered.This course bridges that gap.You will learn how modern AI systems move toward AGI through cognitive architectures, intelligent agents, multi-agent coordination, and alignment frameworks. Instead of focusing only on theory, we explore the engineering principles behind intelligent systems used by leading research labs and AI companies.Throughout the program, you will design agent architectures, build reasoning loops, experiment with multi-agent collaboration, and explore the safety and alignment challenges that arise when AI systems become more autonomous.If you want to understand how the next generation of AI systems will be built, this course gives you the conceptual foundations and system-design mindset required to think like an AGI engineer.

Learn how AGI systems, agents, and alignment frameworks are engineered

Understand AGI systems, architectures, and intelligence frameworks

Design intelligent agents and cognitive AI systems

Build and analyze multi-agent AI systems

Apply AI alignment principles and frameworks

Engineer safe and reliable AI systems

Design AGI-inspired systems and real-world applications

AI engineers and developers who want to understand how AGI systems, intelligent agents, and alignment frameworks are designed.

Researchers and AI practitioners interested in cognitive architectures, multi-agent systems, and the future of AGI development.

Product builders and technologists exploring how to design safe, scalable AI systems beyond basic ML models.

Go from AI beginner to building real ML and deep learning applications ready for deployment.

AI Engineering Bootcamp

AI engineers building practical machine learning and deep learning systems.

Cohort 2

Artificial Intelligence is transforming every industry — from healthcare and finance to transportation, retail, and cybersecurity. Yet many professionals struggle to move beyond theory and actually build real AI systems.This course is designed to solve that problem.Instead of focusing only on concepts, this program walks you through the complete journey of building practical AI solutions. You will start with the foundations of Artificial Intelligence and Python for data science, then progressively learn how to design machine learning models, evaluate them properly, and build deep learning systems.Along the way, you will implement real projects such as housing price prediction, sentiment analysis, image classification with neural networks, and AI-powered web applications.By the end of the course, you will not only understand how AI works — you will know how to build, test, and deploy AI models into real applications using modern tools like Python, TensorFlow, PyTorch, and Streamlit.You will leave with practical skills, portfolio-ready projects, and the confidence to build your own AI-powered applications.

Build Real AI Applications with Python, Machine Learning & Deep Learning

Build machine learning models using Python

Design and train neural networks

Develop Natural Language Processing applications

Build computer vision models

Understand modern AI technologies

Deploy AI models as real applications

Developers or engineers who want to transition into AI and build real machine learning and deep learning applications.

Data analysts and tech professionals looking to expand their skills into AI, ML, NLP, and computer vision.

Students and beginners interested in learning AI from fundamentals to real-world projects and deployment.

Master Generative AI engineering by building production-ready LLM systems using RAG, agents, embeddings, and scalable AI architectures.

Training Engineers in AI & Data Science

Industry-focused academy training engineers in AI, LLMs, and data science.

Generative AI is transforming every industry — but most engineers only learn how to use AI tools, not how to build real AI systems.Companies are urgently looking for engineers who can design production-ready LLM applications, implement Retrieval-Augmented Generation (RAG) pipelines, build AI agents, and integrate AI into real software products.This course bridges that gap.You will learn how to move from AI experimentation to real engineering, building systems that combine LLMs, embeddings, vector databases, APIs, and agents into scalable applications.By the end of this course, you will know how to:• Design reliable prompts and LLM workflows • Build semantic search systems using embeddings • Implement production-ready RAG pipelines • Develop full-stack LLM applications • Optimize systems for cost, performance, and scaleInstead of just learning theory, you will build the core architecture behind modern AI products used by startups and tech companies today.If you want to become an AI Engineer capable of building real Generative AI systems, this course will give you the practical foundation to do it.

Learn to Build Production-Ready Generative AI & LLM Applications

Understand how transformers and LLM architectures work

Design reliable prompts and structured LLM workflows

Build semantic search systems using embeddings and vector databases

Develop Retrieval-Augmented Generation (RAG) systems

Create AI agents and tool-enabled LLM systems

Build and deploy full-stack LLM applications

Software engineers who want to transition into AI engineering and build production-ready LLM and generative AI applications.

Data scientists and ML engineers looking to learn RAG, agents, embeddings, and modern LLM application development.

AI builders, startup founders, and product developers who want to integrate generative AI into real-world products.

Evals aren't your product moat. A continuous improvement flywheel is.

AI Founder & Advisor to F500s | Ex-AWS

Amazon Web Services

Microsoft

University of Oxford

Aishwarya Naresh Reganti

June Cohort

Check out our free AI Evals Certification <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/ai_evals_for_everyone/README.md">here</a> (5000+) Learners!Most teams building AI products today know about evals and LLM judges, but they’re deeply confused about how to actually turn them into actionable improvement signals post production. Just throwing in LLM judges won’t magically fix your product. It won’t.<ul><li>This course shows how to use offline evals, online evals, and production monitoring to drive continuous improvement, based on real production experience.</li><li>We believe that going forward, a thoughtfully designed data flywheel will be the moat for AI applications. This course shows you how to build it.</li><li>This course is for people who’ve already built AI systems and are stuck with evals that are noisy, expensive, or uselessIf you need something more foundational, check out our top-rated flagship<a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design"> enterprise AI course</a> taken by over 1500+ builders and leaders.</li><li>For questions or bulk enrollments please email problemfirst.ai@gmail.com</li><li>We follow a flipped-classroom format with async lecture content and meet twice a week for office hour style sessions to optimize two-way/interactive live time.</li></ul>

Move beyond one-time evals to continuous improvement flywheels for AI products

Understand what evals actually mean and why it’s misunderstood

Build a pre-deployment evaluation foundation from scratch

Choose the right types of metrics for the system you’re building

Monitor in production and close the improvement loop

You should have built small AI applications before and be familiar with concepts like RAG, MCP, and tool use.

A good understanding of AI system design

The course includes optional Python assignments, you're free to use coding agents, but basic coding skills are recommended

Working knowledge of Python/Coding (Optional)

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants looking for systematic evaluation practices

Business Leaders and Product Managers seeking to gain the technical understanding of AI Product Lifecycle and intentional iteration

Founders &amp; teams stuck at systematic evaluations and monitoring, unsure how to turn signals into real product improvements.

This course is a comprehensive, hands-on journey into the world of AI, designed to equip learners with the skills to build & manage AI App

Cohort 9

Cohort 10

CEO & Founder, Decoding Data Science

Mohammad Arshad Ahmad


 AI mentor with 20+ years experience, creator of top AI programs helping careers

Cohort 3

In this course, students will gain hands-on experience with cutting-edge AI tools, frameworks, and methodologies, empowering them to build and deploy real-world applications. From mastering foundational concepts to implementing advanced generative AI models and MLOps pipelines, learners will develop practical skills that are in high demand across industries. With personalized mentorship, collaborative projects, and a strong focus on career outcomes, this course ensures students are fully equipped to excel in AI-driven roles and lead innovation in their fields. Use code SCHOLARSHIP code to get 50 % To get access to the only SELF-PACED course to access the recording use code SELFPACED to get it for $599 Any questions regarding this program book free One on One with me https://topmate.io/mohamad_arshad

Master AI tools, build real-world projects, and transform into an AI leader

✅ Outcome 1: Design Production-Grade AI Systems

✅ Outcome 2: Build & Deploy AI Apps End-to-End

Aspiring AI ProfessionalsThis course is designed for students, recent graduates, and early-career professionals who want to break into AI a

Mid-Career SwitchersIdeal for professionals from adjacent fields such as software engineering, IT, or business analytics who are looking to

AI Enthusiasts and EntrepreneursPerfect for tech enthusiasts, startup founders, and business leaders who want to integrate AI into their ve

AI Fundamentals

Apply AI in your role

Career Development

AI Fundamentals

Career development

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AI Fundamentals

Career development

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AI Fundamentals

Career development

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AI Fundamentals

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AI Fundamentals

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AI Fundamentals

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