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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 1 – June 26 🚨Use Code: Earlybird (20% discount 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 🛠️ Bring your own AI project and apply the frameworks each week (or use a provided case study)🧑‍🏫 2 Live sessions per week plus office hours📊 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.Student or between jobs? Message me. Financial assistance is available

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

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)

You will implement Quantum SVMs, Quantum neural networks, Quantum feature maps, and hybrid quantum-classical models in Qiskit 2.3.

Quantum Machine Learning Scientist

Dr. Muhammad Faryad

Cohort 2

Classical machine learning already pushes limits with kernel methods, optimization, and high-dimensional feature spaces. As models scale, so do the bottlenecks—training cost, feature engineering complexity, and diminishing returns.Quantum computing introduces a fundamentally different regime:<ul><li>⚙️ Exponentially large Hilbert spaces for richer representations</li><li>🧠 Quantum feature maps for embedding data in new ways</li><li>🔁 Variational quantum models with compact expressivity</li><li>📈 Native kernel estimation via quantum circuits</li></ul>But the real challenge is not theory—it is applicability.<ul><li>❓ Where do quantum models fit in an ML pipeline?</li><li>❓ When does a quantum kernel outperform a classical one?</li><li>❓ How do you move from equations to working code on real hardware?</li></ul>This course is designed to answer exactly these questions.<ul><li>🎯 Focus on what is usable today—not distant promises</li><li>💻 Hands-on implementation using Qiskit</li><li>🔬 Build and test quantum feature maps, QSVMs, and variational classifiers</li><li>🔗 Learn how to integrate quantum models into existing ML workflows</li></ul>By the end, you will not just understand quantum ML—you will know when, why, and how to apply it in practice.

Upskill your ML career: Integrate quantum computing with machine learning.

Build and train quantum models end-to-end

Master quantum data encoding strategies

Develop production-ready quantum programming skills

Understand constraints of current quantum hardware for practical programming

This course assumes knowledge and fluency with basic classical ML concepts, including supervised ML, SVMs, kernels, and neural networks.

Classical Machine Learning Concepts 

The course will focus on using Qiskit for quantum ML programming, assuming students are already experienced in implementing ML problems. 

Working Knowledge of Numpy, Sklearn, Pandas

This course is meant for experienced ML/AI engineers/leaders who are already well-versed in classical AI tools and want to learn quantum ML.

This course is NOT for beginner ML students

Senior ML Engineers — Extend production ML expertise into quantum kernels, quantum neural networks, and hybrid training loops

AI / ML Researchers — Reproduce, benchmark, and rigorously evaluate quantum ML models with solid theoretical depth and practical codes.

Tech Lead / Innovation Strategists — Develop insights for quantum machine learning, hardware limits, and credible quantum-ready use cases.

Stop starting every session from scratch. Build a system that compounds.

AI educator | ex-AWS GenAI architect

Amazon Web Services

lululemon athletica

Nike

Sam Keen

Author, Clean Architecture with Python | ex-AWS Generative AI lead architect

Many developers use Claude Code the same way every session: type a prompt, evaluate the output, repeat. Some sessions produce clean, correct code. Others feel like starting over.The difference isn't the prompt. It's what Claude knows before you type a word.Context engineering is how you close that gap. Not with longer prompts, but with agent files, custom skills, and automated hooks that load the right context at the right time. The investment compounds: every session builds on what you've already set up.In this workshop, you'll see a complete context engineering system built from scratch for a real project. Every decision narrated, every trade-off explained.You'll leave with templates, a reference card, and the recording. Everything you need to build the same system for yours.

Developers who use Claude Code regularly but get inconsistent results. Some sessions are great, others feel like starting over.

Developers just getting started with Claude Code who want to build a scalable system from day one.

Tech leads setting up Claude Code for a team who need a repeatable system, not tribal knowledge.

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

AI Founder & Advisor to F500s | Ex-AWS

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

Quantum ML in 4 Hours: Build Real QNNs & Kernels in Qiskit

Penn State College of Engineering

Qiskit

Abdus Salam International Centre for Theoretical Physics

Muhammad Faryad

Hands-on workshop on data encodings, quantum neural networks, and quantum kernels using Qiskit.

Most ML engineers are hearing about quantum advantage—but have no practical way to evaluate or use it. This creates a real professional gap: you’re expected to understand quantum ML, yet most resources are either too theoretical or too fragmented to apply. This workshop solves that gap directly.In just 4 hours, you’ll move from zero intuition to actually building and testing quantum models—quantum kernels, QSVMs, and QNNs—using Qiskit. You won’t just “learn concepts”; you’ll implement circuits, encode data into feature maps, and see how quantum models behave on real problems. All you need to start is the basics of quantum computing. If you are familiar with Qiskit and quantum computing, this workshop will take you to Quantum ML applications.Why this matters now:<ul><li>Quantum computing is transitioning from hype to early adoption—teams need practitioners, not observers.</li><li>Hiring managers increasingly value “applied exposure” to quantum tools.</li><li>Understanding when quantum models help (and when they don’t) is a competitive edge.</li></ul>

The workshop assumes that participants have basic know-how of quantum computing and now want to go deeper into quantum ML. 

No. But an understanding of basic gates and circuit language of quantum computing is required. We will not review quantum gates.

Is prior Qiskit programming experience required?

Yes, the workshop assumes that participants have an understanding of ML/AI concepts and want to transition to Quantum ML/AI.

Is ML experience required?

ML Engineers: Extend ML expertise into quantum kernels and quantum neural networks if you have basic quantum computing knowledge.

AI / ML Researchers: Reproduce, benchmark, and rigorously evaluate quantum ML models with solid theoretical depth.

Tech Lead / Founders: Develop insights for quantum machine learning, hardware limits, and credible quantum-ready use cases.

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

Craft

AI Fundamentals

Leadership fundamentals

AI Fundamentals

Leadership fundamentals

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Developing AI Models

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