Valeriy Manokhin, PhD, MBA, CQF

Cohort 2

Cohort 3

Cohort 4

Cohort 5

Cohort 6

Cohort 1

Cohort 9

Cohort 8

Cohort 7

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.

Master Conformal Prediction and become the expert who delivers trustworthy, risk-aware ML in production.


Conformal Prediction Expert, PhD, Author

Valeriy Manokhin

Conformal Prediction expert · PhD · CP book author

Applied Conformal Prediction

Senior Data Scientist 

Research Associate

Early Stage Researcher

Master forecasting with hands-on, real-world training. No fluff—just insights that drive ROI and advance your career.


Forecasting That Works in Production

Dr Valeriy Manokhin, MBA, CQF

ML expert and 7× book author building real-world forecasting systems

Modern Forecasting Mastery: From Models to Decisions

Researcher

Director of Operations

Data scientist

Team Lead Pricing

Data Scientist

Head of Sales & Sales Operations Planning

🔥 Enrollment Open — Applied Conformal PredictionThe most practical, zero-hype course on Conformal Prediction—designed to take you from curiosity to confident deployment.If you’re tired of vague uncertainty and black-box trust issues, this is for you. Seats are capped to keep the cohort high-signal.Built for real-world ML practitioners<ul><li>Add CP to classification, regression, time series, CV, and NLP pipelines</li><li>Get distribution-free uncertainty with measurable coverage guarantees</li><li>Deliver reliable prediction sets/intervals and communicate risk clearly</li><li>Deploy CP in production with modern ML tooling</li></ul>Instructor: Dr. Valeriy Manokhin Trained under Prof. Vladimir Vovk, author of Practical Guide to "Applied Conformal Prediction" and "Applied Conformal Prediction: Reliable Uncertainty Quantification for Real-World Machine Learning in Python", creator of the Awesome Conformal Prediction repo, and a published ML researcher. Experience building and advising production AI systems across industry.If your models drive real decisions, uncertainty is mission-critical. Join the next cohort and deploy CP with confidence.

What you will learn


Apply Conformal Prediction correctly

Deliver provable uncertainty guarantees

Deploy CP in production

Diagnose trade-offs and failure modes.

Lead trust-critical ML projects.

Communicate guarantees and limits clearly

Comfort with high-school level math, including basic algebra, probability, and statistics. No advanced math or calculus required.

High-school level math

Basic proficiency in Python. You should be able to read and write simple code using tools like pandas, numpy, and scikit-learn.

🐍 Python

No prior experience with Conformal Prediction is required—everything you need is explained from the ground up.

No  Conformal Prediction background - not needed

Senior Data Science Leaders at Global Brands Titles:&nbsp;VP, Director of Data &amp; Analytics, Head of AI

Mid-Level Data Scientist in Tech, Finance, or Industry Titles:&nbsp;Lead / Senior Data Scientist, Quantitative Researcher, ML Engineer

PhD Researcherc/ Academic in Data Science, Consultant Titles:&nbsp;Professor, PhD Student, Assistant Professor, Researcher, Consultant

H-E-B

Royal Holloway

Stanley Black & Decker

🔥 Enrollment Open — Limited SpotsThis is a deep, zero-fluff forecasting program built for real business impact, not academic perfection.We cap attendance to keep the experience focused and high-signal (not mass-market). Pricing increases for future cohorts.Built for real-world forecasters<ul><li>Models work in notebooks but fail in production?</li><li>Tutorials ignore operational constraints?</li><li>Want to lead forecasting with clarity and credibility?</li></ul>This course focuses on what works under pressure, at scale, and when decisions carry weight. Forecasting mistakes don’t just fail—they compound and get expensive.You’ll learn to:<ul><li>Build production-grade forecasting systems</li><li>Apply modern Machine Learning where it truly helps</li><li>Quantify uncertainty and communicate risk</li><li>Align forecasts with business outcomes</li></ul>Instructor: Dr. Valeriy Manokhin, MBA, CQF — ML/forecasting expert, 7× book author, with experience building forecasting systems for large multinationals and startups.Not: a pandas crash course, blog rehash, or black-box shortcut.✅ If you’re ready to ship forecasting that earns trust and drives results—join the next Cohort.

Master proven forecasting methods and build systems that drive real results.


Design end-to-end forecasting systems that work reliably in production.

Apply the right methods

Evaluate forecasts correctly.

Fix production failure modes.

Communicate forecasts clearly.

Lead forecasting initiatives.

No prior forecasting or ML experience needed. All key concepts are introduced from scratch—but taught quickly and practically.

No prior forecasting knowledge needed

For Data Scientists aiming to master real-world forecasting and build robust, scalable models that deliver accurate, actionable busines

For Machine Learning Engineers focused on building reliable, scalable forecasting systems that perform in real-world.

For Researchers, students, and Professors seeking to apply advanced forecasting techniques and contribute to cutting-edge research

Move from point forecasts to decision-grade probabilistic forecasts you can trust, explain, and defend in production.

Probabilistic Forecasting Expert

ML expert and 7× book author building AI forecasting systems

Probabilistic forecasting is hot — because point forecasts no longer suffice.Probabilistic forecasting has shifted from a niche topic to a critical professional skill. In planning, finance, energy, supply chains, and operations, decisions are not made on a single number — they are made under uncertainty, risk, and asymmetric costs.Yet most teams are unprepared. They can generate point forecasts, but struggle to produce uncertainty that is calibrated, interpretable, and defensible. Intervals are often wrong, metrics misaligned with decisions, and “uncertainty” collapses outside notebooks. When stakeholders ask, “Can we trust this?”, there is no clear answer.This course closes that gap.You’ll learn how to build decision-grade probabilistic forecasts: quantiles, predictive distributions, proper scoring rules, calibration diagnostics, and evaluation frameworks aligned with real business outcomes. This is not about hype or exotic models. It’s about mastering methods that work in production and hold up under scrutiny.Probabilistic forecasting is hot for a simple reason: real decisions are probabilistic — forecasts must be too.

From fragile point forecasts to decision-grade probabilistic forecasting

Build calibrated probabilistic forecasts instead of fragile point predictions.

Communicate uncertainty so stakeholders trust and use your forecasts.

Produce quantiles, intervals, and predictive distributions correctly.

Evaluate with proper scoring rules aligned to real business costs.

Diagnose and fix miscalibration before it breaks production.

Lead forecasting projects with authority and defensible uncertainty.

<h3>Senior DS/ML engineers who ship forecasts and need calibrated uncertainty, proper evaluation, and production-ready probabilistic outputs.</h3>

Forecasting &amp; planning leads who own business decisions and need uncertainty they can explain, defend, and align with costs and risk.

Executives/tech leaders who rely on forecasts and want mental models to challenge assumptions and steer strategy under uncertainty.

🔥 Enrollment Now Open — Limited Spots for Cohort 1 This is the most rigorous, zero-fluff data science course available—designed for real-world performance, not leaderboard optics. It’s built to help you avoid costly ML failures and deliver projects that are validated, trusted, and decision-ready. Enrollment for Cohort 1 is now open.Spots are limited and pricing may increase without notice. We cap enrollment intentionally to keep this a focused, high-impact experience—not a mass-market funnel. 🔒 Secure your seat and build the skillset top ML teams use to prevent failure and ship models that actually work.🧠 Built for Real-World Data Scientists, Technical and Business Leaders✅ Tired of models that look great in notebooks but fail in production?✅ Want to avoid being the next Zillow or AI ethics headline?✅ Need to build systems that decision-makers trust—without overselling? This course isn’t about trendy frameworks or flashy benchmarks. It’s about mastering the core principles of&nbsp;evidence-based modeling—rigor, reproducibility, and relevance—so your models deliver value, not damage. The real world isn’t Kaggle. Mistakes here don’t just fail—they mislead, waste budgets, and erode trust. 💡 You'll Learn How To:💎 Audit your ML pipelines for hidden risks like leakage and metric misuse💎 Design projects with testable hypotheses and stakeholder alignment💎 Evaluate models with calibration, prediction intervals, and external validation💎 Communicate uncertainty without undercutting credibility💎 Ship reproducible workflows that survive peer review&nbsp;and&nbsp;executive scrutiny Each session blends the theory that matters with practical tools you can deploy immediately—whether you're building models in production or reviewing them at the leadership table. By the end, you won’t just understand what went wrong in failed projects. You’ll know how to build ones that don’t. 👨‍🏫 Meet Your Instructor: Dr. Valeriy Manokhin Dr. Valeriy Manokhin is an internationally recognized expert in machine learning, forecasting, and uncertainty quantification. He’s known for bridging academic rigor with real-world impact—turning research into results that scale in production. ✅ Designed evidence-based ML systems for Fortune 500 companies and high-growth startups✅ Authored 4 bestselling books on forecasting, uncertainty, and applied machine learning✅ Published peer-reviewed research in top ML journals on Machine Learning, Forecasting and Conformal Prediction✅ Outperformed leading consulting firms in competitive AI tenders✅ Delivered mission-critical DS/ML systems for global enterprises✅ Designed industry-first training on Conformal Prediction and Modern Forecasting✅ Helped teams avoid multi-million dollar mistakes by upgrading their modeling rigor His evidence-based teaching is trusted by senior data scientists, ML engineers, researchers, and leaders across industries. 🌍 Trusted by Professionals From:Amazon, Meta, Google, Morgan Stanley, Bayer, NTT Data, Spotify, Capgemini, Daybreak AI, and more—plus PhD researchers and faculty from top institutions like UCL, UBC, TU Munich, and KTH. Students range from principal data scientists and ML engineers to analytics directors, researchers, and technical founders. ❌ What This Course Is Not🚫 Not a Python 101 or sklearn tutorial🚫 Not a repackaged blog post or buzzword bingo🚫 Not a black-box shortcut or AI hype machine This is a practical, strategic, zero-hype program for those who want to lead with&nbsp;rigor, credibility, and clarity&nbsp;in data science. ✅ Final CallIf you're ready to stop shipping untrustworthy models and start building AI and ML systems that withstand scrutiny, this is your moment. Cohort 1 is now open.Seats are limited. Price may rise without notice. 📌 Enroll now to lock in your spot.

Confidently build ML projects that are trusted, validated, and reproducible.


Course syllabus

Tuesdays & Thursdays

Course schedule

Data Scientists &amp; ML EngineersAvoid silent failures and ship ML models that are validated, trustworthy, and built for the real world.

Executives &amp; Decision-MakersLearn how to assess AI/ML project risk, ask the right questions, and ensure your investments are evidence-based

Researchers &amp; AcademicsBridge the gap between ML theory and practice by applying scientific rigor to real-world, production-grade data scie

Who is this course for

What you’ll get out of this course

Give students an idea of how they can expect to grow throughout your course. Include specificity and precise results so students can benchmark exactly what they’ll learn.

Audit real-world ML pipelines to catch errors like data leakage, poor validation, and misleading performance metrics.

Redesign flawed DS projects by applying evidence-first scoping, metrics, and validation strategies.

Evaluate model trust with calibration, external validation, and prediction intervals that go beyond accuracy.

Create reproducible workflows using evidence-based methods to reduce bias and improve reliability.

Deliver evidence-backed insights by clearly communicating uncertainty and model limitations to any audience.

Learn to build reliable, real-world ML & AI by applying evidence-based practices that prevent failure and boost trust in your models.


Learn directly from Valeriy Manokhin, PhD, MBA, CQF

Valeriy Manokhin, PhD, MBA, CQF

Courses

Applied Conformal Prediction

Modern Forecasting Mastery: From Models to Decisions

Probabilistic Forecasting Mastery

Evidence-Based Data Science

Lightning Lessons

Unlock Accurate AI Forecasts with Superior Metrics