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Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.

Cohort 4

Founder @ SwirlAI • Ex CPO @ neptune.ai

neptune.ai

Aurimas Griciunas

LinkedIn Top Voice in AI • Founder & CEO @ SwirlAI

Cohort 3

The End-to-End AI Engineering Bootcamp is an 8-week, cohort-based experience designed to turn technical professionals into full-stack AI engineers who can confidently design, build, and deploy production-grade AI systems.🛠️ What You’ll BuildYou’ll develop your own capstone project - a real-world AI application built sprint by sprint, applying each week’s concept to solve a business-relevant use case. By the end, you’ll present it live on Demo Day, with a working repo and deployed app you can showcase to hiring managers, CTOs, or investors. 🧑‍💻Technologies include:<ul><li>LLM APIs (Gemini, Claude, GPT, etc.).</li><li>Vector databases &amp; RAG.</li><li>AI agent libraries (LangChain, LangGraph, ADK, AutoGen).</li><li>Docker, FastAPI, Kubernetes, cloud deployment.</li><li>Observability, evaluation, and performance testing.</li><li>Modern communication protocols (A2A, MCP).</li></ul>🧠 How It WorksEach week follows a real engineering sprint:<ul><li>Sprint Lesson (Monday): Self-paced learning with videos, cheatsheets &amp; reference code.</li><li>Sprint Review (Tuesday): Live walkthrough with Aurimas + deep Q&amp;A.</li><li>Sprint Build Lab (Thursday): Live coding session to implement key sprint features.</li><li>Bonus QnA and Feedback sessions.</li></ul>🎬 Pre-course that will help you prepare <a target="_blank" rel="noopener noreferrer nofollow" href="https://youtube.com/playlist?list=PLeId95Z-0iyOATuFuHt3zP5enX3eCOsoq&amp;si=1xrntQdNOUkXwH6H">here</a>.

🚀 Build Real AI Products, Not Just Prototypes

Design and optimize RAG architectures

Engineer and orchestrate agentic systems

Design and deploy multi-agent systems for complex workflows

Implement structured prompt and context management

Apply LLMOps for observability and continuous evaluation

Build and deploy production-grade GenAI applications

Software Engineers &amp; DevelopersWho want to transition into AI Engineering and build, deploy and scale real-world AI systems end-to-end.

ML Engineers &amp; Data ScientistsWho want to architect, evaluate and scale production-grade agentic AI systems.

Technical Founders &amp; CTOsWho want to make AI a core product capability and confidently design and ship scalable AI systems.

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

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

Cohort 7

Cohort 8

🔥 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

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.

The best way to learn Python for Data Science and AI, with the help of AI.

16 yrs in AI & Data, 3xAuthor, Mentor

Mirza Rahim Baig

AI Leader, GenAI Startup Mentor, Educator, Author | Zalando, Flipkart, ZS

Cohort 202505

Cohort 2

Real skills for real data science jobs. No outdated tutorials. Think in logic and use GenAI wisely. One unique course. One capstone. Weekly 1:1 mentorship.Don't be a code monkey, be a data science doer. Each week features a hands-on case study. The course is unique - teaches techniques like pseudocode effectively for data science tasks and uses GenAI for assistance for maximum learning. No fluff, just practical skills for data science roles. Additionally, this course will fill a HUGE gap - teaching skills like API fundamentals, using OpenAI API, that are essential for modern data professionals. By the end of this course, students will be able to: -<ul><li>Apply Python programming concepts - specifically for data science tasks</li><li>Manipulate and analyze data using key Python libraries</li><li>Create effective data visualizations to communicate insights</li><li>Implement end-to-end data analysis projects</li><li>Create ML models using sklearn</li><li>Use APIs for real world data - foundational knowledge for modern data science/AI</li><li>Use OpenAI APIs to access GenAI models like GPT</li></ul>Note: This is not a regular course. You will learn ONLY through case studies, each week, to get extremely practical knowledge.

In weeks, not months. Logic first. Don't be a code monkey!

Learn Python by doing end-to-end analyses

For Data Science, not developer focused

Logic first approach for beginners

GenAI used wisely as validator, not a crutch 

Multiple projects for your portfolio

Modern data science foundations

Basic IT literacy is enough. If you can use technology to access this course, you're good!

No prerequisites!

Anyone seeking to make a career in Data Science/AI.

Graduates looking to get into Data science/ analytics / AI roles.

Professionals in adjacent roles who would like to move to data science roles.

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)

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

AI Engineering Bootcamp

Data Science Academy

AI engineers building practical machine learning and deep learning systems.

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.

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.

Learn to design and evaluate a production-ready media recommender: metrics, architecture, cold start, and drafting an effective roadmap.

Principal ML Engineer @ Disney, ex-Meta

The New York Times

The Walt Disney Company

ESPN

Columbia University

Katerina Zanos

Principal ML Engineer @ Disney, x-Meta, x-NYT

Build a production-ready Recommender System Blueprint for a media surface of your choice. This will be an artifact you can take back to work to guide implementation, align stakeholders, and make smarter roadmap calls.Most teams working on recommender systems don’t struggle because they lack algorithms. They struggle because they lack a practical, end-to-end playbook for shipping.You might know the building blocks (embeddings, retrieval, ranking, bandits), but still feel stuck on the hard parts:<ul><li>which metrics actually reflect what product wants</li><li>how to architect the system from logging to serving</li><li>what model to choose under real constraints</li><li>how to handle cold start and sparse data</li><li>and how to prove ML is worth it over heuristics</li></ul>I’ve built recommendation systems in production across news, feeds, and sports over the last 10 years (NYT, Meta, ESPN). This course turns that experience into a clear, reusable process you can apply to your own product without needing FAANG-scale infrastructure. By the end, you’ll be able to confidently design and evaluate production-ready recommenders, defend trade-offs, and use your Blueprint as a durable reference for future iterations.

Design and evaluate a media recommender system that ships to production.

Build a real-world mental model of recommenders (not just algorithms)

Design metrics and success criteria your team will actually align on

Architect the system: make the right calls on retrieval, ranking, and serving

Handle cold start with pragmatic strategies

Know when ML beats heuristics and how to prove it

You are able to interpret (not compute) common product metrics like retention/engagement and discuss tradeoffs.

Metric + product reasoning at a basic level

You can follow an architecture diagram and understand concepts like services/APIs, latency, reliability constraints.

Ability to follow system-level thinking

We will be working with specs, diagrams, and decision frameworks (not code).

Comfort making structured decisions from imperfect data

Senior ML Engineers &amp; Data Scientists who can build models but want sharper judgment on what to build next and how to defend it

Software Engineers transitioning into ML who want to think in systems + measurement, not just training code.

Tech Leads who need an end-to-end playbook to review designs, align metrics with product, and avoid expensive detours.

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

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

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