Move from ad-hoc AI usage to a clear, repeatable workflow for shipping production software with confidence.

Staff Eng, currently AI Consultant

Vignesh Mohankumar

Founding Engineer @ Multiple Exited Startups 

January Accelerator

March Accelerator

Most engineering teams are already using AI — but without a shared framework, it often creates more risk than leverage.Engineers skip planning, generate large diffs, and rely on reviews to catch mistakes. Juniors hesitate to use AI at all. Seniors are skeptical. Managers struggle to define what “good” AI usage looks like. The result is inconsistency, bad habits, and uncertainty around ownership.This course exists to reset the baseline.AI doesn’t replace engineers — it changes what engineers are responsible for. The highest-leverage work is no longer typing code faster, but exploring systems, planning changes clearly, reviewing output skeptically, and maintaining ownership of what ships.In this accelerator, you’ll learn a repeatable, production-ready workflow for using AI responsibly. We focus on how staff engineers actually work: turning vague ideas into reviewable plans, generating code incrementally, and holding humans accountable for outcomes.This is not a vibe coding course. It’s a mindset and workflow shift designed to make AI safe, fast, and trustworthy in real production environments.

AI has changed software engineering. This course shows how to adapt.

Plan and ship AI-assisted features without losing ownership

Use AI to explore and understand large codebases faster

Apply asynchronous agents safely and intentionally

Improve PR quality and reduce review friction

Build shared standards for AI usage across teams

You should be comfortable building and maintaining features in your primary language or stack so you can focus on workflow, not syntax.

Experience shipping production software

Engineers who want consistent, production-safe workflows, not guesswork or oversized diffs.

Team leads and managers responsible for AI adoption who need practical standards, not hype or tool comparisons.

Engineers tired of vibe coding and social-media advice who want real workflows from someone shipping code daily.

By the end of this course, you’ll transition from a software engineer to a confident AI engineer—able to identify the right problems, design

AI Engineer & Consultant for F-500 firms

Sujeet Mishra

AI Engineer | Entrepreneur | AI Consultant & Trainer for Fortune-500

AI/ML | Search | Ex-Microsoft, Yahoo

Prasenjit Mukherjee

Adjunct Faculty | Quantum ML & Big Data | Ex-Microsoft, Yahoo | IIT Madras

Cohort 1

For software engineers, moving into AI engineering is not just about learning new tools—it’s a mindset shift.Over the past year, we’ve worked with engineering teams, from Fortune 500 companies to unicorn startups, helping them adopt AI-native and agent-based systems. One pattern shows up repeatedly: engineers don’t struggle because they can’t build, but because AI systems behave very differently from deterministic software.⚙️ Engineers must learn how to design, evaluate, and operate probabilistic systems, reason about failures and trade-offs, and think end-to-end—from use-case identification to production and scale.Many engineers hesitate because they believe AI engineering requires deep ML or statistics expertise. In reality, modern AI engineering is about system design, evaluation, observability, and production readiness—skills engineers already have but need to apply in a new context.🚀 This course provides a practical 8-week roadmap to make that transition by building and extending a real, production-grade agentic system.By the end, you won’t just understand AI systems—you’ll think and operate like an AI engineer.For queries, write to us: ai-education@theseuslab.ai

An 8-week roadmap for software engineers to become production-ready AI engineers

Use LLMs to 10x day-to-day engineering productivity

Design and build agentic systems using LangChain/LangGraph

Build reliable agentic systems by designing for evaluation and observability

Take AI ideas from use-case discovery to production and scale

Apply everything you learn to a real, end-to-end capstone project

Basic understanding of SDLC concepts like databases, APIs, frontend/backend systems, and servers

Familiarity with Software Engineering Fundamentals

Some programming experience, even if not recent—or willingness to spend ~8 hours pre-course to learn Python (we will provide resources)

Prior Programming Exposure (or Willingness to Ramp Up)

Openness to learning, experimenting, and thinking differently

Curiosity and Willingness to Experiment

Software engineers looking to transition into AI engineering roles and build production-grade AI systems

AI engineers and data science professionals who want to get a hands-on experience of building, evaluating and scaling agentic AI applications

Product managers and Tech leaders seeking a practical, system-level view of how production-ready AI applications are designed and shipped

"Python for Beginners" is a foundational course designed for absolute beginners, to introduce you to programming.

Winter 2026

Founder and CEO, ScholarU

Roberto Reif

20+ years of Technology and Data Trainings

Fall 2025

Summer 2026

Why should I learn Python?Python is a versatile, beginner-friendly programming language that holds significant value across numerous industries, including web development, data analysis, artificial intelligence, machine learning, and scientific computing. Its broad applicability has created a strong demand for Python skills, opening doors to excellent job prospects and career advancement.About the CourseThis course is designed to prepare you with the essential skills to use Python effectively. You'll dive into fundamental data types such as integers, strings, floats, booleans, lists, dictionaries, sets, and tuples, while mastering core programming concepts like conditionals, loops, functions, and modules. With hands-on exercises, you'll apply what you learn in real-world scenarios.Who Should Take This Course?This course is intended for individuals starting a career in coding, data analysis or data science, as well as professionals seeking to expand their expertise in the field.Course Duration:The course will be delivered live online through Zoom, offering a total of 10 hours of instruction.

Absolute beginners can confidently learn Python!

Get started with Python basics, including syntax and setup

Use comments, variables, and data types effectively

Apply conditionals and booleans to control program flow

Work with lists, ranges, dictionaries, sets, and tuples to manage data

Implement loops and functions to write efficient, reusable code

Import modules and format strings to extend and refine functionality

Gmail account

We will run our live online sessions with Zoom

Zoom account

A thirst for knowledge

Career Changers - Professionals who are transitioning into tech roles and want to learn Python as a first programming language.

Aspiring Data Analysts or Scientists - Individuals looking to build Python skills in data analysis, machine learning, or data science roles.

Excel Users - Experts aiming to enhance their skill sets by mastering advanced and more efficient analysis techniques.

Get hands-on experience with modern forecasting tools & learn from case studies of the toughest forecasting challenges in the industry

Director of Engineering, Databricks

Tim Januschowski

Principal Applied Scientist & Engineer

Jan Gasthaus

Software Engineer & Applied Scientist

Cohort Q1 2026

Learn how to use machine learning techniques for predicting future outcomes in time series to optimize business processes. The course features practical lessons heavily that we derived from two decades of working on some of the world's hardest forecasting problems at Amazon retail, Zalando and for AWS and its customers. You'll pick up the necessary theory, get hands-on example and learn about the tricks of the forecasting trade.

Apply the right modern forecasting method to your problem

Identify the business problems that benefit from modern forecasting techniques

Apply appropriate techniques to maximize the effectiveness of your solution

Develop the skills to present results effectively to a non-technical audience

Data Scientists who want to go beyond standard ML/AI problems and solve forecasting related business problems

Business analysts with familiarity in machine learning in industry settings who want to uplevel themselves in a top ML application domain

Economists and Applied Scientists who want to apply industry-proven modern forecasting techniques

Build robust LLM-powered apps, chatbots, and agents. Learn by writing real code, one line at a time.

CEO @ Actualize

Jay Wengrow

Software Engineer and Educator, Author of A Common-Sense Guide to AI Engineering

Cohort 2

Do you have a grasp of basic LLM principles but struggle to put it all into code? Tired of scattered documentation, outdated tutorials, and frameworks that impress in demos but collapse in production?You’re not alone. Building with large language models doesn’t have to be confusing - it can be clear, structured, and deeply rewarding.In this course, you’ll build real LLM-powered apps, one line of code at a time. You'll see how an app is built from scratch in real time, and follow along. Along the way, you'll discover not only how to implement these systems, but the why behind each line of code.From chatbots to agents and RAG to evals, you’ll learn the core concepts of building atop LLMs. More importantly, you’ll get how LLMs “think” - allowing you to guide them to do what you want despite their nondeterministic nature. Additionally, you’ll be able to balance quality, latency, and cost, making big-picture decisions about AI-powered app architecture.AI engineering isn’t just another branch of software development - it’s a different mindset altogether.

Gain hands-on AI Engineering proficiency by writing real code

Gain real-world intuition for working with LLMs

Reliably guide LLMs instead of getting unpredictable results.

Build retrieval-augmented assistants that can access proprietary knowledge

Use evals to systematically optimize your LLM app’s performance over time

Assemble agents that act upon the real world

This course is designed for current software engineers who will use Python  to build LLM-powered apps. We won't teach basic coding here.

Software Engineering

We'll be using Python, but we'll keep it simple in case you're more familiar with other coding languages.

Note about Python

If you want an inside look at what's involved with building AI apps but don't plan on coding yourself, you'll still follow what's going on.

Exception: Product Managers

Software engineers who want to build LLM-powered chatbots and agents, or break into AI engineering more generally

Data Scientists/Engineers who want to build their own LLM-powered apps

Product Managers who want to understand AI engineering from the coding perspective

AI &amp; Machine Learning Modeling- 2:00 PM—4:30 PM (EST) 24th of February 2026. Vibe Coding with AI 2:00 PM—4:30 PM (EST) 26th of February 2026. Machine Learning Data Prep with Python2:00 PM—4:30 PM (EST) 3rd of March 2026. Introduction to Machine Learning2:00 PM—4:30 PM (EST) 5th of March 2026. Introduction to LLMs 2:00 PM—4:30 PM (EST) 12th of March 2026. Introduction to RAG – Retrieval Augmented Generation2:00 PM—4:30 PM (EST) 19th of March 2026. Introduction AI Agents2:00 PM—4:30 PM (EST) 26th of March, 2026. Build and Launch Your AI Project2:00 PM—4:30 PM (EST) 2nd of April, 2026.&nbsp; If you’ve missed any, don’t worry—you still have access to the 13 sessions that passed including our On-Demand courses: * Data &amp; Generative AI Literacy*.Data Wrangling With SQL* Linear Algebra* Statistics and Hypothesis Testing* Introduction to Math for Data Science You have access to:* All 13 recordings (30 hours)* All code notebooks ( ~ 20)* All 13 slide decks As these are primer courses, no prior experience is necessary. Individual setup prerequisites will be provided prior to each course. From AI Novice to Job-Ready AI Skills By the end of this AI Bootcamp, students will transform from AI novices to confident practitioners, equipped with the skills and knowledge to tackle complex data challenges and build sophisticated AI systems. They'll move from understanding basic concepts to applying advanced AI techniques in real-world scenarios, gaining the hands-on experience and expertise needed to drive innovation and lead in the rapidly evolving AI landscape. In this Bootcamp, you'll be learning from a leading expert in the field of AI, ensuring that you receive top-tier instruction and insights. The program includes optional on-demand prerequisite courses to build your foundation, followed by 8 live, hands-on sessions that dive deep into AI concepts and applications. You'll have access to take-home exercises to reinforce your learning, and all sessions are recorded so you can revisit the material anytime. Additionally, office hours are available to provide personalized support and help you succeed in mastering the course content.

🚀Our next AI Mini Bootcamp live session is happening on Tue. 24th of February

Course syllabus

Live Courses: Weekly on Wednesdays

3 optional on-demand prerequisites will be available 2 weeks before the bootcamp start date.

ON-Demand Prerequisites

Held weekly, office hours day may vary based on student cohort preferences.

Weekly Support Office Hours

Each weekly course will include take-home exercises that include solution code notebooks

Weekly Exercises

Course schedule

NOVICES: This course is designed for AI novices, including professionals, students, and enthusiasts eager to dive into the world of AI

ENGINEERS: Software engineers and other technical professionals seeking to deepen their understanding of AI and enhance their skills

Product managers and Builders who want to understand and build AI into their products, leveraging AI-powered innovation to stay competitive

Regardless of your current skill level, this AI bootcamp will help transform you from AI novice to  confident practitioner

What you’ll get out of this course

<ul><li>Free Courses and Free Content | ODSC Top Picks</li><li>Courses, Webinars <a href="https://aiplus.training/on-demand/" rel="noopener noreferrer" target="_blank">Access to all On-Demand Library</a></li><li>Skills and Coding Assessments | Feedback</li><li>Access to Virtual Agentic AI Summit in summer 2026</li><li>Access to Monthly Live Trainings</li><li>ODSC Conference Pass Credit</li><li>+ Certificates of C</li></ul>

1 year free access to Ai+ Training & Agentic Ai Summit

In this bootcamp, you'll complete a total of 8 courses, including 5 live, hands-on training sessions and 3 optional prerequisite courses, all taught by a leading expert in the field. You'll also receive code notebooks, comprehensive slides, and access to weekly office hours

A Comprehensive AI Bootcamp with Expert Guidance and Resources

Develop a strong command of Python programming, with a focus on the essential libraries and frameworks used in machine learning. This includes mastering data structures, control flow, and functions, as well as advanced topics like data manipulation with Pandas

Python Proficiency: Gain expertise in Python programming, specifically tailored for machine learning applications.

Build a solid foundation in machine learning by understanding and implementing key algorithms and concepts. You'll explore essential topics such as supervised and unsupervised learning, model training and evaluation, feature engineering, and data preprocessing.

Fundamental ML Concepts: Understand and implement key machine learning algorithms and concepts.

Master the critical techniques for transforming raw data into a format ready for machine learning models. You'll learn how to clean and preprocess data by handling missing values, detecting and removing outliers, normalizing and scaling features, alongside feature engineering

Machine Learning Data Preprocessing: Learn techniques for data cleaning, preparation, and feature engineering.

A deep dive into the architecture and inner workings of large language models (LLMs). You'll explore the foundational principles that power these models, including transformer architecture, attention, and the process of training on vast datasets. In addition to prompt engineerin

LLM Comprehension: Understand the architecture and functioning of large language models (LLMs) and Prompt Engineering

Gain hands-on experience in designing and building sophisticated AI agents that can autonomously perform complex tasks. Learn core principles including how to integrate decision-making algorithms,and create responsive and intelligent agents. Additionally, you'll explore RAG

AI Agent & RAG Development: Learn to design and build AI agents capable of performing complex tasks autonomously.

Each class is hands-on to ensures practical proficiency in Python, ML concepts,, model, LLMs, prompt engineering, AI agents, and RAG,

Begin your bootcamp journey with 6 weeks pre-conference live and on-demand training to build confidence in the fundamentals.

Sheamus McGovern

September 2025 Cohort

March Bootcamp 2026

AI Fundamentals

Apply AI in your role

Career Development

Career development

Craft

Product

Engineering

Design

Marketing

Leadership fundamentals

Leadership

Founders

By Role

More

Justin Otero

Director of Engineering @ Navan | Career Coach | Hiring Manager

Understand the truth behind how promotions and raises actually work

What actually drives promotions

Talk tracks on what to say and ask so you can uncover gaps, understand the bar, and get aligned with your manager.

How to have a conversation with your manager

Identify areas you need to improve on and your next steps

How to create a clear and actionable path forward

How to Ask for a Promotion as a Software Engineer in 2026

Yan Wang, PhD

Staff Applied Scientist @ Samsara

Build a better-than-commercial photo resizing app in 5 mins (basic python skills required).

Build a prototype with ChatGPT and Python

GUIs used to be the way to access compute, but now GenAI has enabled accessing compute through chat.

Understand a new paradigm of accessing computes

Build often and fast to overcome the mindset bottleneck of productivity.

Transition your mindset from user to builder

AI-Assisted Programming for Non-Devs

Understand how probabilistic AI systems behave, fail, and are evaluated differently than deterministic software

How AI systems differ from traditional software

Learn the modern AI stack, the roles across its layers, and how skill expectations shift from software to AI engineering

The modern AI stack and AI roles

Understand a clear 8-week roadmap to close skill gaps and transition from software engineering into AI engineering roles

An 8-week roadmap to transition into AI engineering

See how engineers use agents, MCP, and tool calling to build applications in minutes instead of hours.

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

By Role

Software engineering

28 Courses

3 Lightning Lessons