1:1 WITH DR. ALEENA BABY · ONE PARTICIPANT AT A TIME
You will build a deployed ML system – containerised, experiment-tracked, and interview-ready – with direct guidance from an Applied Data Scientist / AI Engineer who made the exact transition you are trying to make. 5 out of 6 participants have used their sprint project to land interviews.
5 of 6
Got Interviews With Their Project
1:1
One participant per sprint
Live URL
Deployed model endpoint
€499
One-time investment
0
Projects Completed
The Problem Nobody Talks About
You have the skills. You have done the research. But every time an interviewer says “show me a project you’re proud of,” all you have are Jupyter notebooks, scattered scripts, and academic papers that do not translate.
Meanwhile, candidates with half your technical depth are getting hired – because they can show a deployed model, a clean GitHub repo, and a 3-minute pitch that sounds like a business case, not a thesis defence.
The gap is not your knowledge. It is your portfolio.
Same pipeline. Same deliverables. Same direct access to Dr. Aleena. Different pace based on where you are.
For experienced practitioners who have built ML models before and want to level up to production standards fast.
For researchers who know Python but have never built an end-to-end ML project in production. More time to absorb, practice, and build confidence.
Not sure which track fits? Describe your background in the application – Dr. Aleena will recommend the right one after reviewing your profile.
Expert track: one phase per day. Intermediate track: one phase per week.
Define the ML problem, load your industrial dataset, analyse it with purpose, and produce a clean EDA notebook with documented decisions. No guessing – every choice is justified.
Baseline first, always. Then a full sklearn Pipeline with preprocessing, model selection, and every experiment logged in MLflow. Reproducible, explainable, production-ready.
Your model becomes an API. You write the FastAPI endpoint, containerise it with Docker, and deploy it to a live public URL on Streamlit. Anyone can call your model from anywhere.
Structure your GitHub repo like a professional. Write the README that recruiters actually read. Draft your LinkedIn post. Rehearse your 3-minute project pitch for interviews.
Everything you’ll use is what actual ML engineering teams use in production. No toy frameworks. All tools are free or have generous free tiers. You will not spend a single euro on infrastructure during this sprint.
Application Required
This is a 1:1 engagement, not a mass course. We need to see that you have the foundation to get real value from the sprint. Here is what we look for.
Not ready yet? Build the foundation first:
Each phase is a focused session with Dr. Aleena. Not a lecture – you build, she guides. Real-time feedback, real problems, real code.
Sent before your sprint starts. Environment setup, prep videos, fundamentals refresher, and a checklist so Phase 1 isn’t wasted on basics.
FastAPI serving template, MLflow tracking boilerplate, EDA checklist, Dockerfile, GitHub repo structure – yours to keep and reuse.
Every session is recorded. Rewatch any part, revisit the deployment steps, or catch anything you missed in the flow of building.
If anything feels shaky – Python, pandas, sklearn, Git – a reference repo covers the foundations you need before and during the sprint.
Questions after the sprint? Reach out via email or LinkedIn for 30 days. Getting the job matters as much as building the project.
24 chapters. 175+ pages. 100+ code examples. Covering everything from data versioning and experiment tracking to CI/CD pipelines and LLMOps. Yours to keep as a permanent reference long after the sprint ends.
This guide is sold separately – sprint participants receive it at no additional cost.
Get the Guide
Real Projects, Real Interviews
5 out of 6 sprint participants have used their project to land interviews. 3 are currently interviewing with their sprint project as their lead portfolio piece.
Cancer detection pipeline with image data
Sreerag – interviewing with this project
Probabilistic 24-hour energy demand forecasting for the German grid
2 interviews landed with this project
CNC machine sensor data from Bosch
Interviewing with this project
Real economic indicators from Deutsche Bundesbank
Interviewing with this project
Confidence-scored cell population pipeline with DVC versioning and MLflow tracking
Quantized CNN on cardiac signals, deployed as a live public inference demo
In Their Own Words
“I had spent years in academia building models, but I didn’t realize how far that was from industry standards. My GitHub was just notebooks and plots, no structure, no deployment. During the sprint, I built my first real API and documented a full workflow. By Week 3, it finally clicked: I wasn’t just experimenting anymore, I was solving a business problem. That shift alone changed how I walk into interviews. Now I talk about deliverables, not just accuracy, and companies notice.”
“Honestly, my code was a mess – one long script, hardcoded paths, no tests. It worked, but nobody else could use it. In ML4 Sprint, I got a crash course in professional standards: modular code, configs, unit tests, and error handling. The daily reviews were tough, but by the end, my repo looked like something I’d be proud to show any hiring manager. The next interviews were different: instead of criticizing my code, reviewers complimented it. That’s never happened before.”
“In every interview, I froze when they asked, ‘Show me a project you’re proud of.’ All I had were Jupyter notebooks. After ML4 Sprint, I walked into my next interview and said, ‘Let me show you how this predictive maintenance dashboard works.’ Suddenly, I wasn’t just explaining theory – I was demoing a live system. The energy in the room completely changed. That project didn’t just get me hired; it gave me the confidence I’d been missing.”
You won’t be stuck alone. Every session is live with Dr. Aleena – she guides you through blockers in real time. Between sessions (especially on the 4-week track), you have async support via email.
The sprint is designed so you finish – whether that’s 4 days or 4 weeks. Each phase builds on the last with clear milestones. If something runs over, you have 30-day follow-up access to get it across the line.
No. The expert track is 4 consecutive days. The intermediate track is 4 consecutive weeks with one session per week. Both have a fixed schedule – that structure is what makes it work.
Yes. 5 out of 6 previous participants have used their sprint project to land interviews. 3 are currently interviewing with their sprint project as their lead portfolio piece. You leave with a deployed model, a professional GitHub repo, resume bullet points, and a rehearsed pitch.
The sprint covers tabular and industrial data, not yet text data. But the skills – pipelines, deployment, MLOps, portfolio structure – transfer to any ML domain. Previous projects have spanned biomedical, business analytics, and sensor data.
On your own, you’d spend weeks guessing at best practices. Here, you get direct feedback from someone who builds production ML systems and reviews code professionally. Every decision is guided, every shortcut is intentional.
Sessions are scheduled in CET (Central European Time). We accommodate other timezones on a case-by-case basis – mention your timezone when you apply.
No. Everything runs on free tools – Python, Docker Desktop, VS Code, and free-tier cloud services. The pre-sprint prep portal walks you through setup before your first session.
You should be comfortable writing Python functions, using pandas for data manipulation, and have used scikit-learn at least once. If you’ve completed a data science course or used Python in your research, you’re ready.
If your profile isn’t a fit for the sprint yet, you’ll receive honest feedback on what to work on first. Many applicants start with the self-paced ML Pipeline Guide and reapply when they’re ready.
This is a private, 1:1 engagement – not a group course. Spots are limited to one participant at a time. €499, one-time.