87% of ML models never make it to production. Most data scientists know how to build them, but not how to deploy, monitor, and own them end-to-end. Learn how to do all three, and become the data scientist your team depends on.
Hosted by Andres Vourakis
Senior Data Scientist • 8+ years experience
Full refund up to 7 days after start
Installments available on the regular rate
Hiring is shifting toward production skills, not just modeling. Data scientists who can ship end-to-end are seeing the upside.
Deployment skills are cited as the key differentiator in data science hiring.
One XGBoost project, six layers. Each week adds a layer to the same repo. You don't build six toy demos. You evolve one real system from a notebook into a deployed, monitored, alert-firing production service.
Wrap your model in a FastAPI service and package it in Docker. The same container runs identically on your laptop, a colleague's machine, or on AWS. Predictions are one curl away.
Self-host MLflow backed by S3. Log experiments, compare runs, and register versioned models so the question "which hyperparameters did we use last quarter?" has an actual answer.
Stand up an EC2 instance and set up GitHub Actions. Push to main, watch the pipeline build, test, and deploy your service automatically. No more SSHing into a server to drop in a new model.
Add drift detection with Evidently, structured logging, and a Streamlit dashboard. Wire alerts to Slack so the system tells you when reality starts diverging from training data.
Every managed ML platform (SageMaker, Vertex AI, Databricks) is a UI on top of these patterns. Learn them once, swap platforms whenever you want.
Each tool maps to a layer of the system you'll build.
PythonCookiecutter Data Science
FastAPI
Docker
MLflow
S3AWS EC2
GitHub ActionsCI/CD
Evidently
Streamlit
SlackYou'll add every tool above to your resume, signaling to future employers you've worked with the modern, in-demand stack data teams actually hire for.
Most MLOps content was built for engineers. Almost none of it teaches you what a data scientist needs to take a model end-to-end.
This program is built for the people who can build models but haven't shipped one their team actually depends on, regardless of title:
You've trained models that produce numbers you trust. You haven't deployed one your team depends on, and "go talk to engineering" has gotten old. You want to become the data scientist your team can't ship without.
You've shipped pieces of ML systems but never owned one end-to-end. You want to be the kind of MLE who designs and ships whole systems, not the one plumbing other people's models into production.
Note: Not suitable if you're already shipping production ML systems day-to-day. The content would feel basic. This program is for the people on the other side of that bridge.
Everything you need to ship your first production ML system
Pre-recorded lectures every Friday plus optional Sunday AI Chats with an experienced instructor.
Get unstuck fast with peer and instructor support all week long.
Weekly project work that builds the system layer by layer.
Leave with one deployed, monitored ML system on AWS you can show your team.
Showcase your production ML skills.
Keep all materials forever.
From notebook to a live, monitored production system. One ML project, six layers.
Pre-recorded lectures are released every Friday (week by week), followed by an optional AI Chat on Sundays. AI Chats cover topics beyond the lectures with your instructor.
Lectures unlock week by week so you can progress with the cohort without feeling overwhelmed.
Get direct guidance from an experienced instructor through the community and weekly AI Chats
Senior Data Scientist @ Nextory | 8+ yrs in tech & applied AI/ML
I've been in your shoes as a data professional figuring out how to grow. After more than eight years in data science, the last year marked a clear shift: the market was changing fast, I felt some stagnation, and staying relevant became a real concern. I took that seriously and went deep into building AI and ML systems that hold up in production. Shipping that work is what removed the uncertainty for me, and it is what every program at Future Proof Data Science is built around.
Rather than treat infrastructure as someone else's problem, I built the engineering skills myself: containerized services, tracked experiments, CI/CD pipelines, and monitored deployments. The same patterns this bootcamp teaches are the ones I use day to day on real models that real teams depend on.
More than anything, building these skills is what made me a full-stack data scientist, the kind who can navigate a fast-changing job market with ease instead of worrying about staying relevant.
Join the founding cohort of ML in Production.