Probabl Services, Forward Deployed Engineering

Forward
Deployed Engineers
for ML teams.

Our scikit-learn maintainers, working alongside your data scientists. Pioneers in Tabular Foundation Models, embedded into your stack with expertises ready to plug in: data science agents, time series, causal inference, survival analysis.

Talk to an FDE Get a free stress test
Built on scikit-learn Skore licenses included Local, SaaS, private cloud
Engagement #2026-114 ● Active

Stress test and AI-powered industrialization for a 14-person DS team.

FDEs on site2
Models audited47
Skore tierTeam
Time to first PRDay 3
Quick wins Industrialize Explore AI
Stress test, free
Founded by core scikit-learn maintainers
200M+monthly downloads
15+years shaping Python ML
2×scikit-learn growth in 9 months
4.5Mmonthly doc visitors
The frontier, applied

Pioneers on Tabular Foundation Models. Expertises ready to embed.

We do not just talk about what is next in data science, we build it, publish it, and deploy it inside your team. Our Forward Deployed Engineers bring frontier methodology with them, ready to plug into the problems you already have on your roadmap.

Pioneering work

Tabular Foundation Models, before they go mainstream.

LLMs commoditized in 18 months. Tabular Foundation Models will follow the same curve, on a domain that drives 80% of enterprise prediction. We publish on it, train models, and ship the methodological scaffolding that lets you evaluate one without lying to yourself.

We publish and contribute

Active research on pre-trained tabular models, with the people who maintain the libraries you already use.

We train and benchmark

Honest cost vs accuracy comparisons across foundation models, classical ML, and hybrid pipelines.

We deploy them with you

FDEs bring the patterns, your team keeps the leverage. No black box, no vendor lock-in.

Harness your data science agents

Ready-to-embed frontier expertises.

When your roadmap touches a hard methodological problem, you do not need a six-month hire, you need an FDE who has shipped it before.

01

Data science agents, harnessed.

Wire Claude, Codex, and agentic tools into your team with a feedback loop that catches generated mistakes before they reach production.

02

Tabular Foundation Models.

Pre-trained models for tables, evaluated against your domain baseline, on your data, with leakage detection on by default.

03

Time series and forecasting.

From classical baselines to deep models, with backtesting, drift detection, and metrics that survive contact with the business.

04

Causal inference.

Move past correlation. Estimate uplift, treatment effects, and decisions you can defend in front of a CFO, not just a leaderboard.

05

Survival analysis.

Time-to-event modelling for churn, equipment failure, clinical risk, with calibrated uncertainty instead of false precision.

06

Evaluation, made trustworthy.

The methodological layer your team has been faking with screenshots: cross-validation, model cards, comparison reports, by default.

Why change, what we see in the field

The question is not "when do we start?" anymore. It is "how do we do this without breaking what works?"

Trend 0101

AI is accelerating traditional ML, not replacing it.

Coding assistants and pre-trained models are real. They generate scikit-learn by default. Downloads doubled to 200M per month in 9 months. Traditional ML became the execution layer for AI.

Trend 0202

No shared standards, no tooling for the data scientist's job.

Buy a closed suite, glue 12 components together, or build internal tooling that becomes debt. The bottleneck is not compute, it is the absence of a shared scientific working environment.

Trend 0303

The translation gap with the business is widening.

Data scientists speak statistics, the business speaks business. Decisions stall. Models wait for a validation that gets lost between teams. The translation cost lands on the practitioner.

Trend 0404

Trust is harder to earn, easier to lose.

AI-generated pipelines look correct and can be silently wrong. Reproducibility, peer review, leakage detection, the principles do not disappear because code generation is instantaneous.

How we work, three ways in

Pick the door
that fits where you are.

Adopting AI is obvious. Industrializing your data science practice is a prerequisite. Business impact is the reason this team exists. Our FDEs meet you at any of three entry points, every engagement ships Skore licenses and our maintainers' time, side-by-side with your team.

01

Quick wins

Audit what is in production. Zero risk, zero cost.

02

Industrialize and best practices

Standardize how your team builds, evaluates, ships.

03

Explore AI in your context

AI-powered POCs with Claude, Codex, and foundation models.

01
01, Quick wins

Find the cracks before production does.

Two engagements you can run with your existing stack, no procurement, no migration. Designed to surface risk and create the first asset your team will reuse for years.

Service 01 Free engagement

Stress Test

What we do
  • Stress test of your model registry
  • Stress test of your production models
  • Stress test of compliance and governance posture
Outcome

Reduce risk and uncertainty. You receive a complementary audit report with prioritized recommendations, actionable in the same quarter.

Talk to an FDE
Service 02 Skore licenses

Inventory

What we do
  • Build your data-science model and experiments registry
  • Sync with MLflow as the source of truth
  • Tag, version, and surface reusable assets
Outcome

Synchronize with MLflow and start a repository of reusable ML assets, the first piece of leverage your team has had.

Talk to an FDE
02
02, Industrialize and best practices

Turn personal expertise into team leverage.

When tribal knowledge stops scaling, the answer is not another platform. It is a working environment shaped around the data scientist's actual job.

Service 01 Skore + FDEs

Data Science Project

What we do
  • Define and embed business-oriented metrics
  • Custom evaluation reports per project context
  • Stakeholder-facing model cards and reviews
Outcome

Align scientists and business stakeholders around the same numbers. Ship models faster, without sacrificing methodological rigor.

Talk to an FDE
Service 02 Skore + FDEs

Data Science Blueprint

What we do
  • Define your company's best practices, encoded
  • Project framework with reproducibility built in
  • Onboarding kit for new and junior practitioners
Outcome

Equip your data science teams with a framework that closes the communication gap with the business, by default, not as compliance theatre.

Talk to an FDE
Service 03 Skore + FDEs

AI-powered Roll Out

What we do
  • Productionize Skore across the practice
  • Embed FDEs into your team's review loop
  • Quarterly methodology review with maintainers
Outcome

Capitalize on the team's work. Stop depending on whoever is in the room, the methodology travels with the codebase.

Talk to an FDE
03
03, Explore AI in your context

Move fast on AI, without giving up rigor.

A short, scoped engagement to evaluate AI-assisted ML in your environment, with the methodological scaffolding to know whether what it produced is real.

Service 01 Claude, Codex, foundation models

AI-powered Data Science Proof of Concept

What we do
  • Pilot with Claude, Codex, or a foundation model of your choice
  • AI-generated pipelines validated with Skore methodology
  • Cost vs performance vs accuracy comparison report
Outcome

Explore the benefits of AI in your context with a feedback loop that catches generated mistakes before they reach production.

Talk to an FDE
POC report, sample run
Time to first model 2.1d Leakage caught 7/9 vs human baseline +4.1%
Ready when you are

The question is not
"when do we start?"
It is "how do we do this?"

Talk to a Forward Deployed Engineer. We will start with a free stress test of what you have in production, and walk you through the path from there.

Talk to an FDE Get a free stress test