Thursday, March 5 2026

Skore Is Live: Track Your Data Science

8:22

By François Méro, CEO of Probabl

Two weeks ago, I wrote about the five challenges holding back enterprise data science: technology-first thinking, spiraling costs, vendor lock-in, a lack of industrial maturity, and the fading of scientific thinking. This week, Guillaume Lemaitre laid out the principles for a new generation of data science tooling “built for data scientists, by data scientists”.

Today, we are putting those principles into practice. Skore is now publicly available.

Skore is the collaboration layer for teams. This first release is the first concrete step toward the future of enterprise data science we are building at Probabl. Not the finished vision. The foundation it starts from.

Get started now:

Sign up for Skore: skore.probabl.ai
Learn more: probabl.ai/skore
Explore the code: github.com/probabl-ai/skore
Read the docs: docs.skore.probabl.ai

What Skore Does Today

If you work with scikit-learn, Skore will feel immediately familiar. Same API philosophy. Same commitment to clarity. Scikit-learn gives you powerful building blocks for machine learning, Skore extends it by giving you the guidance and structure to use them well.

Here is what you can do right now.

Evaluate any model (even old ones) in one line of code. Feed your scikit-learn compatible estimator and your dataset to EstimatorReport. It automatically generates the metrics, feature importance, and plots that are most relevant to your use case. No boilerplate. No navigating through documentation to figure out which evaluation applies. Skore does that work for you, with efficient caching under the hood so everything runs fast.

Cross-validate with full visibility. CrossValidationReport gives you a complete estimator report for each fold of your cross-validation. Not just a score, a structured, inspectable report per fold. You see how your model behaves across your data.

Benchmark models side by side. Training several estimators? ComparisonReport lets you compare them in a structured way. No more ad hoc notebooks with copy-pasted metric tables. You get a clear, standardized comparison.

Catch methodological mistakes before they matter. Skore brings together the tools you need to spot modeling issues early. Explore associations between variables to understand how your features relate to each other, relationships that could impact your modeling, and put them in perspective with the feature importance as seen by your predictive model. Combine this with utilities designed to help flag potential pitfalls in your data splitting strategy, and you have the building blocks to catch fishy patterns before they compromise your model. These are the kinds of insights experienced data scientists develop over time.

Organize and persist your work. The Project system lets you save reports, experiments, and artifacts in a structured way. Everything is stored, locally or remotely. Nothing gets lost when you close a notebook.

Collaborate through Skore, the collaboration layer for teams. Teams can share, compare, and build upon each other's experiments. It brings visibility across a team's work, standardizes workflows without slowing anyone down and frames results for decision-making; so your next stakeholder meeting starts from structured evidence, not a scramble through notebooks.

Why This Matters

If you are a data scientist, you know the reality of your day-to-day work. You have excellent tools at your disposal: plotly and seaborn for data exploration, scikit-learn for model training and evaluation. These libraries are powerful. They are also generic by design. They accommodate a wide range of use cases without prescribing how to use them.

That is a strength but also a challenge. Your experience is the key ingredient that determines whether those building blocks are assembled correctly. You spend time navigating documentation, writing boilerplate code for common evaluations, and maintaining project structure by hand. When you are experienced, it works. When you are under pressure, or when the team has mixed levels of seniority, things slip through. Methodology gets cut short. Context gets lost. Models reach production with flaws that could have been caught earlier (if ever).

Skore is designed and envisioned to close that gap. It acts as a conductor that transforms your way of working into structured, meaningful artifacts. It reduces the time you spend on documentation navigation, eliminates code boilerplate, and guides you toward the right methodological choices, the ones you would have made if you had infinite time and attention.

Think of it this way: scikit-learn trusts you to make the right decisions. Skore helps you actually make them, consistently, across every project.

Our First Move, Not Our Last

We want to be straightforward. This is early. Skore is at the beginning of its journey. We are shipping fast, and there is much more to come.

What you see today (evaluation reports, cross-validation insights, methodological diagnostics, model comparison, and team collaboration) is the first layer. It is where we deliver immediate, tangible value to any data scientist using scikit-learn.

But our ambition goes further. In the two posts that preceded this one, we laid out a vision for enterprise data science grounded in science, composability, reusability, and transparency. Skore is the vehicle for that vision. Over the coming months, you can expect:

Deeper guidance: starting with the scientific guardrails you already see in this release, and evolving toward contextual recommendations that learn from your practice and your organization's data science work.
AI-powered augmentation: feeding the right context from your experiments into code generators and assistants, so that AI-generated code is grounded in your specific project, not generic suggestions.
Full process coverage: extending Skore upstream toward data preparation and downstream toward MLOps handoffs, always from the data scientist's perspective.
Richer collaboration: multi-audience reporting, model cards, and documentation that translates technical results into business narratives.

We are building Skore the same way scikit-learn was built: step by step, guided by real-world usage, with the community as co-pilot. This release is the result of working closely with early users and our Design Partners. Their feedback and yours shape every decision.

Who Is Skore For

Skore is for data scientists who use Python and the scikit-learn ecosystem. Whether you work alone or in a team. Whether you are building your first model or managing a portfolio of hundreds.

If you are experienced, Skore saves you time. It eliminates the repetitive evaluation code you write on every project and gives you a clean, structured record of your work.

If you are building your skills, Skore accelerates your growth. The methodological warnings and automated diagnostics encode the judgment that takes years to develop. You benefit from that expertise from day one.

If you lead a data science team, Skore gives you visibility. Through Skore, you can see how experiments progress across the team, standardize best practices without micromanaging, and present results to stakeholders in a format they can act on.

And if your company has already invested in a data science practice but struggles to scale its impact, Skore is designed precisely for you. It works with your existing stack, not against it. It plugs into your environment. It does not create vendor lock-in.