Compare model experiments. Catch what AI misses. Decide together.
Built on scikit-learn. Designed for teams. Skore is the methodology layer between your AI coding tools and production. Track experiments, validate models, and collaborate with confidence.
Trustworthy AI. Industrialized practice. Results your business can act on. Scale & Transform.
The habits good teams try to enforce on every project. Now enforced automatically.
Your AI writes the code. Skore makes sure it holds up.
AI coding tools generate scikit-learn pipelines in seconds. Skore validates them detecting data leakage, picking the right metric per fold, and flagging silent errors before they reach production.
Every experiment linked. Every decision tracked.Nothing lost when someone leaves the team.
Skore gives your team a shared, versioned experiment registry. Every run, dataset version, and estimator choice is linked, searchable, and reviewable. No duplicate notebooks. No institutional knowledge walking out the door.
Your models. Your business. One shared language.
Skore auto-generates model cards, documentation, and visual reports structured around your domain: lift curves, business-defined thresholds, KPIs that matter to the team consuming the output. Data scientists spend less time justifying their work. Business stakeholders get results they can act on
See what your workflow looks like with Skore.
From experiment to decision, in a few clicks.
Write code
In your favorite notebook/IDE
Track metrics
One line of code
Compare & decide
Together with your team
Enterprise data science. Market trends.
Four shifts are redefining how enterprise data science gets done. Skore is built around all four.
Agentic is the new norm in data science
LLM assistants and agents are moving into DS workflows and generating scikit-learn by default. Downloads doubled from 100M to 200M monthly in nine months. Traditional ML became the execution layer for AI.
No shared standards for how data scientists work.
Teams buy rigid platforms, stitch their own stack together, or build internal tooling that becomes debt. The bottleneck isn't compute, it's the absence of shared standards.
Projects stall in translation.
Data scientists speak in statistics. Business teams speak in outcomes. Models wait on validation that gets lost between the two.
Agentic AI amplifies the trust problem
Trust in a model depends on reproducibility, explainability, and peer review. AI-generated code is fast. It's also easy to ship without any of that.
The question isn't when to start. It's how.
Fits your stack.
Respects your data.
Skore integrates with your existing tools and runs in your environment, cloud or on-premise. Your data never leaves your infrastructure.
Transparency
Understand how models work so you can trust and improve them.
Composabilité
Modular tools that fit your stack. No lock-in, ever.
Réutilisabilité
Past experiments become building blocks. Nothing is lost.
Science first
Start with the problem, not the tool. Methodology guides everything.
Compare model experimentations. Trust AI. Decide together.
Track your first experiment in 5 minutes. No sign-up required, no vendor lock-in. Open source, built on scikit-learn.
