Ship AI generated models that you can actually trust.

Built by the founders of scikit-learn, Skore is the pre-MLOps platform that helps data science teams to follow good practices, ensure reproducibility, and chose the best model for production.

Sign up for Skore

Sound familiar?

You're not the only data team dealing with this.

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AI-generated code, zero validation

Your AI coding assistant writes sklearn pipelines in seconds. But who checks for data leakage, wrong metrics, or silent overfitting?

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"Can you explain this to the business?"

Great F1 score. Now explain what it means to someone who doesn't speak Python. Model cards, reports, compliance docs ; all manual, all painful.

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When someone leaves, the knowledge leaves

No shared experiment library. No documentation. When your senior DS leaves, 18 months of context walks out the door.

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Duplicate notebooks everywhere

"model_v3_final_FINAL_v2.ipynb"  
Everyone runs their own version, nobody knows which model actually made it to production. It's impossible to replay experiments.

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Vendor lock-in disguised as convenience

Cloud-only MLOps tools look great until you try to leave. Your models, your data, your experiments, but trapped behind a proprietary API.

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2 months to onboard a new team member

Your new hire needs to reverse-engineer Jupyter notebooks, Slack threads, and tribal knowledge just to understand the pipeline. There has to be a better way.

This is some text inside of a div block.

Duplicate notebooks everywhere

"model_v3_final_FINAL_v2.ipynb"  
Everyone runs their own version, nobody knows which model actually made it to production

This is some text inside of a div block.

AI-generated code, zero validation

Your AI coding assistant writes sklearn pipelines in seconds. But who checks for data leakage, wrong metrics, or silent overfitting?

This is some text inside of a div block.

"Can you explain this to the CEO?"

Great F1 score. Now explain what it means to someone who doesn't speak Python. Model cards, reports, compliance docs ; all manual, all painful.

This is some text inside of a div block.

When someone leaves, the knowledge leaves

No shared experiment library. No documentation. When your senior DS leaves, 18 months of context walks out the door.

This is some text inside of a div block.

Vendor lock-in disguised as convenience

Cloud-only MLOps tools look great until you try to leave. Your models, your data, your experiments, but trapped behind a proprietary API.

This is some text inside of a div block.

3 weeks to onboard a new team member

Your new hire needs to reverse-engineer Jupyter notebooks, Slack threads, and tribal knowledge just to understand the pipeline. There has to be a better way.

Meet Skore.

The data science platform that brings structure, collaboration, and trust to your ML workflow, without leaving your notebook.

pip install skore

Start locally

Your personal ML assistant inside the notebook. Validates pipelines, selects the right metrics, detects data leakage, and suggests best practices, powered by scikit-learn's own methodology.
Automated cross-validation reports
Data leakage detection
Smart metric selection
Easy model comparison
Works with any scikit-learn compatible estimator
Free forever
Works offline
No account needed
Example 
import skore, skrub
from sklearn.linear_model import Ridge
model = skrub.tabular_pipeline(Ridge())
report = skore.evaluate(model, df, y, splitter=0.2)
report
import skore, skrub
from sklearn.linear_model import Ridge
model = skrub.tabular_pipeline(Ridge())
report = skore.evaluate(model, df, y, splitter=5)
report
import skore, skrub
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
models = [
    skrub.tabular_pipeline(Ridge()),
    skrub.tabular_pipeline(RandomForestRegressor()),
]
comparison_report = skore.evaluate(models, df, y, splitter=5)
comparison_report
Try Skore for Free
skore.probabl.ai

Share it remotely

Your team's shared experiment library in the cloud. Store, compare, and annotate models together, one single source of truth that survives team changes and project pivots.
Shared experiment library (browse, compare, pick)
Auto-generated model cards & documentation
Visual reports for non-technical stakeholders
Team activity feed & comments
Governance & compliance ready (EU AI Act)
Free for 1 user
Team plan starts at $1,750/mo

Built for the way you actually work.

AI Agents

Your AI writes the code.
Skore makes sure it works.

AI coding tools generate scikit-learn pipelines in seconds. Skore validates them ; detecting data leaks, selecting the right metrics, and flagging silent errors before they reach production.
Automatic pipeline validation (structure, leakage, overfitting)
Smart metric recommendations based on your use case
Works with any genAI agents
Team

Compare, share, decide, as a team.

The platform Skore gives your team a place to share experiments. Browse, compare, and pick the best model together, instead of working in silos with duplicate notebooks. Nothing is lost when someone leaves.
Shared experiment storage with version history
Side-by-side model comparison with visual diff
Comments, approvals, and activity feed
Data × Business

Make your work speak business.

Skore auto-generates model cards, documentation, and visual reports tailored to your domain, in terms your stakeholders actually understand. Less time justifying, more time building.
Auto-generated model cards (purpose, bias risk, audit status)
Export to PDF, share with non-technical stakeholders
EU AI Act compliance-ready documentation

90 seconds. Full workflow.

From pip install to team comparison, watch Skore in action.

Try Skore for Free
Compatible with :
MLflow
DVC
Jupyter
VS Code
Docker
Kubernetes
AWS
GCP
Azure
On-premise
MLflow
DVC
Jupyter
VS Code
Docker
Kubernetes
AWS
GCP
Azure
On-premise

They are talking about us

I use skore in all my ML projects. It saves me a lot of time and improves code clarity by generating a detailed model report (metrics and plots) with a single line of code.
Marie-Ange Rasendra - Data Scientist at HEVA
Skore not only streamlined my workflow but also ensured I adhered to best practices in data treatment and model evaluation.
Daniel Perez, PhD. - Team Lead at Qantev
Quickly evaluate, inspect, and benchmark models with ease.
Martin Khristi - Automation & AI Consultant