Deploy your models in production
Monitor their performance

Accelerate the deployment and production piloting of your Machine Learning models
with the MLOps Platform - Craft AI

Craft AI platform

MLOps at the service of Data Science teams

Without MLOps, the industrialization of an AI application takes 7 months on average and costs 300K€.

Deploy and redeploy your ML models & LLMs

Endpoint, API, Re-training

Scale up your models

x 1000 the size of your AIs

Drive model performance

Drift, Steering, Alerts

Manage infrastructure and deployments

Without DevOps skills

Discover the platform

Our platform under the microscope

Machine Learning Pipelines

Develop your models in a workspace compatible with all open-source libraries. Import and fine-tune your LLMs in dedicated pipelines. Identify the key steps in your code. Assemble them into complete pipelines, from data preparation to model training.

The pipeline is versionable and easily deployable. Our containerization technology ensures that it scales without code rework.

Machine Learning Pipelines -
Screenshot of the platform

Environments

Set up environments made up of databases (vector database) and compute servers (CPU & GPU) in just a few clicks. Easily configure the size and power of each component. Monitor costs for each environment in real time. Build environments dedicated to experimentation and production.

Environments are easily parameterized by Data Scientists without DevOps skills. Control your infrastructure budget with a FinOps module.

Model Serving

Deploy your Machine Learning pipelines in production in just a few clicks. Create a service to expose the pipeline via API to end-users in real time. Define execution conditions (temporal or metric-based) to automate retraining. Redeploy your pipelines using multiple methods: A/B testing, Canary, Shadowing, Failover.

Deployment is done in a few clicks, without any DevOps skills. It saves a lot of time and offers total autonomy to Data Scientists.

Deployments - Screenshot of the MLOps platform

Execution tracking

Get detailed tracking of your executions, step by step. Analyse the execution time of each step and the resources used. Easily view the results and patterns generated by your pipelines. Be alerted in case of failure during the execution of a pipeline to proceed with debugging.

Run tracking takes the guesswork out of data scientists' job of tracking runs and ensures that they are working properly.

Tracking executions - Screenshot of the MLOps platform

ML Monitoring

Monitor the performance of your production models in real time. Evaluate the reliability of your LLMs (loss of context, accuracy drift, hallucinations or tone alteration). Automatically detect when your models drift and lose accuracy. Trigger re-training to correct drifts. Manage your infrastructures and deployments by monitoring their health. Set alerts to react as quickly as possible in the event of a problem.

The production model monitoring tool provides a 360-degree view of the health of your AI applications in real time.

ML model pilots - MLOps platform screenshot

Explainable AI (XAI)

Analyse the weight of each explanatory variable in the prediction. Visualise your decision trees. Obtain a local or global explanation, agnostic of the algorithm used with Shap. Inspect and analyse the behaviour of each of the explanatory or predicted variables. Monitor the evolution of explainability even in production.

With the model explainability module, remove the "black box" operation of algorithms and keep the human in the loop.

XAI - Screenshot of the MLOps platform

A platform compatible with the entire ecosystem

aws
Azure
Google Cloud
OVH Cloud
scikit-lean
PyTorch
Tensor Flow
XGBoost
VS Code
jupyter
PC
Python
R
Rust
mongo DB

Request a demo

A platform compatible with the entire ecosystem

aws
Azure
Google Cloud
OVH Cloud
scikit-lean
PyTorch
Tensor Flow
XGBoost
jupyter
PC
Python
R
Rust
mongo DB

Get started !

Import your code

From your individual workspace, retrieve your existing code from a Git repository or start a new data science project, alone or in a team.

Create pipelines

Build your own Machine Learning pipelines by identifying a series of steps from data preparation to model training. Save and version each pipeline in the platform.

Run the pipelines

Run a pipeline directly in the workspace or on the platform for additional resources and access to various monitoring, control and explainability features.

Deploy pipelines in production

Once the pipeline is validated, you can deploy it to production very easily either by exposing it via an endpoint or by defining runtime rules. Multiple redeployment methods are also available to you.

Get started !

Import your code

From your individual workspace, retrieve your existing code from a Git repository or start a new data science project, alone or in a team.

Create pipelines

Build your own Machine Learning pipelines by identifying a series of steps from data preparation to model training. Save and version each pipeline in the platform.

Run the pipelines

Run a pipeline directly in the workspace or on the platform for additional resources and access to various monitoring, control and explainability features.

Deploy pipelines in production

Once the pipeline is validated, you can deploy it to production very easily either by exposing it via an endpoint or by defining runtime rules. Multiple redeployment methods are also available to you.

FAQ

We answer the most common questions you may have about the MLOps platform.

See all questions

What can the MLOps platform do?

The beta version of the platform allows you to set up computing and storage infrastructures and to create and deploy Machine Learning pipelines.

In which language(s) is the platform available?

The platform is available in English at first. We will progressively add a multi-language mode allowing you to set the language of your choice.

What is MLOps?

MLOps is a set of practices that aims to deploy and maintain Machine Learning models in production in a reliable and efficient manner.

How does the subscription / billing of the platform work?

You pay a subscription fee that gives you access to all the features of the MLOps platform, present and future. On top of that, you will be billed monthly based on your usage of computing and storage resources.

How to stay informed about Craft AI news?

Follow us on Linkedin & Twitter and visit our blog where we regularly publish articles on artificial intelligence.

Lead your AI to production

Test our MLOps platform for accelerating the deployment and piloting of Machine Learning models for free.

Request a demo

Craft AI platform