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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
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
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Manage infrastructure and deployments
Without DevOps skills
Discover the platform
Our MLOps features
Take control of the most powerful and easy-to-use MLOps platform on the market.
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Machine Learning Pipelines
Sequence of steps including data pre-processing, model training and model exploitation.
Environments
Spaces dedicated to experimentation or production consisting of databases and calculation servers.
Model Serving
Deploy your models in production, deliver predictions to end users and set up re-training.
Execution tracking
Get all the information you need to run your pipelines and deployments.
ML Monitoring
Keep your models performing with real-time monitoring of mathematical and software metrics.
Explainable AI (XAI)
Understand the why and how of each prediction and keep the human in the loop.
Read more
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.
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.
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.
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.
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.
A platform compatible with the entire ecosystem
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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.
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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.
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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.
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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.
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
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