to full scale production
MLOps & LLMOps platform
Develop, deploy and pilot in production your Machine Learning models and LLMs in record time. Bring MLOps into your company to accelerate the development of your AI projects and make them more reliable.
Develop, deploy and pilot your Machine Learning models in production in record time, without DevOps or ML Engineer.
MLOps for AI
Develop the best models
Integrate and unify your structured and unstructured data flows. Build your Machine & Deep Learning models using any open-source framework.
Fine-tune your LLMs on your data assets. Version data, features, models and work collaboratively. Set up complete pipelines from data preparation to model training.
Deploy your models on a large scale
Execute your Pipelines and track each run. Deploy your models, make their results available and set re-training conditions. Serve model results in real time. Have working environments dedicated to experimentation, testing and production. Choose the exact size of your infrastructure and precisely manage the cost of each project.
Monitor the performance of your models
Monitor the accuracy and reliability of all your production models. Evaluate the reliability of your LLMs (loss of context, accuracy drift, hallucinations or tone alteration). Detect drifts and anomalies in models or data. Set up alerts to react as quickly as possible. Manage your infrastructure and deployments in real time. Optimize your infrastructure costs with a FinOps module.
A platform compatible with the entire ecosystem
Recommend to students the most appropriate learning content for their learning profile
Develop a trustworthy AI
44% of companies see "black box" AI as a major risk
The State of AI 2021 - MCKinsey
Head of Data Science @AddixGroup
"We particularly liked the flexibility of the platform, the ability to go into production without having to refactor the code, and the quality of the user experience."
Mouhoud El Mouhoub
President @Université Paris-Dauphine
"Craft AI offers the clearest AI solution for education that is tailored to our needs and is already a true technology success story."
CEO @EDTech France
"Trusted AI at the service of students, explainable and fair, complementing teachers is the beautiful promise of Craft AI."
CDO @Total Direct Energie
"The most remarkable thing is that thanks to Craft.AI's platform, we have developed an assistant that reduces our customers' consumption by an average of 5%!"
Generative AI: Craft AI raises €5.5 million
Paris, July 18, 2023 - Craft AI receives 5.5 million euros in funding to accelerate the development of its generative AI specialized in dataset creation. This funding, in addition to the 9 million already raised, is made up of 1.5 million euros from Bpifrance's i-Démo call for projects, and 4 million euros contributed by the investment holding company Talis.
Pandas vs Polars: Who's the fastest (library)?
MLOps, short for Operational Machine Learning, is a practice that combines DevOps principles with the development and deployment of Machine Learning models. By automating and streamlining the entire lifecycle, from data preparation to model scaling, MLOps enables organizations to effectively leverage data for real-time, large-scale ML applications. In this context, Python libraries like Pandas and Polars play a crucial role, offering powerful data processing capabilities. Comparing these libraries helps optimize MLOps workflows, ensuring efficient and successful ML deployments.
MLOps: Buy Vs. Build?
MLOps has emerged over the last few years to simplify AI projects by offering a set of practices and tools to manage all stages of development in an automated manner. Its main goal is to reduce development time, speed up and minimize deployment-related risks by improving model reliability and stability. When using an MLOps stack to develop a model, the transition from experimentation to production can be done with just a few clicks, whereas it could take up to 6 months previously.
Why MLOps is every data scientist's dream? Part 2
We will try to provide some answers to this questions in two parts. This second article focuses on the first deployment and iterations to quickly improve it while the first one focuses on conception, data collection, exploration and application prototyping.