to full scale production
The MLOps Platform
Develop, deploy and pilot in production your Machine Learning models in record time, without DevOps or ML Engineer. Bring MLOps into your company to accelerate and make your AI projects more reliable.
Develop, deploy and pilot your Machine Learning models in production in record time, without DevOps or ML Engineer.
MLOps for AI
Deploy your models on a large scale
Execute your Pipelines and track each run. Deploy your models, make their results available and set up re-training conditions. Have dedicated working environments for experimentation, testing and production. Choose the exact size of your infrastructure and manage the cost of each project.
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
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%!"
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.
Why MLOps is every data scientist's dream? Part 1
We will try to provide some answers to this questions in two parts. This first article focuses on conception, data collection, exploration and application prototyping while the second one focuses on the first deployment of the solution and iterations to quickly improve it.
Improve your ML workflows with synthetic data
As a data scientist, you know that high-performance machine learning models cannot exist without a large amount of high-quality data to train and test them. Most of the time, building an appropriate machine learning model is not a problem: there are plenty of architectures available, and since it is part of your job, you know exactly which one will best suit your use case. However, having a large amount of high-quality data can be much more challenging: you need a labeled and cleaned dataset matching exactly your use case. Unfortunately, such a dataset is usually not already available. Maybe you only have a few data matching your requirements, maybe you have data but they are not matching exactly what you want (they can have biases or unbalanced classes for example), or maybe a dataset exists but you cannot access it because it contains private information. Therefore, you need to collect new data, label them and clean them, which can be a time-consuming and costly process, or even not be possible at all.
How will MLOps streamline your AI projects?
When speaking of Artificial Intelligence, the efficiency and profitability of projects depend on the ability of companies to deploy reliable applications quickly and at low cost. To succeed, you need to organize and improve the processes for creating, implementing, and maintaining AI models with a diverse and sizable team.