Coretex is an MLOps platform for managing the complete lifecycle of your experiments and complex workflows. The platform supports data scientists, ML engineers and less experienced users in running their data processing workflows such as building AI models, statistical data analysis and various types of computational simulations. Coretex makes processing data on a large scale simple using battle-tested workflow and task templates, while keeping track of the resulting data models and task parameters used, so reproducibility is guaranteed. Users can create their own machine learning models quickly, with less friction and more stability.
Apart from training ML models, Coretex can be used for optimizing execution of any computational pipeline, including large scale statistical analysis and various simulations (physics, molecular dynamics, population dynamics, econometrics and many more).
The platform provides dataset management and annotation tools, task tracking and result analysis, so the user never loses track of task configurations and parameters between runs. It also makes it incredibly simple to set up the IT infrastructure, whether you connect self-managed computers to the platform or you use paid, dynamically scalable cloud computers to run tasks.