MLOps as a Full Service
We provide, install, configure and customize MLOps for you.
WHAT WE DO
2 Challenges, 1 Solution
Current MLOps systems are designed for self-service and are too general for your precise needs.
Data research and modeling involve thousands of experiments before and during production. If not effectively managed, rapidly changing versions, variables, parameters, and models might be lost completely or extremely hard to reconstruct.
We solve both problems by customizing MLOps to your specific needs - as a service:
Control your entire architecture from one place
Reduce data scientist time wasted on infrastructure taks by 90%
Reduce devops support time by 80%
Easy access to your system
Easy move from MVP to ecosystem and production, reduce the mode to production by ~3-6 months
CoreControl is the Accelerator of AI Project Success
CoreControl is an end-to-end MLOps and model management solution that keeps the domain expert in the loop with a unified dashboard.
Have full control over different variables like the configuration of your system and the hyperparameters. Be able to go back to your working version of the code.
Data-centric solutions and fast deployment of MLOps Infrastructure by experienced CoreAI DevOps.
The model can be served in production using different methods. You can leverage all of its features like monitoring your model and data, or serving a different version of the algorithm.
Data and experiment version control with ongoing data and model monitoring optimization.
Visualize all the data that is different from your training scenario. coreControl offers various methods that detect this drift like KL divergence, KS statistical test, tree methods, etc.
Robust production models require advanced monitoring that is usually divided into data monitoring and concept drift, which will help validate better model versions success.
Neural network model optimization, making your model smaller and more efficient in inference timing.
Enter the Data-centric age via MLOPS
Automate your ML research process;
Control your entire architecture from one place;
Manage experiments, CI/CD (model serving), and data versioning;
Monitor data quality, data drifts, and production-model performance while maintaining your model and pipeline up-to-date with ongoing optimization;
Work in a collaborative environment using dashboards and online documentation, keeping the domain expert a key player.
Save Costs and Optimize Resources
Move from MVP to production 3-6 months faster, while constantly improving data and model quality;
Reduce DevOps support time by 80% and data scientists’ time spent on infrastructure by 90%;
Manage model training costs (FinOps) and receive loss prevention alerts;
Cut new employee training by 3-5 months.
End-to-end Process Visibility
Manage and reconstruct your experiments with clear and easy-to-use data versioning and preprocessing;
Keep track of the data used for each experiment and get end-to-end process visibility, including the data, models, and infrastructure across multiple environments and project phases;
Benchmark experiments, model performance and data health while monitoring ML costs across all phases of your project.
“A lot of AI projects have a POC-to-Production gap problem."
We have developed an algorithmic engine that guarantees better results within a shorter research time while reducing expenses. Also, we are building AI paradigms customized to your needs to train yourself, bringing your domain expertise.
Our mission is to bridge the gap between AI and humans by making AI accessible without the need to be a computer science expert.
We are a service from the home of CoreAI, led by a lecturer in the Technion and an AI researcher with more than 15 years of using algorithms to solve problems, in Israel Elite Intelligence forces 8200, Academia, Startups, and hi-tech Industry.