Works for everyone...
Stay ahead of the game by embracing and integrating AI into business processes across your organization.
You create an AI Hub where the development is centralized, standardized and governed. You empower team members of all skill levels to harness AI in solving meaningful problems. You enable data science self-serve in a unified and collaborative platform.
We allow you to focus on what you like doing - build ML models.
Our platform helps you reduce the time for developing and deploying models into production from weeks to hours. APIs, microservices and documentation are generated for you automatically.
Save time waiting for software engineers to create all the above.
Model risk managers
Our platform allows transparent and people independent AI models.
You can validate models for interpretability, avoid biases, incorporate feedback and improvement, revert to a previous model, or swap current model with a new or existing one.
Have you noticed how long it takes you to develop an API? With our platform, you can create an end-to-end AI application and easily integrate models into new or legacy applications in minutes.
The models and algorithms are reusable so that you don't have to develop everything every time from scratch.
In our platform, open-source algorithms and libraries e.g. TensorFlow, H20, SciLearn and more, are available across the enterprise.
Data and business analysts
Our platform doesn’t require you to have any coding skills. You will be able to solve complex ML problems with a simple wizard-based user interface.
Let our platform complete the technical work for you. You boost your career by enjoying the perks of Auto ML and Visual ML.
Agility - speeds up the development of use cases requiring an ML model by 300%.
Reusability - models and algorithms can be reused without recreating from scratch.
Safe & ethical AI - automated documentation and governance of models helps model risk managers ensure safe and ethical AI.
Easy management - incorporate feedback and improvement in a central model, revert to previous models or swap models with new ones.
Trust - rapidly tune and propagate model changes to strengthen the level of trust users have in existing models.
Visibility - knowing how frequent a model is used and which applications and processes depend on them enables portfolio managers to confirm that changes don't have unintended consequences.
Adoption - it is easy for users to adopt built models since they can be integrated into new and legacy applications with no hustle.
Standardization - it brings disparate ML assets together, governs all models, and serve models to applications from a central location.
Usability - simple user interface and wizard-based workflow reduce the effort in solving complex analytical problems.
Collaboration - a visible repository of enterprise modeling assets fosters collaboration and improves sharing AI capabilities in an organization. Such collaboration results in better AI products.
Extensibility - open source ML and AI algorithms and libraries like TensorFlow, H20, ScikitLearn and more are available for model builders across the enterprise.
Automation - rapidly develop and scale model to production. Easily track different versions of models. No need for API/micro-services development or documenting the model development process.