Data to Dashboard, simplified
Braintoy works for everyone...
Agility - speed development of use cases requiring an ML model by 81%
Reusability - reuse models and algorithms with ease without recreating from scratch
Safe & ethical AI - automated documentation and governance
Support & focus - incorporate feedback and improvement in a central model, revert to previous models or swap current models with new or existing ones.
Opportunity cost - rapidly tune and propagate model changes to strengthen the level of trust that users have in existing models.
Visibility - understanding how frequent a model is used and which applications and processes depend on them enable portfolio managers to confirm that model changes do not incur unintended consequences.
Adoption - it's easy for users to adopt built models since they could easily be integrated into existing applications and processes.
Standardization - with a common ML platform for a team or organization, it is easier to bring disparate ML assets together, govern all models from a central location and serve models to applications from one ML abstraction layer
Usability - simpler user interface (wizard-based) reduces complexity in solving complex machine learning problems.
Collaboration - a visible repository of enterprise modeling assets will foster collaboration and improve how AI capabilities developed are shared in an organization. Collaborative design and development lead to better AI products for an organization.
Extensibility - open source ML and AI algorithms and libraries are available for model builders across the enterprise (e.g. TensorFlow, H20, ScikitLearn and more)
Automation - rapidly develop and scale model to production - no need to develop API’s and micro-services; no need to write documentation for the model development process, easily track different versions of models.