Padma Polash Paul
Just like a food truck, I want to create an “AI truck” that contains all the utensils and recipes to create and serve intelligence on any dataset.
I want to make AI capabilities accessible to everyone, everywhere in the world
To make AI useful, it has to be simple. I want to Improve the world we live in by simplifying intelligence creation for everyday use.
Our mission is to make AI accessible to everyone, everywhere, making it easy for the world to harness the power of AI in solving meaningful problems.
Transform the world’s data into information and help organizations make the most of it.
Our primary objective is to enable organizations to increase speed in delivering services to internal and external customers. Braintoy’s AI platform enables this by provisioning an agile way to develop, tune and support models in a timely and efficient manner. We minimize disruption and promotes reuse, allowing organizations to focus on effectiveness. Its components include:
Visual ML or wizard-based platform that makes it easy for leaders and team members with limited ML and AI ability to solve any complex ML problem.
Autopilot which automatically creates machine learning models and dashboards for your datasets
Expert ML which provides advanced controls that allow data scientists, AI developers, and machine learning experts to have command over the modeling process by writing and modifying their own code
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.