For any such platform, where the bulk of the processing is performed on cloud or on-premise servers, it is imperative that access controls and requests are bounded tightly. A REST interface is a standard requirement for such tasks. To that end, we provide a RESTful interface that can be authenticated, queried upon and initiate jobs that require machine intelligence. Our RESTful interface is exposed from an API Server that utilizes Kong which then communicates with the underlying interfaces that are written in Java or C++
While having a RESTful interface allows an easy invocation of the libraries and methods available within the platform, it is at times necessary to work within the ecosystem that MI users are familiar with. Python is growing as the de facto language for Machine Learning and numerical computations, courtesy of the excellent Sci-Kit Learn and NumPy packages. Additionally, the ecosystem allows for integration with a number of packages and tools that allow easy access to virtually any system out there. With this in mind, we have focused on making available a Python SDK package that users can leverage to develop mission-critical solutions while using a familiar ecosystem.
While the REST API and the Python SDKs are key parts of an application strategy, Symphony AyasdiAI went one step further, developing a framework for accelerating the development of intelligent applications by allowing a far larger group of “data aware” resources to design and deploy these next-generation applications.
Envision addresses the gap between data science, IT and the business. Intelligent application development is often a disjointed, iterative and plodding process. Some resources had ML experience, others, data experience, others with business experience and yet others with development and deployment experience. The challenge was getting all of those people on the same page. It was difficult at best, so much so that many organizations did the natural thing – default to the familiar – powerpoint, excel or .pdfs.
Envision changes that process by providing simple Python, pre-built UI libraries, collaboration features, and AI platform connectivity, enabling more parts of the organization to create intelligent applications.
Now business analysts that understand analytic workflows can collaborate live with business owners and have their work checked by data science.