Modeling Language Support
Modeling languages for optimization problems are great at succinctly describing a model without getting into low level languages like C, C++ or Java. These languages operate at a higher level of abstraction, closely resembling how you might write a model on paper using symbolic notations. Another advantage of using modeling language is portability. Most modeling languages offer a series of solvers to go with, and you can switch between solvers easily as per your need - which is much harder to do when you are using solver-specific APIs. myOptEngine uses GNU Mathprog as the modeling language - it is easy to learn and develop in. You can start with a model written in MathProg, and a free solver, and then switching to a commercial solver is a matter of clicking a button - saving you months of development time.
Billing by the Minute
Only pay for the resources, hardware and network bandwidth that you use for solving your model. This helps you save money on two fronts: costly hardware with cutting edge specs and costly (commercial) solver licenses. If you need to solve optimization problem once in a while, avoid your costly hardware lying under-utilized. State-of-the-art commercial solver licenses are costly as well, and the same reasoning applies there too.
Easy to use API
myOptCloud comes with a set of API to access all the functionality, so that you can integrate with your existing workflow easily. This is accompanied by a simple UI to manage models, schedule jobs and view results, just in case you feel more at ease that way. Deployment of optimization models are easy with either the API or the UI. In case you need help, our experienced team can help you in that process.
We provide data connectors to quite a few common data sources so that we can pull your data and form the model instance on the cloud, in order to solve it. The data connectors are configured using simple JSON properties, and explained in detail in our user manual. Once you set up your data connectors to a model instance, and then set up a job to run, say, every morning, our system will automatically pull new data every morning, solve the new problem and return result. We support pulling data from Excel spreadsheets, Google spreadsheets, various relational and NoSQL databases, and modern big-data pipelines like kafka (and their cloud-based variants).