Datasense Cloud Platform

Feature Engineering

We will help to explore the data and depending on what/which model will be appropriate to solve the business problem, we can set data pipelines to extract features that a machine learning model can work on. This is the most crucial phase for prepping the data. We leverage variety of tools to parse textual, image and extract data from different databases and CRM/Sales systems. Learn more how we do feature engineering.

Develop a Model

Model exploration involves find the right model(s) to learn and predict from the prepped data. During this phase, intuition based and data driven analysis are performed to find the best fitting model(s) for the job. Metrics to test the accuracy of the prediction which, also involves building special mathematical functions are accomplished during this phase. We have a lot of expertise in machine learning application for geospatial , security, healthcare, transportation, sales and marketing domain. See some our modeling use cases.

Integration and Deployment using Cloud Technologies

AWS , Azure and Google have taken big initiatives to build and deploy machine learning applications. We have a strong background in using these tools to deploy, support and fine-tune machine learning micro-services that can scale horizontally and vertically to support our customer’s needs. In this phase of development, we can charter and implement a deployment plan that is highly automated using container based technologies.