Have you experienced situations where reports and dashboards were created for specific needs and to adapt these artifacts with evolving business process requires heavy technology investment and enormous time, sounds way too familiar and feels painful?
Our agile analytics framework along with a DevOps platform provides process, methodology and tools with measurable outcomes that allows team to work collaboratively to understand the problem, iterate through solutions and deploy to production with proper governance.
The Dev-Ops platform with a set of accelerators allow data scientists and analysts to collaboratively work in a one development environment for model experimentation and deployment with access to enterprise data assets across multiple cloud or on-premise locations.
An open platform to build, experiment, validate and deploy machine learning models.
Accelerate model development and research at scale by leveraging disparate datasets
Various traditional and big data connectors are available in this module which would be utilized during model development to access data from disparate sources directly into jupyter notebook.
An ingestion module is available to transform data to and fro from various data sources to prepare data for data science activities.
The workbench is where the data scientists and model developers will write programs and conduct exploratory work on model development. Each user on the platform will be able to create project workspaces to define the assets for the models including data files, images, documents, and program code.The workbench also includes a fully functional jupyter notebook integration into the platform to write programs and seamlessly integrate data.
This module allows the model developer to deploy the trained model to a production-like environment to test real-time scenarios with production data.