Open Data Group is an analytic deployment company. The company was started over 10 years ago and has transitioned from consulting to a product company, applying their expertise in Data Science and IT to create an analytic engine, FastScore.
Successful analytics require organizational alignment (specifically between Data Science and IT) to create coordination of systems and business problem collaboration. In addition to understanding analytics, companies are trying to leverage new technologies and modernize their analytic approach. To address some of these challenges, Open Data Group have developed FastScore.
FastScore is designed to address various analytic deployment challenges to monetize analytic outcomes including:
- Manual recoding and other complexity
- Too slow to deploy analytic models (largely as a result)
- Too many languages being used
- IT and analytic teams are not on the same journey – analytic/data science teams care about iteration and exploration while IT cares about stable systems and control.
FastScore provides a repeatable, scalable process for deploying analytic workflows. Open Data Group see the model itself as the asset and emphasize that a model needs to be language and data neutral as well as deployed using micro-services (they are a Docker container) to be a valuable, and future proofed, asset.
FastScore is an analytic deployment environment that connects a wide range of analytic design environments to a wide range of business applications. It has several elements, all within a Docker container. It also includes a model abstraction (input and output AVRO schemas, an initialization and the math action) that allows models to be ingested from a wide variety of formats (including, Python, R, C, SAS, PFA) and a stream abstraction (input and output, AVRO schema in JSON, AVRO binary or text) to consume and produce a wide range of data (from streaming to traditional databases) using a standard lightweight contract for data exchange.
The FastScore Engine is a Docker container into which customers can load models for push button deployment. Input streams are then connected to provide data to the model and output streams to push results to the required business applications or downstream environment. Multiple models can be connected into an analytic pipeline within FastScore. Models can be predictive analytic models, feature generators or any other element of an analytic decision. Everything can be accessed through a REST endpoint, with model execution being handled automatically (selecting between runners for R, Python, Java, C for instance). Within the container is the stream processor that will enforce the input and output schemas and a set of sensors that allow model performance to be monitored, tested and debugged.
Besides the core engine, additional features include:
- Model Deploy
A plugin for Jupyter that integrates the engine with the Jupyter data science tool. Allows a data scientist using Jupyter to develop models and then check that they will be able to deploy them, generate the various files etc. - Model Manage
Docker container that hooks into running instances of FastScore and provides a way to address and manage the schemas, streams and models that are deployed. Can be integrated with version control and configuration management tools. - Model Compare
New in the 1.6 release, allows models to be identified as A/B or Champion/Challenger pairs and manage the run time execution of the models. Logs this data along with the rest of the data created. - Dashboard
Shows running engines and Model Manage abstractions, changes and manages the run time bindings and abstractions, provides some charting of data including that generated by Model Compare etc. Uses the REST API so all of this could be done in another product, too.
Plus Command Line Interface and REST APIs for everything.
Because all of this is done within a Docker container, the product integrates with the Docker ecosystem for components such as systems monitoring and tuning. The Docker container, allows easy deployment to variety of cloud and on premise platforms and supports micro services orchestration.
FastScore allows an organization to create a reliable, systematic, scalable process for deploying and using all the analytic models developed by their analytic and data science teams – what might be called AnalyticOps, a “function” created to provide a centralized place to manage, monitor and manipulate enterprise analytics assets.
More information on FastScore.
