PRODUCT

Enterprise-wide Democratization of AI/ML

Democratization of AI/ML should enable AI citizens to fully utilize the AI/ML Tools/Technologies and to get access to the required data and other assets with minimal or no process friction

AI/ML Tool & Technologies supported

categories created by well-curated request & approval workflows to provision resources

times faster development with foster collaboration

“With frictionless interface and collaboration capabilities provided to our data analysts, we see an increase in response time within our team by 30%.”

Objective

The Data Science leadership of a leading pharma company was in need of a variety of Data Science capabilities across departments.
Industry tools did not provide the required capabilities to take models to production and operationalize them.
There was a need for a streamlined approval process without which getting access to data and AI tools were time-consuming.

Introduction

D Cube Analytics surveyed the needs of several departments having 600+ Data Scientists across departments and performed market research and conducted workshop sessions involving relevant stakeholders, domain experts and analysts to brainstorm multiple use cases to capture & identify functional customizations.

Solution

The platform integrated a governance process to enable frictionless provisioning of the data and other assets. This provided leadership and information governors visibility to the usage and cost of the data science resources across enterprises.

The platform allows departmental heads to make different kinds of assets available for its users.

Multiple workflow-based approvals were leveraged such as project access, dataset access, vendor data access and custom cluster provisioning.

The platform provided easy and intuitive tool provisioning to the data scientists along with collaboration capabilities using an organized project structure.

Here are some key features included within the project-based structure:
– Ability to publish/share reusable components and cite a project based on the level of collaboration
– Provision to restrict access at project level/ department level/ Enterprise level
– Provision to tag the cost center for each project
– Provision to select the server resources based on project needs (Predefined or
Custom)
– Provision to add datasets for accessing centralized data feeds on S3 Buckets
– Provision to deploy developed apps for internal/external consumption
– Provision to scale server environment dynamically
– Provision to allow developed apps to be published to various stakeholders

Outcome

  • D Cube Analytics delivered a high impact by creating a Unified Data Science platform on cloud
  • The platform offered a very contextualized experience including integration to client cost centers
  • Considerable license cost savings were realized with users moving into cloud-based computes through the unified solution

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