Case Study

An ML Ops Platform Powered by Databricks

A reputed global audit company builds a centralized ML ecosystem to accelerate ML adoption

A leading audit organization establishes a Shared Services Machine Learning (ML) Platform on Azure, leveraging Databricks to test, share, and onboard its various ML use cases rapidly.
Industry & Region: Audit & Accounting, USA
Technology Stack: Data Platform: Databricks, Cloud Computing Services: Azure (DevOps), Azure ML Designer, Machine Learning: MLflow, Identity & Access Management: AWS Cognito , Programming Framework: .NET Core, Database: PostgreSQL, Communication Protocol: gRPC, JavaScript Library: React
Client Overview
Our client is a global audit and assurance organization enabling enterprises to achieve new benchmarks in their business through powerful modern technologies.
Business Challenge
The audit company’s ML applications were operating in silos and were not being shared with other stakeholders in the company. This resulted in redundancies and delays in testing the feasibility of potential ML use cases, missed opportunities to reuse effective ML models and limited knowledge-sharing. The client wished to set up a collaborative environment and allow all application development stakeholders access to various ML use cases across the organization.
Solution Offered
Our experts at KANINI proposed a shared services ML Ops platform that would centralize their ML environment to support effective collaboration, swift onboarding of the ML use cases across the various applications (assets) within their portfolio for testing, and fast adoption of ML use cases.
The new ML Ops platform was built on Azure, leveraging Databricks for its robust data processing and ML capabilities along with a host of other advantages.
Databricks allowed the platform to be seamlessly integrated with Azure’s cloud-based ML services and infrastructure. It also enabled fast ingestion of data from multiple sources for smooth integration with different applications. Scaling data, preprocessing, and preparing data for the ML model was now much easier with the robust data transformation pipelines in place.
The company’s data engineers, data scientists, ML engineers, and other stakeholders could easily collaborate and build, train, and assess ML models using Databricks’ powerful computational capabilities. The users could quickly register into the ML Ops platform to onboard their ML solutions to test and easily deploy successful ML use cases. The platform allowed easy management, versioning, and tracking of ML models to accelerate ML use cases.
Value Delivered
  • A comprehensive solution to support the end-to-end ML Ops lifecycle.
  • A secure, fail-fast, and fail-cheap environment for testing the viability of ML use cases.
  • Robust, rapid, and reclaimable ML deployment for enhanced operational efficiency and data-driven decision-making.
  • A shared ML ecosystem to uncover new opportunities for innovation and transformation.
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