PAPERWORK TRANSFORMATION THROUGH DATABRICKS + ML

CLIENT: A global music provider company.

CHALLENGE:

The Client has a data science project featuring a trained Machine Learning Model, designed to optimize their paperwork processes.

Initially, it was a point solution that has been employed for internal purposes, accompanied by a significant amount of manual input and oversight. Over time, the Client recognized the potential of scaling it to entire organization and their partners level, streamlining workflows, and enhancing collaboration.
The Client requested a straightforward and easy-to-support approach to

take their data science solution to the next level by transitioning it into a production-ready system.

SOLUTION:
- Initial Resources Setup
We implemented Azure Databricks, Azure Machine Learning Service, Azure VM, Azure Web App, Azure Blob Storage and other tools for setting up communication between Azure services.
- Building Machine Learning (ML) model in Azure Databricks
We created a new cluster, imported and ran notebook to generate predictions through data loading, and set up logistic regression with various features.
- Managing the Model in Azure ML
We added the model to Azure ML and ran notebook with varying regularization parameters. We assessed the models’ performance. Then we chose the best performing model for production.
- Building & Releasing Model in Azure DevOps
We created pipeline for building model, configured steps for deploying the model to various environments, deployed model to Staging environment and executed tests. Then we promoted the model to a Production environment.

RESULT:
- Operational Efficiency
The integration of ML model (combined with the capabilities of Databricks and Azure ML) enhances and standardizes paperwork processes across company´s global operations, reducing the need for manual intervention, speeding up processes, and providing quicker responses for clients and partners.
- Resource Optimization
The automated paperwork processes enabled by ML mitigate the need for excessive human resources dedicated to administrative tasks. Such allocation of resources is now enabling creative endeavors, strategic planning and innovation, contributing to overall business growth.
- Enhanced Accuracy
ML-driven automation ensures precision in handling intricate paperwork processes, which involve calculations, data analysis, and compliance verification. By minimizing human errors, the company maintains data integrity and enhances its reputation while improving customer experience.
- Business Agility
The solution enables the Client to make quicker and more accurate decisions.

It significantly shortens new customers’ onboarding time and simplifies paperwork for existing ones. The Client´s company stays adaptable and responsive in a dynamic market environment.