Data Engineering for Data Science & AI

Client

A multinational healthcare organization specializing in data collection, clinical trials, and market analysis

Challenge

The client wanted to leverage complex AI algorithms to improve their data-based product. The type of analytics they needed required the following:

Advanced data preparation and integration capabilities, on top of a set of complex business rules.
A data platform able to support a huge volume and heavy data processing
Integration of various technology components (which created a risk of an overly complex architecture).

Solution: Robust data pipelines to support the AI & Data Science team

To address the challenge, Craftware planned and performed the following:

Developed robust data pipelines that empowered data scientists and enhanced machine learning model performance. Both batch and inference pipelines were part of the scope.
Used cloud infrastructure to ensure access to the latest AI/ML components in the form of managed services, and to ensure scalability provided as an ‘out of the box’ feature.
Used Databricks technology stack to simplify the delivery process as it was flexible enough to handle even the most complex requirements while providing out-of-the-box support for the powerful AI/genAI features.

Key Outcomes

As a result, the client achieved:

Highly scalable cloud data infrastructure able to handle the most complex analytics, allowing for robust cost control.
Thanks to using Databricks as a main technology, ensured access to a wide range of the latest, well-validated AI/ML components and integrations, allowing the client to address even the most complex ML and GenAI requirements.
Leveraging API-centric technologies and implementing DevOps practices resulted in an increased level of automation and eliminated error-prone deployments.

Technology Stack