CASE STUDY
AI for Scalable Big Data Services Across Multi-User Platforms
Developed AI-based data pipelines to analyze and classify complex data, improving delivery speed and scaling projects 6x.
Problem Solved
The projects are designed to classify and give meaning to specific data types, the details of which are protected by an NDA. Primarily, they focus on data analysis, manipulation, and extraction.
Currently, twelve projects are being actively improved and maintained, serving different teams across the organization. While some teams use the results as direct dependencies within their own projects, others rely on them through already deployed services.
Problem Solving Approach
The Conditional Random Fields AI model is employed, undergoing periodic training and testing on the data. Domain experts are continually involved in tackling the variability in languages and scripts (e.g., Hindi, Chinese, Arabic).
The AI model is used by the project, which is primarily written in Java. Other projects, such as those related to data extraction and manipulation, are mostly written in Scala and utilize Spark to effectively manage millions of complex data points.
Outcome
The approach massively improved delivery times for requested features while also streamlining existing pipelines and enabling the onboarding of new projects. In addition, ongoing quality support is continuously provided for teams that depend on the services.
As the project expanded, additional responsibilities and greater autonomy were acquired. As a result, completely new pipelines were constructed and new services were deployed within the ecosystem. Consequently, efficiency and precision were significantly enhanced, leading to growth from 2 to 12 projects in less than four years.
Massively improved delivery times for requested features
Provided ongoing quality support for dependent teams
Constructed new pipelines and deployed new services within the ecosystem
Enhanced efficiency and precision across the board
Key Features Implemented
Developed a comprehensive testing framework covering all parts of the code base, from unit tests to integration tests
Deployed a standalone application that helped increase accuracy and reduce bugs when manipulating data
Technologies
Development
Project Timeline and Team Structure
The project has been ongoing since 2020, and currently involves five team members, Software Engineers and Data Analysts, delivering Data Science & Analytics and Software Development services.
Software Engineers
Data Analyst
AI/ML Software Engineer
Methodology
Internal tools (under NDA) are used while operating on bi-weekly sprints, with planning every two weeks. Due to the project’s dynamic nature, many ad hoc tasks are handled outside of planned sprints, ensuring flexibility.
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