Optimizing ML Governance and Security: Genpact’s Path to Enhanced Model Efficiency

Business Problem

Genpact encountered several key challenges that were hindering the efficacy and reliability of its ML operations:

  • Lack of Standardized ML Governance: Without a structured approach to model development, validation, and monitoring, Genpact struggled to maintain consistency and quality across various ML initiatives. This gap affected the reliability and operational efficiency of the models deployed.
  • Insufficient Security Measures: In the face of complex data privacy regulations, Genpact needed enhanced security protocols for ML models to protect sensitive information throughout the entire ML lifecycle. Security lapses would risk compliance and data integrity.
  • Absence of Responsible AI/ML Practices: Genpact recognized a need to prevent bias in ML outcomes. However, without robust Responsible AI/ML practices in place, ensuring fairness and transparency across models proved challenging.
  • Complexity in Ongoing Model Monitoring and Maintenance: As the number of models grew, Genpact faced difficulties in regularly monitoring and improving ML models, leading to performance degradation over time.

About Genpact

Genpact, a global professional services firm, focuses on delivering digital transformation solutions to optimize business processes and drive innovation. With numerous machine learning (ML) models implemented across diverse applications, Genpact required a scalable solution to streamline ML governance, enhance model security, and ensure Responsible AI/ML practices.

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Solution

GoML enabled Genpact to streamline ML governance by implementing a structured process for model development, validation, and monitoring, ensuring consistency and high-quality performance across all ML initiatives. Additionally, GoML’s automated evaluation and retraining system helped maintain model accuracy and relevance over time, ensuring continuous alignment with Genpact’s evolving business needs

1.

Standardized ML Governance Process:
Implemented a structured, end-to-end ML development process, covering algorithm selection, data preprocessing, and model validation.Utilized AWS EC2 for high-performance computing resources and Amazon RDS for efficient data management, supporting rapid prototyping and seamless model deployment.Ensured consistent, high-quality model performance, allowing Genpact to deploy models confidently across various applications.

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Responsible AI/ML Practices for Bias Mitigation:
Integrated Responsible AI practices within the governance framework to identify and reduce bias in models.Used GPT-3.5 for model interpretability and fairness assessments, aligning models with ethical guidelines to enhance transparency and reduce bias.

2.

Ongoing Model Evaluation and Retraining:
Established a continuous evaluation and retraining process to enhance model relevancy and accuracy based on real-world data.Integrated AWS S3 for secure file storage and version control of model updates, facilitating efficient retraining cycles.Maintained model accuracy over time, adapting models to evolving business needs.

3.

Enhanced Security Protocols:
Designed a multi-layered security architecture, implementing stringent user authentication protocols for both admin and general users.Embedded security measures across all ML workflow layers, ensuring compliance with industry standards and protecting sensitive data and ML models.

Architecture

  • InfrastructureVirtual Private Cloud (VPC):
    Houses the entire solution within a secure, isolated environment.AWS EC2 Instance:Hosts the Angular UI application and backend services within the VPC.Centralizes user interactions and data processing in a secure, scalable cloud environment.
  • Data Storage and ManagementAWS S3:
    Provides scalable storage for user files, rules, and configuration data.Supports version control, ensuring data integrity and secure access.Amazon RDS:Manages relational data, including user information, file metadata, and processing configurations.Ensures efficient data retrieval and management of structured data.
  • User Roles and Access
    Authenticated Admin User:
    Access to system configuration and data management settings for setup and maintenance.
    Authenticated General User:
    Access to core data processing, validation, and analytics functionalities for day-to-day operations.
  • Admin Capabilities
    Data Configuration:
    Define Valid Columns and Mandatory Columns to enforce data consistency.
    Set Mappings and Preprocessing Rules to ensure standardized data processing.
    Resource Uploads:
    Upload Codings for classification schemes.
    Upload Templates to streamline processing workflows.
  • General User Capabilities
    File and Workspace Management:
    Manage Workspaces to organize data processing tasks.
    Upload Files for data processing through the Web UI.
    Data Processing and Validation:
    Conduct Pre-process / Cleanup to prepare data for analysis.
    Update Data as needed and Validate Data for quality control.
    Template Mapping and Analytics:
    Map Templates to process data for consistency.
    Execute Analytics for insights and reporting.
    Collaboration:
    Use Chat for support or collaboration within the platform.
  • User Interaction Flow
    Data Upload and Processing: Users upload Excel files through the Web UI, then process, validate, and analyze data based on their roles.
    Data Download: Upon completion, users can download cleaned, validated, and mapped files.
  • Frontend Interface
    Angular UI Application:
    Provides a user-friendly interface hosted on the EC2 instance.
    Enables smooth interaction for all user roles, from file uploads to data analysis.
  • Advanced Functionality with LLM Integration
    GPT-3.5 Client Integration:
    Enhances the platform with advanced natural language processing capabilities.
    Likely supports functionalities such as the chat feature or assists in data processing.
Outcomes

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92% Customer Satisfaction: Enhanced model consistency and security boosted customer satisfaction.

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88% UAT Acceptance Rate: Standardized governance improved reliability and ease of use, resulting in high acceptance in User Acceptance Testing.

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80% Increase in Project Performance: Continuous monitoring, model retraining, and Responsible AI practices significantly boosted project and operational efficiency.handle increased demand