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Enabling a Smarter, Scalable Travel Management Platform

Impact Delivered

  • 90% Increase in operational efficiency
  • 70% Improvement in decision-making accuracy
  • 85% Higher customer objective fulfillment
  • The Customer

    The Customer

    The customer is an online corporate travel management company and is a leading provider of management solutions for administrative travel via their desktop and mobile applications. Their platform is well known for the use of artificial intelligence and machine learning to optimize search results, inventory, and the support experience.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg


    Some of the key challenges encountered include:

    • Enhanced Cloud and On-Premises Services:
      Customer required improved cloud and on-premises services to meet growing business demands.
    • Intelligent Model Management:
      Develop and deploy machine learning models, including effective versioning, monitoring, retraining, and integration into existing workflows.
    • Advanced Analytics Capabilities:
      Provide tools, techniques, and expertise for harnessing data and deriving actionable insights.
    • Streamlined Onboarding and Development:
      Facilitate seamless integration of new developers and data scientists through comprehensive API services and deployment guides for consistent and efficient development practices.
  • The Solution

    The Solution

    • Talent on demand approach: Deployed a team comprising a Cloud Data Solutions Architect, Senior Data Scientist, Senior Data Engineer, and a Senior Tableau Developer to meet customer objectives.
    • Standardized framework: Provided code coverage and unit testing for robust core functionality.
    • Comprehensive catalog of API services: Enabled easy management and access to models and rule-based paradigms.
    • Manual and user guide: Pioneered a deployment guide for Windows and Unix/Linux environments, facilitating onboarding of new developers and data scientists.
    • Optimizing AWS strategy: Assisted in adopting Amazon SageMaker for developing and deploying machine learning models.
    • Streamlined process: Implemented contemporary DevOps principles for efficient model development and deployment.
  • Solution Component

    Solution Component

    Amazon SageMaker, Tableau (data visualization tool), Code coverage and unit testing tools, API management platforms, Cloud platforms, DevOps tools and practices, Windows and Unix/Linux environments

  • The Results

    The Results

    • Enhanced cloud and on-premises services for businesses
    • Improved decision-making through valuable insights gained from advanced analytics
    • Strategic planning and expertise to fulfill customer objectives
    • Standardized framework with code coverage and unit testing for robust core functionality
    • Comprehensive catalog of API services for models and rule-based paradigms
    • Pioneering manual and user guide for deploying services in various environments
    • Adoption of Amazon SageMaker for developing and deploying machine learning models
    • Leveraging Managed Services and spot VM instances for seamless model development and deployment at scale
    • Streamlined and efficient process through contemporary DevOps principles
    • Tailored solutions to meet specific needs and drive organizations forward in the data-driven era

Impact Delivered

  • 90% Increase in operational efficiency
  • 70% Improvement in decision-making accuracy
  • 85% Higher customer objective fulfillment

The Customer

The customer is an online corporate travel management company and is a leading provider of management solutions for administrative travel via their desktop and mobile applications. Their platform is well known for the use of artificial intelligence and machine learning to optimize search results, inventory, and the support experience.

The Challenge

3D-Priniting-Challenges-Img.jpg


Some of the key challenges encountered include:

  • Enhanced Cloud and On-Premises Services:
    Customer required improved cloud and on-premises services to meet growing business demands.
  • Intelligent Model Management:
    Develop and deploy machine learning models, including effective versioning, monitoring, retraining, and integration into existing workflows.
  • Advanced Analytics Capabilities:
    Provide tools, techniques, and expertise for harnessing data and deriving actionable insights.
  • Streamlined Onboarding and Development:
    Facilitate seamless integration of new developers and data scientists through comprehensive API services and deployment guides for consistent and efficient development practices.

The Solution

  • Talent on demand approach: Deployed a team comprising a Cloud Data Solutions Architect, Senior Data Scientist, Senior Data Engineer, and a Senior Tableau Developer to meet customer objectives.
  • Standardized framework: Provided code coverage and unit testing for robust core functionality.
  • Comprehensive catalog of API services: Enabled easy management and access to models and rule-based paradigms.
  • Manual and user guide: Pioneered a deployment guide for Windows and Unix/Linux environments, facilitating onboarding of new developers and data scientists.
  • Optimizing AWS strategy: Assisted in adopting Amazon SageMaker for developing and deploying machine learning models.
  • Streamlined process: Implemented contemporary DevOps principles for efficient model development and deployment.

Solution Component

Amazon SageMaker, Tableau (data visualization tool), Code coverage and unit testing tools, API management platforms, Cloud platforms, DevOps tools and practices, Windows and Unix/Linux environments

The Results

  • Enhanced cloud and on-premises services for businesses
  • Improved decision-making through valuable insights gained from advanced analytics
  • Strategic planning and expertise to fulfill customer objectives
  • Standardized framework with code coverage and unit testing for robust core functionality
  • Comprehensive catalog of API services for models and rule-based paradigms
  • Pioneering manual and user guide for deploying services in various environments
  • Adoption of Amazon SageMaker for developing and deploying machine learning models
  • Leveraging Managed Services and spot VM instances for seamless model development and deployment at scale
  • Streamlined and efficient process through contemporary DevOps principles
  • Tailored solutions to meet specific needs and drive organizations forward in the data-driven era

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