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  • ABOUT US
        • Services

          UCS is your strategy and technology consulting services partner empowering your business to co-create a shared future.

          Find out more

        • Application Development

          Application Development
        • Cloud Solutions

          Cloud Solutions
        • Data Analytics

          Data Analytics
        • Digital Transformation

          Digital Transformation
        • AI/ML Solutions

          AIML Solutions
        • Customer Experience

          Customer Experience
  • CAREERS
        • Healthcare

          healthcare
        • Travel & logistics

          travel
        • Education

          education
        • Media

          Media
        • eCommerce

          ecommerce
        • Retail

          Retail
        • Cybersecurity

          cybersecurity
        • Manufacturing

          Manufacturing
  • BLOG
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Streamlining AI Model Deployment for a Global Telecom Leader with Azure AI Studio

UCS relied on numerous AI models across various business functions (e.g., network optimization, customer churn prediction, fraud detection). However, managing the deployment and monitoring of these models was becoming increasingly complex and time-consuming due to the diverse infrastructure requirements, the need for continuous updates and maintenance, difficulties in ensuring model accuracy and performance, and challenges in integrating these models with existing systems and workflows.

Challenge

Solution

Solution

Key Takeaways

90% reduction in model deployment time

Automated pipelines drastically reduced the time required to deploy models from weeks to hours.

Improved model governance and collaboration

Centralized registry and version control streamlined collaboration between data scientists and engineers, ensuring model quality and compliance.

Enhanced model performance and reliability

Real-time monitoring and logging allowed for proactive issue identification and resolution, ensuring optimal model performance.

Increased scalability

The company could now easily scale model deployment to handle the growing volume of AI use cases.

Cost savings

The streamlined process reduced the need for manual intervention and resource allocation, resulting in significant cost savings.

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