Revolutionising MLOps with Amdocs: Accelerating enterprise-wide ML model deployment

Tackling complexities of scaling machine learning models

As your organisation expands its machine learning (ML) efforts, you may encounter significant challenges. Your data science teams might operate in silos, each using different tools, processes and security protocols – leading to inconsistencies and inefficiencies across departments.

ai man in cloud chair

Existing data solutions, potentially assembled over time, may not integrate smoothly with new machine learning initiatives, creating operational friction. Data inconsistencies from varied ingestion methods can hinder cross-team collaboration.

Your data scientists might find themselves overwhelmed by non-core tasks such as infrastructure management and security policy formulation, preventing them from fully applying their expertise where it's most valuable.

These issues impact productivity, potentially introduce security risks, and prevent you from realising the full potential of your data science investments.

Understanding these challenges is the first step towards addressing them effectively and enhancing your ML capabilities.

Amdocs’ MLOps Foundations: Scaling your ML efforts efficiently

Amdocs MLOps Foundations eliminates hurdles in scaling machine learning operations, offering a comprehensive solution for enterprises:

  • Seamless ML lifecycle management: Gain faster time-to-value and consistency across your organisation by removing bottlenecks in the ML development-to-production process.
  • Unified platform approach: Achieve rapid creation, deployment and oversight of data science projects and ML models at scale through a cohesive platform for MLOps that standardises and automates processes, aligning your technology landscape.
  • Ready-to-deploy blueprints: Quickly build a solid foundation for your ML initiatives using our well-established architectural blueprints.
  • Automation & streamlining: Simplify both technical and business processes, from infrastructure provisioning to securing organisational approvals, using our platform's Infrastructure as Code, automated pipelines and workflow automation.
  • Robust security & compliance: Align model usage with regulatory and risk management objectives by integrating security and compliance measures directly into the ML pipeline, including necessary guardrails and continuous monitoring of ML models in production.

The result? Increased data scientist productivity and enhanced business value through efficient deployment and monitoring of AI and ML models across your enterprise.

Benefits

  • Accelerate ML model deployment at scale

    Streamline your infrastructure and operational tasks with automation, allowing data scientists to focus on innovation. This approach enables a 30% increase in the deployment of AI/ML models into production.

  • Robust & secure MLOps platform

    Utilize a structured MLOps framework featuring pre-designed architectural templates, automated workspaces, and built-in safeguards to ensure controlled, consistent operations.

  • Eliminate barriers in the ML development process

    Facilitate rapid ML model deployment with a frictionless process that integrates DevOps principles, continuous integration/continuous deployment (CI/CD) pipelines, and automated release management.

  • Optimize costs with enhanced observability

    Gain control over your resource utilization with integrated monitoring tools and FinOps workflows designed to curb unnecessary expenses and enhance cost efficiency.

  • Comprehensive security & compliance

    Ensure your ML initiatives align with organizational risk management, regulatory compliance, and security policies through a systemic approach that incorporates identity and configuration safeguards.

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