DXC Data Science Enablement, MLOps, LLMOps & XOps

DXC enables enterprises to operationalize AI end to end by establishing a secure, scalable, and governed Data Science Enablement, MLOps, and LLMOps operating model. The offering is designed to move from fragmented experimentation across data science, machine learning, and generative AI to production‑grade, enterprise‑ready AI platforms operated at scale.

Built to support and accelerate adoption of Microsoft Azure, DXC helps organizations quickly get started with Azure AI services or extend existing Azure environments with standardized operating models, tooling, and governance for enterprise AI delivery.

DXC supports cloud‑native, on‑premises, and hybrid deployments, including out‑of‑the‑box Quickstarts for Microsoft Azure and Amazon Web Services, Kubernetes‑based on‑premises setups, as well as Databricks‑native and Snowflake‑native implementations. This allows enterprises to take advantage of the strengths of each platform while maintaining a unified way of working across data science, ML, and GenAI that is aligned with enterprise architecture, security, and operational requirements—including seamless integration with Azure-native services and ecosystems.

Integrated AI Enablement Workshop – From Strategy to Execution DXC engagements typically start with an integrated advisory workshop covering Data Science Enablement, MLOps, and LLMOps. The workshop is structured into three aligned sessions and provides a shared foundation for business, technology, governance, and delivery decisions, forming a practical foundation for execution—with clear alignment to Azure adoption and optimization paths where relevant. AS‑IS – Current State & Pain Points

• Existing tools, platforms, and workflows
• Organizational setup and collaboration patterns
• Pain points, inefficiencies, and operational risks
• Maturity assessment across Data Science, ML, and GenAI practices, including current Azure usage (if applicable)

TO‑BE – Target State & Best Practices

• Recommended ways of working across data science, ML, and GenAI
• Target reference architecture covering experimentation, training, deployment, and operations, aligned to Azure-native and hybrid patterns
• Governance, security, compliance, and traceability requirements
• FinOps policies, roles, and cost allocation model across cloud platforms including Azure
• Alignment to platform‑native and hybrid deployment options, with specific guidance for Azure adoption or expansion

WRAP‑UP – Alignment & Roadmap

• Validation of key roles, processes, and architectural decisions
• Review of agreed‑upon reference architecture
• A phased implementation roadmap with clear next steps, including Azure onboarding or optimization milestones

Data Science Enablement – Establishing a Unified Way of Working DXC Data Science Enablement focuses on creating a consistent, enterprise‑wide way of working for data science before scaling AI into production. It provides the organizational, process, and technical foundations required for sustainable AI adoption—aligned with Azure data, analytics, and AI services where applicable. Key outcomes include:

• Clearly defined roles and responsibilities across Data Scientists, ML engineers, platform, and IT teams
• Standardized workflows for experimentation, collaboration, review, and handover
• Governed data science environments with experiment tracking, lineage, and reproducibility
• Governance of notebooks, code, data assets, and feature development
• Integration with enterprise DevOps and data platform ecosystems, including Azure DevOps and Azure data services

This foundation helps data science scale beyond isolated projects, prepares teams for industrialized AI delivery, and establishes a consistent model for leveraging Azure at scale.

MLOps – Industrializing Machine Learning DXC MLOps builds on Data Science Enablement to industrialize the full machine learning lifecycle. It enables organizations to reliably move models into production and operate them with consistency, transparency, and control—while leveraging Azure machine learning and cloud-native capabilities where appropriate.

• Model training, evaluation, registration, and version management
• Automated CI/CD pipelines for models, data, and features
• Controlled deployment patterns for batch and real-time inference
• Production monitoring for performance, drift, reliability, and availability
• Lifecycle governance from experimentation through production and retirement

Business Outcomes

• Establish a unified, enterprise‑wide way of working for data science, ML, and GenAI
• Reduce fragmentation across teams, tools, and platforms
• Accelerate AI adoption using proven architectures and Quickstarts, including faster onboarding to Microsoft Azure
• Enable organizations to maximize and extend existing Azure investments with standardized operating models
• Introduce FinOps controls for cost allocation, budgeting, anomaly detection, reporting, and continuous optimization across AI platforms, including Azure