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https://store-images.s-microsoft.com/image/apps.42955.666e1e90-0a5f-401c-bd7a-dc34c047bbea.12b86155-9cb1-4239-b621-2e18616ed2e4.9a3bc0ff-d53b-49b1-ba91-d6449c406be2
US$45,000

Publisher

WinWire Technologies
Service type
Proof of concept
Solution Areas
AnalyticsApp ModernizationAI + Machine Learning
Industries
HealthAutomotive, Mobility, TransportationIndustrials & ManufacturingHospitality & TravelGovernmentRetail & Consumer Goods
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United States
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Alabama
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More...
Service offered in the following state/province of United States
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    • Azure Databricks MLOps & Model Governance Advisory

    WinWire Technologies

    Operationalize and govern your ML and GenAI models with a unified MLOps framework on Azure Databricks

    Why this Offer?

    As enterprises scale AI adoption, reliable and secure MLOps becomes essential. Most lack the structured CI/CD, lineage, and audit capabilities needed to operationalize ML responsibly. This offer helps you architect and implement a governed, scalable MLOps practice using Azure Databricks, MLflow 3.0, and Unity Catalog—with best practices across deployment, rollback, monitoring, and model governance.

    Benefits

  • Complete MLOps lifecycle support for Azure Databricks
  • Versioned, auditable ML models using Unity Catalogue. li>
  • MLflow 3.0 for experiment tracking and GenAI evaluations.
  • Azure-native CI/CD: Azure DevOps + Workflows + Key Vault.
  • Compliance-ready integration with Purview and Monitor.
  • Optional MVP to validate tooling and approach.

    • What we Deliver?

  • Maturity assessment for current MLOps capabilities
  • Target-state MLOps architecture and tooling plan
  • Unity Catalog governance playbook for models and features
  • MLflow experiment tracking and registry integration
  • CI/CD setup with secure deployment practices
  • Optional MVP for production-simulated deployment

    • Deliverables

  • MLOps Maturity Assessment Report
  • MLOps Framework & Architecture Document
  • Unity Catalog Governance Templates.
  • MLflow 3.0 Implementation Plan
  • PoC Outcome Summary (if applicable)

    • Timeline

  • Phase 1: Discovery & Assessment (Weeks 1–2)
  • Phase 2: Framework & Governance Design (Weeks 3–5)
  • Phase 3: Operationalization & Recommendations (Weeks 6–8)
  • Optional: PoC Deployment (Weeks 9–10)
  • Total Duration: 8 Weeks (10 Weeks with PoC)

    • Contact WinWire to schedule your MLOps assessment and begin building a trusted AI foundation on Azure Databricks.

    Learn more
    Azure Databricks MLOps & Model Governance Advisory
    https://store-images.s-microsoft.com/image/apps.63460.666e1e90-0a5f-401c-bd7a-dc34c047bbea.12b86155-9cb1-4239-b621-2e18616ed2e4.3ebd8f62-ee57-4dd9-8311-9014f4019735
    https://store-images.s-microsoft.com/image/apps.63460.666e1e90-0a5f-401c-bd7a-dc34c047bbea.12b86155-9cb1-4239-b621-2e18616ed2e4.3ebd8f62-ee57-4dd9-8311-9014f4019735