MLOps Framework for Government Community Cloud (GCC)

Machine learning models hold transformative potential, but the path from experimentation to production is often fragmented, opaque, and ungoverned, particularly in well-regulated or sensitive data environments like GCC (Government Community Cloud). Acclaimed sources like Gartner say at least 70% of ML models never make it to production.

OmniData (now Fresche Solutions) delivers a structured foundation for collaboration, governance, and rapid model deployment leveraging Microsoft Fabric, Azure ML, or Azure Databricks. This MLOps framework is designed to align ML efforts with business priorities while reducing risk and accelerating time-to-value.

What’s Included?

This engagement is broken into 3 phases;

Phase 1. Assessment and Roadmap The engagement begins with a collaborative discovery phase to understand your current ML environment, deployment practices, team workflows, and ML development objectives. This ensures that we tailor the framework to your needs and select the optimal platform.

Phase 1 Deliverables:

• 2-4 hours with stakeholders and data science team
• Inventory of current ML development practices (e.g. R on local machines?)
• Platform recommendation: Azure ML, Azure Databricks or Microsoft Fabric

Phase 2. MLOps Framework Design & Deployment

This phase is focused on the design and deployment of your MLOps foundation. We configure your DevOps environment, apply best practices around governance and collaboration, and lay out a clear path from model development to deployment—regardless of platform.

Phase 2 Deliverables:

Azure DevOps Setup

• Workshop to define and configure best practices
• Branching strategies, approval gates, CI/CD pipelines
• Output: Process Wiki + Configured DevOps Environment

Workspace Strategy Design (Platform-Agnostic)

• Strategy tailored for Azure Databricks, Azure ML, or Microsoft Fabric
• Focused on managing environments, access, and model lifecycle

Model Registry Implementation

• Centralized tracking of model versions, metadata, and status
• Supports traceability, auditability, and promotion pipelines

Feature Store Integration

• Reusable, production-grade features to accelerate experimentation
• Compatible with ML Flow or Azure AI Foundry-style solutions
• Enables sharing features across models and teams

Model Serving Strategy

• Define how and where models are deployed (API, Power BI, Excel, app)
• Implement best practices like Delta Parquet logging for time travel
• Assess whether endpoints are actively used and monitored
• Design your model’s journey to the consumption layer

Phase 3. Data Rules Analysis & Ingestion:

Finally, we ensure your teams can own and operate the framework. Our hands-on training introduces the MLOps mindset, demos the full lifecycle, and equips your team with templates, processes, and next steps.

Phase 3. Deliverables:

2–4 hour hands-on workshop

• Walkthrough of the full MLOps process using demo/sample data
• Includes example notebooks, pipelines, and deployment templates

Responsible AI Model Card Template

• Standardized model documentation
• Captures model purpose, data sources, risk assessments, and governance artifacts

Key Outcome: Foundational MLOps Platform By the end of this engagement, your organization will have a clear, centralized, and secure approach to managing the machine learning lifecycle, from experimentation to production, within a structure that is auditable, scalable, and aligned with modern governance standards. You’ll move from siloed development environments (e.g. models built on personal machines with little oversight) to a professional-grade ML development foundation, designed to meet business demands withing a secure and compliant environment.

• A Centralized Framework to Build, Track, and Deploy ML Models.
• Governance and Monitoring Best Practices Implemented
• Clear Documentation and a Defined Promotion Process
• A Reusable, Microsoft-Focused Structure to Scale ML Efforts Securely

Who It's For:

• Data Scientists and ML Engineers working within the Microsoft GCC environment.
• IT and DevOps professionals responsible for deploying and maintaining ML models.
• Business leaders and decision-makers looking to leverage machine learning to drive business outcomes.
• Compliance and governance officers ensuring that ML models meet regulatory standards.