Introducing:
MLOps Framework for Government Community Cloud (GCC) Organizations
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.
The OmniData MLOps Framework delivers a structured foundation for collaboration, governance, and rapid model deployment leveraging Microsoft Fabric, Azure ML, or Azure Databricks.
This framework is designed to align ML efforts with business priorities while reducing risk and accelerating time-to-value.
What You'll Get:
- Governance & Documentation: Clear processes, AI model cards, audit trails
- Standardized Processes: Testing, approval, and promotion for visibility
- CI/CD: Streamlining ML model deployment
- Model Monitoring: Ensuring long-term reliability
- Platform Recommendations: Choosing the best platform for your needs
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.
Key Outcomes:
Gain a clear, centralized, and secure approach to managing the ML lifecycle, from experimentation to production, within a scalable and compliant structure. Transition from siloed development to a professional-grade ML foundation.
Transform your ML operations with the OmniData MLOps Framework.
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