DigiEdze AI · MLOps
Drive Reliability for the Models You Depend On
Most enterprises running Generative AI also depend on classical models for forecasting, scoring and detection. We modernize that estate onto one platform, so both kinds of model share governance, monitoring and release discipline.
- feature stores
- training pipelines
- model registry
- serving
- drift monitoring
- model risk governance
Reference architecture
- 06Feature platform
- 05Training pipelines
- 04Model registry & approval gates
- 03Serving — batch · real-time · streaming
- 02Drift & performance monitoring
- 01Model risk documentation
The Problem
What Changes When This Goes Wrong?
Classical models run on ageing bespoke pipelines that only their original author understands.
Model risk governance covers the old estate and the new one differently, or not at all.
Retraining is manual, so drift is corrected late and inconsistently.
What we build
MLOps Capability Grid
Feature platform
Consistent feature computation across training and serving, eliminating train/serve skew.
Training pipelines
Reproducible, scheduled training with data and code versioning and full lineage.
Model registry and approval
One registry covering classical and generative models, with the same promotion gates.
Serving infrastructure
Batch, real-time and streaming inference with autoscaling and cost awareness.
Drift and performance monitoring
Data drift, concept drift and performance decay detected with automated retraining triggers.
Model risk governance
Documentation, validation evidence and challenger models where regulation requires them.
How it works
Unifying the release cycle for ML-powered workloads with automated testing, agile principles, and support for ML models within CI/CD systems, with an aim solve complex real-world scenarios.
Technology
- Feature stores
- Pipeline orchestrators
- Model registries
- Serving runtimes
- Monitoring platforms
Use Cases
Our Practical Approach
BFSI
Credit and fraud models governed under model-risk requirements alongside generative systems.
Retail & CPG
Demand forecasting retrained automatically as seasonality shifts.
Manufacturing
Quality and maintenance models monitored for drift against changing process conditions.
Outcomes
What We Measure
Keep your model accurate, available, and auditable long after deployment. This is how we prove your MLOps is effective and driving what your business needs.
- Model refresh cadence
- Drift detection lead time
- Serving reliability
- Governance evidence completeness
Questions
MLOps, Answered Directly
Should classical models and LLMs share one platform?
They should share governance, registry, monitoring and release discipline. Their training and serving paths differ, but running two separate governance regimes doubles the audit burden and guarantees inconsistency.
Related services
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Modernize Your Model Platform
Reach our experts who can lay the foundation and run MLOps at full scale for your enterprise. From fragmented data systems to structured, we make your data reliable and efficient.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT