Automated CI/CD/CT Pipelines, Resilient AI Gateways & AI FinOps
MLOps Consulting & Production AI Infrastructure Services
Bridge the gap between data science notebooks and 99.95% production uptime. Led by Principal Architect Zeeshan Dad Khan, we engineer automated CI/CD/CT training pipelines, DVC & MLflow model registries, Polly-hardened ASP.NET Core 10 AI gateways, real-time drift monitoring, and FinOps cost governance.
We eliminate manual "throw-it-over-the-wall" model deployments by uniting data engineering, ML model lifecycle automation, resilient .NET 10 systems engineering, and cloud cost governance.
Automate the entire path from training commit to production endpoint. We architect Continuous Integration, Continuous Delivery, and Continuous Training (CI/CD/CT) workflows using GitHub Actions, Azure DevOps, multi-stage GPU Docker images, and Kubernetes (AKS/GKE) with automated evaluation gates and canary rollouts.
Measurable Business Impact: Cuts model release cycles from weeks to minutes with zero-downtime canary deployments and instant rollback.
GitHub Actions & Azure DevOps
CI/CD/CT Automation
Docker & Kubernetes
Canary Rollouts
Data & Model Versioning (DVC, MLflow & Reproducible Lineage)
Never lose track of which dataset or hyperparameter set produced a production model. We implement DVC (Data Version Control), MLflow Model Registry, reproducible dataset lineage, and automated ETL schema validation so every training run is 100% auditable and reproducible.
Measurable Business Impact: Complete regulatory auditability and 100% deterministic model reproduction across dev, staging, and production.
DVC & MLflow Registry
Dataset Lineage
Automated ETL Validation
Experiment Tracking
High-Throughput Model Serving & Edge Packaging
Deploy models wherever your latency and privacy SLAs demand: managed cloud endpoints on Google Vertex AI and Azure Machine Learning, dedicated PyTorch MPS / CUDA worker queues, or automated INT8/FP16 quantization into ONNX Runtime and Apple CoreML packages for .NET MAUI mobile/desktop apps.
Measurable Business Impact: 3x–5x higher inference throughput with unified cloud and offline edge release pipelines.
Google Vertex AI & Azure ML
ONNX & CoreML Quantization
PyTorch Worker Queues
Multi-Target Serving
Real-Time Model Drift, Data Anomaly & Hallucination Monitoring
Models silently degrade when real-world data shifts. We engineer continuous observability pipelines tracking covariate data drift (PSI/KS statistics), prediction confidence decay, schema anomalies, and live LLM hallucination/grounding scores with automated retraining triggers.
Measurable Business Impact: Catches silent accuracy degradation and schema anomalies in real time before business KPIs are impacted.
Real-Time Drift Detection
Data Anomaly Alerts
Hallucination Evaluators
OpenTelemetry Metrics
Resilient AI Gateway Engineering (Polly, Failover & Cloud Pub/Sub)
Harden your AI infrastructure against cloud outages and HTTP 429 rate-limit storms. We engineer ASP.NET Core 10 AI gateways armed with .NET Polly retry, jittered backoff, and circuit-breaker policies, automatic cross-provider model fallback, and asynchronous Google Cloud Pub/Sub event orchestration.
Measurable Business Impact: 99.95% production availability and zero dropped asynchronous AI jobs during upstream provider outages.
Polly Circuit Breakers
Automatic Model Fallback
Google Cloud Pub/Sub
Zero-Key GCP ADC
FinOps for AI: Token Cost Alerts, GPU Optimization & Caching
Stop runaway cloud AI bills. We implement per-tenant and per-feature token consumption alerts, GPU memory and batch-size utilization optimization, Redis semantic prompt caching, and tiered model routing that directs simple tasks to low-cost Flash or Edge models.
Measurable Business Impact: Reduces monthly LLM token and cloud GPU infrastructure expenditure by 40% to 65%.
Token Budget Alerts
GPU Utilization Tuning
Semantic Caching Layers
Tiered Model Routing
Production MLOps in Action
How We Engineer MLOps Under the Hood: Real Production Proof
Our MLOps patterns aren't theoretical diagrams—they power our own high-traffic AI platforms and enterprise client workloads every day.
VisionFlow AI (10,000+ Users): Zero-Key Vertex AI ADC, Polly & Cloud Pub/Sub
The Architectural Challenge
Operating a global 2K generative AI studio for 10,000+ users requires zero credential exposure in mobile binaries, strict per-user quota governance, and resilience against regional Vertex AI rate limits.
Our Engineering Solution
For VisionFlow AI, we engineered an ASP.NET Core 10 BFF gateway authenticating via keyless Google Cloud Application Default Credentials (ADC), protected by Polly resilience pipelines, Anchor & Delta context compression, and Google Cloud Pub/Sub asynchronous billing and job orchestration.
Measurable Outcome
10,000+ users served at 4.8★ rating, zero leaked API keys, and 99.95% generation reliability under burst traffic.
Deploying deep PyTorch restoration models without proper queue isolation causes GPU out-of-memory (OOM) crashes, zombie processes, and opaque black-box wait times for users.
Our Engineering Solution
In LuminaEdit, we containerized PyTorch MPS / CUDA inference workers behind a bounded queue with automated warm-model preloading, health probes, and real-time ASP.NET Core SignalR telemetry streaming GPU progress directly to client UIs.
Measurable Outcome
Zero GPU OOM crashes under concurrent load and 3.4x higher GPU worker utilization.
Running routine pre-processing—like face detection, 2x2 biometric cropping, DPI validation, or Whisper speech transcription—on cloud GPUs destroys unit economics at scale.
Our Engineering Solution
Our MLOps pipelines automatically export and quantize models to ONNX Runtime and Apple CoreML while offloading deterministic vision checks to client-side runtimes (proven live at visionflowai.app/tools).
Measurable Outcome
Up to 65% FinOps reduction in cloud compute bills and instant zero-latency local execution.
2-Week Onion Architecture Refactoring for Unmanageable ML Codebases
The Architectural Challenge
Data science teams often ship monolithic Python notebooks with hardcoded file paths, unversioned model weights, and zero retry logic that fail immediately in enterprise production.
Our Engineering Solution
Using our 2-Week Onion Architecture rescue methodology, we decouple domain logic from model execution, wrap inference endpoints in Polly-resilient C# / .NET 10 gateways, and wire DVC, MLflow, and GitHub Actions/Azure DevOps CI/CD pipelines.
Measurable Outcome
Transforms fragile notebook prototypes into containerized, monitored production infrastructure in 10 business days.
MLOps Maturity Comparison & Delivery Blueprint
Ad-Hoc Notebook Deployments vs. Cloud-Locked MLOps vs. KCC Hybrid MLOps
Compare how our vendor-agnostic .NET 10 + PyTorch/ONNX MLOps architecture outperforms manual deployments and single-cloud lock-in.
Enterprise MLOps Maturity Evaluation Matrix (Manual Notebook Deployment vs. Single-Vendor Cloud MLOps vs. KCC Hybrid MLOps & Resilient AI Infrastructure)
Infrastructure Dimension
Ad-Hoc Notebook / Script Deployment
Single-Cloud Proprietary MLOps
KCC Hybrid MLOps & Resilient AI Gateway
1. Deployment & Retraining Speed
Manual SSH/FTP weight uploads; weeks of fragile handoffs.
Automated in one cloud, but cannot package mobile/edge models.
We audit your current training notebooks, dataset storage, inference latency bottlenecks, and monthly GPU/token bills to design a target MLOps and FinOps architecture blueprint.
We implement reproducible data versioning with DVC, centralize model artifacts in MLflow, and build GitHub Actions or Azure DevOps pipelines for automated training, testing, and Docker/ONNX packaging.
We deploy your models to Google Vertex AI, Azure ML, or Kubernetes GPU workers behind a zero-key ASP.NET Core 10 gateway with Polly circuit breakers, Google Cloud Pub/Sub queues, and Redis semantic caching.
We configure OpenTelemetry dashboards for data/concept drift, hallucination scoring, and per-tenant token budget alerts, complete with runbooks and team training.
Enterprise MLOps Technology Stack
CI/CD/CT Automation, Model Registries & Resilient Serving Stack
We integrate industry-standard open-source MLOps tooling with enterprise cloud platforms and high-availability .NET 10 gateway infrastructure.
End-to-end Continuous Integration, Continuous Delivery, and Continuous Training pipelines with DVC dataset lineage, MLflow experiment/model registries, multi-stage CUDA Docker builds, and Kubernetes (AKS/GKE) orchestration.
GitHub Actions & Azure DevOps
DVC & MLflow
Docker & Kubernetes
Automated ETL Validation
Canary Deployments
Cloud Model Serving & Mobile/Edge Packaging
Google Vertex AI, Azure ML, PyTorch Workers, ONNX & CoreML
High-throughput cloud serving on Google Vertex AI and Azure ML alongside dedicated PyTorch MPS/CUDA worker fleets and automated INT8/FP16 model quantization for ONNX Runtime and Apple CoreML.
Google Vertex AI
Azure Machine Learning
ONNX Runtime & CoreML
PyTorch MPS / CUDA
NVIDIA Triton / TensorRT
Resilient AI Gateway
ASP.NET Core 10, Polly & Pub/Sub
.NET Polly retry/circuit-breaker policies, automatic multi-model fallback, Google Cloud Pub/Sub event queues, and SignalR streaming.
ASP.NET Core 10
.NET Polly Resilience
Google Cloud Pub/Sub
Drift & Quality Telemetry
OpenTelemetry & Drift Monitors
Real-time covariate/concept drift detection (PSI/KS), data anomaly alerts, LLM hallucination scoring, and Prometheus/Grafana dashboards.
Keyless cloud authentication via Google Cloud ADC and Azure Managed Identities, secret-free client binaries, and RBAC audit trails.
Google Cloud ADC
Azure Managed Identity
Zero Hardcoded Keys
Frequently Asked Questions
Executive & Architectural FAQs on MLOps Consulting Services
Answers on CI/CD/CT automation, Polly gateway resilience, cloud + edge packaging, drift monitoring, and AI FinOps.
How do your CI/CD/CT (Continuous Training) pipelines automate model updates without breaking production?
We build automated GitHub Actions and Azure DevOps pipelines that trigger whenever new validated training data arrives or model drift exceeds a threshold. New PyTorch or ONNX models are trained, versioned in MLflow and DVC, benchmarked against golden regression datasets, packaged into immutable Docker containers, and rolled out via canary or shadow deployments on Kubernetes or Google Vertex AI.
How do you protect production AI applications from LLM provider outages and rate-limit spikes?
We engineer resilient ASP.NET Core 10 AI gateways utilizing .NET Polly retry, timeout, bulkhead isolation, and circuit-breaker policies paired with Google Cloud Pub/Sub event orchestration. If a primary Vertex AI or Azure OpenAI region experiences latency spikes or HTTP 429 rate limits, traffic automatically fails over to secondary regions or fallback models with zero user-facing downtime.
Can you package and automate deployment for both cloud GPUs and on-device mobile/desktop Edge AI?
Yes. Our MLOps pipelines support dual-target packaging: we deploy high-concurrency PyTorch MPS/CUDA worker queues and Vertex AI/Azure ML endpoints for cloud inference while automatically quantizing and compiling INT8/FP16 ONNX Runtime and Apple CoreML model artifacts for over-the-air delivery to .NET MAUI iOS, Android, macOS, and Windows apps.
How do you detect model drift, data anomalies, and LLM hallucinations in real time?
We instrument every inference gateway and worker node with OpenTelemetry tracing, statistical feature-distribution monitors (PSI and KS-tests), and automated LLM-as-a-Judge grounding evaluators. When input distributions shift or confidence scores drop, our monitoring stack alerts engineering teams and queues edge-case samples for DVC/MLflow retraining.
How much can AI FinOps reduce our monthly cloud GPU and LLM token bills?
Enterprises typically reduce monthly AI infrastructure and token spend by 40% to 65%. We achieve this through Redis semantic prompt caching, Anchor & Delta context compression (proven on VisionFlow AI), tiered model routing (Flash/Mini models for routine classification), dynamic GPU autoscaling, and per-tenant token budget alerts.
Direct Principal Consultation
Architect Your MLOps & Production AI Infrastructure with Zeeshan Dad Khan
Share your current deployment bottlenecks, model serving targets, or cloud GPU/token cost challenges. Principal Architect Zeeshan Dad Khan personally reviews every inquiry within 24 hours on business days.
Principal Architect
Zeeshan Dad Khan
20+ years C#, .NET, Cloud & AI Infrastructure Architecture
