Production-Grade MLOps & Cloud Infrastructure — So Your AI Never Goes Stale
Training a model is easy. Keeping it accurate, secure, and cost-effective across millions of predictions in production is the real engineering challenge.
Infrastructure Capabilities
Automated ML Pipelines
Building end-to-end CI/CD pipelines for machine learning. Automate data extraction, model training, validation, and deployment.
Model Serving & Inference
Optimizing model weights via quantization (ONNX/TensorRT) and deploying high-throughput, low-latency microservices via Kubernetes.
Drift Detection Systems
Implementing statistical monitoring to detect data drift, concept drift, and performance degradation in real-time, triggering automated retraining.
Compute Cost Optimization
Architecting dynamic, auto-scaling infrastructure on AWS/GCP to scale GPU instances down to zero when idle, cutting inference costs drastically.
Secure VPC AI Deployment
Deploying open-source LLMs (Llama 3, Mistral) entirely within your private cloud. Zero public internet access. Absolute data sovereignty.
Feature Store Architecture
Centralizing standardized machine learning features (e.g., using Feast or Hopsworks) to prevent data leakage and guarantee online/offline consistency.
How We Operationalize AI
01: Architecture Audit
We review your current cloud footprint, identifying bottlenecks, security vulnerabilities, and areas of massive compute overspending.
02: IaC Implementation
Re-platforming your ML workloads using Terraform. We define your entire infrastructure as reproducible code.
03: Pipeline Construction
Setting up GitHub Actions or GitLab CI strictly for machine learning. We automate the journey from a Jupyter Notebook to a Docker container.
04: Monitoring Setup
Integrating Datadog, Prometheus, or Arize AI to track GPU utilization, inference latency, API error rates, and model accuracy.
05: Load & Chaos Testing
Intentionally breaking the system. We test auto-scaling triggers and failover redundancy to ensure 99.99% uptime during peak loads.
06: Ongoing SRE
The launch is just the beginning. We provide round-the-clock monitoring, Kubernetes cluster management, and incident response.
The Tools We Trust in Production
We remain stack-agnostic, choosing the right combination of state-of-the-art research models and bulletproof enterprise engineering tools for every project.
AI / ML Frameworks
LLMs & Orchestration
Vector Databases
Cloud & MLOps
Backend & Data
Frontend & Mobile
Sector-Specific Intelligence,
Proven Results
Infrastructure Wins
Fintech GPU Cost Optimization
Redesigned a struggling startup's AWS architecture to utilize Spot instances and dynamic batching, slashing their monthly GPU bill by 60%.
Air-Gapped LLM Deployment
Successfully deployed a fine-tuned billion-parameter model onto bare metal servers for a defense contractor requiring zero internet connectivity.
E-Commerce Pipeline Automation
Replaced 5 manual data science scripts with an automated Airflow/MLflow pipeline, allowing models to retrain daily without human intervention.
Common Questions
Everything you need to know about our research methodology, engagement models, and AI engineering practices.
Machine Learning Operations (MLOps) is the extension of DevOps to machine learning. It's the set of practices combining software engineering, data engineering, and data science to reliably and efficiently deploy and maintain ML models in production.