MLOps & AI Infrastructure
Your data scientists can build models. Getting those models into production reliably - versioned, monitored, and auto-retrained - is a different engineering problem entirely.
Get StartedThe Problem
Most ML teams hit the same wall: models work beautifully in Jupyter notebooks and then take months to reach production. The gap between data science and engineering is not a skill gap - it is an infrastructure gap.
Without MLOps infrastructure, every model deployment is a bespoke, manual process. Model versions go untracked. Inference latency spikes are invisible. Data drift goes undetected until accuracy degrades and nobody knows why. Feature pipelines are brittle scripts that only the person who wrote them understands.
MLOps is the DevOps discipline applied to machine learning. We bring the same automation, observability, and reliability principles that transformed software deployment - to your model lifecycle.
Our Approach
Audit your ML workflow
We map how models are trained, versioned, deployed, and monitored today. We identify every manual handoff and every point where things break between experimentation and production.
Build the training pipeline
Automated, reproducible training pipelines with DVC for data versioning and Weights & Biases or MLflow for experiment tracking. Every run is logged, every artifact is versioned.
Model registry and deployment
We set up a model registry (MLflow, Vertex AI, SageMaker) and automated deployment pipelines to your serving infrastructure - BentoML, Triton, Seldon, or managed endpoints.
Monitoring and retraining triggers
Data drift detection, model performance monitoring, and automated retraining pipelines so your models stay accurate as your data evolves.
What You Get
- Automated model training pipeline (Kubeflow Pipelines or Airflow)
- Experiment tracking with MLflow or Weights & Biases
- Data versioning with DVC
- Model registry with staging/production promotion workflow
- Inference serving infrastructure (BentoML, Triton, or managed)
- Model performance monitoring and drift detection
- Automated retraining triggers
- Feature store integration (Feast or managed)
- Full documentation and team handoff
Tech Stack
Real Example
Context: Series B fintech with 4 data scientists. Model deployment took 6–8 weeks and required manual engineering involvement each time.
MLOps pipeline reduced model-to-production time from 6 weeks to 3 days. Data team now deploys models independently.