Capability
MLOps
A model that isn't operated is a liability with good intentions. We build the operational backbone for machine learning — CI/CD, registries, monitoring, and drift detection — so models ship faster and stay trustworthy in production.
Proven where it counts
API design & development
Accelerated delivery with cost savings for two public-facing API products. Transitioned from Akamai to AWS firewall for a direct cost reduction.
- API
- AWS
- Cost Optimization
100+ microservices, event-driven architecture
Accelerated build, deployment, and scaling timelines for a large-scale program by maintaining 150+ AWS EC2 instances and a 90-microservice polyglot containerized platform.
- Java
- GovCloud
- Microservices
High-performance data engineering
Built and supported a data warehouse capable of sub-second, high-throughput queries. Designed ETL pipelines using Apache Spark Structured Streaming in the cloud.
- Apache Spark
- ETL
- Big Data
What we deliver
ML Pipelines & CI/CD
Versioned, tested, automated paths from data to deployed model — every release reproducible and reviewable.
Model Registry & Governance
A single source of truth for model versions, lineage, approvals, and audit trails — essential in regulated environments.
Reproducible Training
Pinned data, code, and environments so any model can be rebuilt, explained, and defended months later.
Monitoring & Drift Detection
Live tracking of input distributions, prediction quality, and business metrics — with alerts before users notice degradation.
Serving & Inference Infrastructure
Low-latency, autoscaling inference on Kubernetes, SageMaker, or Vertex — sized for your traffic and budget.
Evaluation Gates
Automated offline and online evaluation that blocks bad models from shipping — including LLM evals for generative systems.
Approach
Treat models like releases, not experiments.
The gap between a promising model and a dependable system is operational, not algorithmic. We close it with the same discipline software earned decades ago: version everything, test before shipping, monitor after, and keep an audit trail — so every model in production has a paper trail and a rollback plan.
- Every model versioned, approved, and traceable
- Evaluation gates on every release — including LLM evals
- Drift alerts tied to business metrics, not just statistics
- Infrastructure-as-code across the entire ML stack
120-day MVP delivery. Partnered with a prime contractor to rapidly deliver an MVP web application — deployed within 120 days, the same disciplined release cadence we automate for models.
Technologies we work in
- MLflow
- Kubeflow
- SageMaker
- Vertex AI
- Airflow
- DVC
- Docker / K8s
- Terraform
- GitHub Actions
- Evidently
Common questions
How is MLOps different from DevOps?
DevOps manages code; MLOps manages code plus data plus models. A model can break without a single line of code changing — because the world drifted. MLOps adds the registry, lineage, evaluation gates, and drift monitoring that make model behavior as governable as software releases.
We have models in notebooks. Where do we start?
The first step is reproducibility: version the data, pin the environment, and script the training path. From there we add automated evaluation, a registry, and deployment pipelines. Teams usually reach a governed, repeatable release process within the first engagement phase.
Can MLOps satisfy federal audit and compliance requirements?
Yes — that is where it earns its keep. Registries, lineage, and evaluation gates produce exactly the evidence auditors and authorizing officials ask for: what model is running, what data trained it, who approved it, and how it performs. We design those controls to map to your compliance framework.