DATA ENGINEERING AND MLOPS
The infrastructure that makes your AI systems run reliably at scale
We build data pipelines, feature stores, model deployment infrastructure, and observability dashboards so your AI models run in production without manual intervention. If your team has built models in notebooks but cannot get them into production, or your deployed models are degrading and nobody knows why, this is the service to start with.
What is included
Data Pipeline Design and Implementation
We build batch and streaming data pipelines that move raw data from source systems (databases, APIs, file drops, event streams) into clean, structured formats ready for model training and inference. Includes schema validation, error handling, retry logic, and alerting on pipeline failures.
Feature Store Setup
We implement a centralised feature store (Feast or Tecton) so your data science team can define, compute, store, and serve features consistently across training and production. Eliminates the training/serving skew problem that causes models to behave differently in production than in notebooks.
Monitoring, Alerting, and Drift Detection
We build observability dashboards that track prediction quality, feature distributions, data freshness, latency, and throughput. Automated alerts fire when model drift is detected (input distribution shift, output quality degradation) so your team knows before users notice.
Technologies We Use
Apache Airflow
dbt
Apache
Spark
Apache
Kafka
Feast
Tecton
MLflow
Kubeflow
BentoML
Prometheus
Who this is for
Data science teams with models stuck in notebooks
You have trained models that work in Jupyter but have no path to production. We build the deployment infrastructure and CI/CD so your team can ship models with the same confidence as shipping code.
Companies scaling from one model to many
Your first model is in production but managing five or ten models manually is breaking. We set up shared infrastructure (feature store, model registry, monitoring) so every model gets the same reliability baseline.
Enterprises requiring governance
You need audit trails, model lineage, and access controls for compliance. We build the governance layer on top of your ML infrastructure so every model deployment is traceable and auditable.
Our Process
Discovery
Infrastructure audit and architecture design (week 1 to 2)
Prototype
Data pipeline build and feature store setup (week 3 to 5)
Build
Model deployment infrastructure and CI/CD (week 6 to 8)
Launch
Monitoring, alerting, and drift detection (week 9 to 10)
Support
Documentation, training, and handover (week 11 to 12)
Ready to build with generative AI?
Book a free scoping call and get a tailored proposal within 48 hours.
We already use AWS SageMaker / Azure ML. Can you work with our existing setup?
Yes. We build on top of your existing cloud ML platform rather than replacing it. If you are on SageMaker, we configure pipelines, endpoints, and monitoring within that ecosystem. Same for Azure ML and GCP Vertex AI.
How do we know if our models are degrading in production?
We set up automated drift detection that monitors input feature distributions and output prediction quality against baseline metrics established during training. When drift exceeds configurable thresholds, alerts fire to your team via Slack, email, or PagerDuty.
Do you train the models themselves or just deploy them?
This service focuses on the infrastructure layer: pipelines, deployment, monitoring, and governance. If you also need model training and development, we combine this with our Generative AI Development or AI Agents and Automation services in a single engagement.