01
Model deployment pipelines
CI/CD for models on SageMaker, Vertex AI, Azure ML, or self-managed Kubernetes – with shadow deployments, canaries, and automated rollback on degradation.
AI/MLOps
Machine learning that holds up
in production
Move models out of notebooks and into reliable, observable, retrainable production systems – with the same engineering rigor you apply to the rest of your platform.
We build the pipelines, deployment patterns, and monitoring that make ML a repeatable engineering discipline rather than a one-off experiment.
Overview
AI/MLOps is the engineering discipline around production machine learning
Most ML projects fail not at the model, but at the system around it. Data leaks between training and serving, models silently degrade, retraining is manual, deployments are risky, and nobody can reproduce a result from six months ago. AI/MLOps closes that gap.
AI/MLOps is the engineering discipline of running ML in production. It combines data engineering, model lifecycle management, deployment, and observability into one continuous workflow – owned end-to-end by the same team that ships the rest of your software.
DataX Power builds AI/MLOps platforms that turn ML projects from one-off experiments into a repeatable capability. Whether you are deploying your first model or scaling to dozens, we deliver the engineering foundation that makes the difference between an ML demo and an ML system.
What we deliver
The complete
AI/MLOps engagement
A complete AI/MLOps platform – from feature pipelines to monitored production endpoints – with the documentation and observability your team needs to operate it.
02
Versioning for data, code, and models
MLflow, DVC, Weights & Biases, or feature store integration so every prediction can be traced back to the exact data, code, and parameters that produced it.
03
Feature engineering and feature stores
Reusable feature pipelines (Feast, Tecton, in-house) with offline/online consistency – so models train and serve on the same data definitions.
04
Model monitoring and observability
Drift, performance, and fairness monitoring with Evidently, WhyLabs, Arize, or open-source equivalents – with alerting tied into your existing on-call.
05
Retraining workflows
Automated triggers based on drift, performance, or schedule – with human-in-the-loop checkpoints where regulation or business sensitivity demands them.
06
Scalable inference infrastructure
Real-time, batch, and streaming inference patterns. GPU autoscaling, request batching, and model compilation (TensorRT, ONNX) for cost-efficient serving at scale.
Use cases
Where AI/MLOps typically
drives impact
- Move ML models from notebooks to production for the first time
- Replace manual deployment scripts with reproducible CI/CD pipelines
- Build a feature store to eliminate train/serve skew
- Stand up real-time or streaming inference at production scale
- Implement drift, performance, and fairness monitoring
- Operationalise LLMs and generative AI workloads with cost controls
Why choose us
Why teams partner with us
ML engineers and platform engineers in one team
We bring data scientists and platform engineers on the same engagement so the platform actually fits how models are built.
Vendor-neutral architecture
Production experience across SageMaker, Vertex AI, Azure ML, Databricks, and self-managed Kubernetes – we design for your constraints, not a vendor preference.
Built for the long run
Our platforms are designed to host the next 20 models, not just the first one.
Outcome-focused
We measure success against time-to-deployment, model reliability, and inference cost – the metrics that decide whether ML pays off.
Outcomes
What you walk away with
- Time from model trained to model in production reduced from months to days
- Reproducible pipelines for data, training, and deployment
- Drift and performance monitoring on every production model
- Inference cost-per-request optimised for your traffic pattern
- A platform your team can run, extend, and govern without us
Ready to Get Started
Let's build what's next
Share your challenge – AI, data, or infrastructure. We'll scope your project and put the right team on it.