From notebook to production, repeatably.

Most ML projects stall between the prototype and production. We build the platform that closes that gap: reproducible training pipelines, feature stores, model registries, CI/CD for models, scalable serving and monitoring for drift and quality, for both classic ML and LLM workloads.

What we deliver

MLOps & LLMOps

ML platform setup

Experiment tracking, model registry and artifact management on your cloud.

Training pipelines

Orchestrated, versioned and reproducible pipelines with automated retraining.

Model serving

Low-latency, autoscaling inference on GPUs or CPUs, including LLM serving with vLLM or TGI.

CI/CD for models

Automated tests, evaluation gates and progressive rollouts for every model version.

Monitoring & drift detection

Data quality, prediction drift, latency and cost dashboards with alerting.

LLMOps

Prompt versioning, eval pipelines, tracing and feedback loops for LLM apps.

Outcomes

What you can expect

  • Shorter time from experiment to deployed model
  • Reliable, observable models with automated rollback
  • Lower GPU spend through right-sizing and autoscaling
Technology

Tools we use

MLflowKubeflowSageMakerVertex AIAzure MLRayvLLMFeastEvidentlyWeights & Biases

We are vendor-neutral and choose tools to fit your stack, team and budget.

How it works

From first call to production.

  1. 1

    Free consultation

    A 30-minute call to understand your goals and constraints.

  2. 2

    Proposal

    A written scope, timeline and fixed price or monthly rate.

  3. 3

    Delivery

    Weekly demos and shared repositories, from day one.

  4. 4

    Handover & support

    Documentation, training and optional managed services.

FAQ

Common questions

Do we need Kubernetes for MLOps?

Not always. Managed services like SageMaker or Vertex AI are often the fastest path. We recommend Kubernetes when you need portability, cost control or custom serving.

Can you work with our existing data science team?

Yes. We usually build the platform alongside your team and leave them with templates, documentation and training so they can run it themselves.

Let's talk about MLOps.

Tell us what you are building. We will reply within one business day with next steps, and the first consultation is free.