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Codemotives

AI & Data

From Prototype to Production AI/ML

We engineer the full AI/ML lifecycle: data pipelines, training, deployment and monitoring, so models stay reliable once real users depend on them.

Get in touchHow we work

Overview

AI/ML Development

Where other pages on AI stress strategy or features, this service is about engineering discipline. We take models from notebooks to production systems: reproducible training pipelines, versioned data and models, automated tests and deployment paths that your operations team can trust. That discipline is what lets teams trust, audit and safely change a model later.

Our AI/ML development work combines data engineers, ML engineers and software developers on one team, so the model is never isolated from the product around it. We set up monitoring for drift and performance, plan retraining, and document how the system behaves so it can be audited and maintained. Handover includes runbooks and training, so your own engineers can operate the system confidently.

Context

Problems we solve

  • 01

    Notebook-only models

    Models trained in notebooks are hard to reproduce or deploy. We move them into versioned, testable pipelines.

  • 02

    Model drift

    Real-world data changes and quality slowly erodes. We monitor inputs and outputs so drift is noticed early.

  • 03

    Fragile data flows

    Pipelines break when upstream systems change. We add validation checks and alerting at every stage.

  • 04

    Unclear ownership

    Data science and engineering teams often work in silos. We structure delivery so one team owns the result end to end.

Capabilities

What we deliver

  1. 01

    Data pipeline engineering

    We build ingestion, cleaning and feature pipelines with validation and lineage you can trace.

  2. 02

    Training workflows

    We set up reproducible experiments with tracked parameters, datasets and metrics.

  3. 03

    Model serving

    We deploy models as APIs or batch jobs with sensible latency, scaling and rollback behavior.

  4. 04

    MLOps automation

    We use CI/CD for models, including automated tests, staged releases and retraining triggers.

  5. 05

    Monitoring and alerts

    We track accuracy proxies, drift, latency and cost, and alert your team when behavior changes.

  6. 06

    Documentation and handover

    We provide model cards, runbooks and training so your team can operate and extend the system.

Where it fits

  • Productionizing an existing data science model
  • Building a retraining pipeline
  • Real-time scoring APIs for applications
  • Batch prediction for reporting systems
  • Model monitoring for regulated workflows

Technologies we use

  • Python
  • TensorFlow
  • PyTorch
  • MLflow
  • Docker
  • Kubernetes
  • Airflow
  • AWS SageMaker

Approach

How we deliver

  1. 1

    Discover

    We learn your goals, users, constraints and existing systems before proposing anything.

  2. 2

    Plan

    Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.

  3. 3

    Design

    User flows, interfaces and technical design are shaped and reviewed with you early.

  4. 4

    Build

    Work is delivered in short iterations with regular demos, so progress is always visible.

  5. 5

    Test

    Functional, integration and performance checks run throughout, not only at the end.

  6. 6

    Launch & support

    We deploy, monitor and keep improving the product after it goes live.

FAQs

Common questions

Have a question that is not covered here? Get in touch.

We already have a trained model. Can you deploy it?

Yes. We review the model, its dependencies and its data assumptions, then build the serving, testing and monitoring around it. Sometimes we recommend retraining so it fits a reproducible pipeline.

What is MLOps and do we need it?

MLOps is the set of practices for deploying, monitoring and updating models reliably. If a model affects customers or decisions beyond a one-off analysis, some level of MLOps is worth having.

How do you measure whether the model is good enough?

We agree on metrics tied to the business task and test on held-out data that reflects real use. We also look at failure cases, not only averages, before approving a release.

Explore

Keep exploring

Let's build what's next

Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.

Get in touch