AI & Intelligent Solutions · Service 10 of 14

Machine Learning Development the simplest model that meets the target

O Clock Software, headquartered in Chennai, India, builds custom machine learning models for classification, ranking, recommendation, allocation and anomaly detection, and ships them as production services. We start with the simplest model that meets the target and add complexity only when the data proves it helps.

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  • 2009Founded in Chennai
  • 1,000+Projects delivered
  • 12+Countries served
  • 100+Engineers in-house
  • 4.6★Google rating · 59 reviews

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What we build

Machine Learning Development: what we build

Custom models trained, evaluated and shipped into production systems. Here are the six kinds of machine learning development work we are asked for most often.

Model complexity

The model ladder: we climb only as far as needed

More complex models are harder to explain, run and maintain. Each step up must earn its place with a measurable gain on your data.

  1. Step 1Rules and heuristics

    When it fits: The pattern is known and stable.

    Trade-off: Easy to explain; breaks when patterns change.

  2. Step 2Linear and logistic models

    When it fits: Clear relationships and a need for explanation.

    Trade-off: Fast and transparent; misses complex interactions.

  3. Step 3Gradient-boosted trees

    When it fits: Tabular business data with many factors.

    Trade-off: Strong accuracy on tables; needs careful feature work.

  4. Step 4Deep learning

    When it fits: Images, text, audio or very large datasets.

    Trade-off: Most powerful; needs more data, compute and monitoring.

How we deliver

How we deliver Machine Learning Development

  1. 01

    Frame the problem

    Prediction target, success metric and how the output will be used.

  2. 02

    Prepare data and features

    Cleaning, joining and feature engineering with your domain experts.

  3. 03

    Baseline and experiments

    Simple benchmark first, then tracked experiments.

  4. 04

    Evaluate and check fairness

    Held-out testing, error analysis and checks across groups.

  5. 05

    Deploy and monitor

    Versioned API or batch service with drift monitoring.

Technology

Technology for Machine Learning Development

Chosen per project for fit, maintainability and your team's skills.

Data

  • Python
  • SQL
  • pandas
  • Apache Spark

Modelling

  • scikit-learn
  • XGBoost
  • LightGBM
  • PyTorch
  • TensorFlow

Serving

  • FastAPI
  • BentoML
  • Docker
  • Kubernetes
  • SageMaker
  • Vertex AI

Tracking

  • MLflow
  • Weights & Biases
  • DVC

Straight answers

Is Machine Learning Development right for you?

We would rather recommend less than sell you more. Here is how we judge it.

A good fit when

  • A repeatable decision is made from data
  • Historical examples with known outcomes exist
  • Accuracy can be measured
  • The prediction will be used inside a system or process

  • You need text generation

    Drafting and conversation are generative AI tasks. See Generative AI Development.

  • Labelled outcomes do not exist

    Supervised models need examples with known answers; a labelling phase may come first.

  • A pre-trained service already solves it

    For common tasks, an existing API may be good enough.

Use cases by industry

Machine Learning Development by industry

The same capability, shaped by the data, rules and users of each sector.

Related work

Where we have shipped this

Platforms in production that use this capability. See all work

Related services

Often combined with Machine Learning Development

Solutions built on this service

Prefer engineers in your own team?

The full service area

FAQ

Machine Learning Development FAQ

What is the difference between machine learning and AI?

AI is the broad goal of making software perform tasks that need human-like judgement; machine learning is the main technique for achieving it, where models learn patterns from data rather than following hand-written rules. Generative AI and computer vision are both built on machine learning.

What types of machine learning problems do you solve?

We build classification, regression, ranking, recommendation, clustering, anomaly detection, forecasting and optimisation models, plus deep learning for text, images and audio.

How do you choose the right algorithm?

We start with a simple baseline, then test progressively more complex models on the same held-out data and choose the simplest one that meets the target. Explainability, response time and maintenance needs weigh into the choice as much as accuracy.

How are machine learning models deployed?

Models are packaged as versioned services, either a real-time API for instant predictions or a batch job that scores data on a schedule, and run in containers on your cloud with monitoring.

How do you check models for bias?

We measure performance across relevant groups, inspect which features drive predictions, remove or adjust features that act as unfair proxies, and document the results so decisions can be reviewed.

Do you work with our in-house data team?

Yes. We can build end to end or work alongside your data scientists, for example by productionising their models, setting up MLOps or adding engineering capacity.

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