Machine Learning Development
Machine learning development is building a predictive model — for forecasting demand, classifying records, detecting anomalies, or recommending items — trained specifically on your own operational data, distinct from general-purpose language models like the ones behind chatbots and agents. At Beacon Coders that means a smaller, purpose-built model for a defined prediction task, not a large language model repurposed for something it was not designed to do. Projects start at ₹4,00,000 / $4,800 and typically run 10 to 24 weeks, depending on data readiness and model complexity.
How this differs from integrating a large language model
This is the one AI service that involves genuine model training rather than integrating an existing one, and it is a meaningfully different kind of project. AI integration, chatbots and agents all build on pre-trained models from OpenAI, Anthropic or Google through an API. Machine learning development trains a model — often a much smaller one than a general-purpose language model — on your specific historical data, for a specific prediction task those general models are not built to do well: forecasting next quarter's demand from your sales history, flagging anomalous transactions against your specific fraud patterns, or recommending products based on your actual customer behaviour.
This is not a bigger, more serious version of AI integration — it is a different discipline with different cost drivers, chiefly data quality and quantity, and a different failure mode: a model can perform well in testing and still degrade in production as real-world patterns shift, which is why monitoring after launch is part of the deliverable, not an afterthought.
What's included
A data audit assessing whether your existing data is sufficient, in volume and quality, to support a reliable model for the task
done honestly before any development spend
Feature engineering
transforming raw operational data into the inputs a model can actually learn from
Model selection and training, using established approaches (regression, gradient boosting, or neural network architectures) matched to your task rather than defaulting to the most complex option available
Model selection and training, using established approaches (regression, gradient boosting, or neural network architectures) matched to your task rather than defaulting to the most complex option available
Validation against held-out data to measure real-world accuracy before deployment, not just performance on the training set
Validation against held-out data to measure real-world accuracy before deployment, not just performance on the training set
Deployment into your production environment, with an API your existing systems can call
Deployment into your production environment, with an API your existing systems can call
Monitoring for model drift
the gradual degradation that happens as real-world data patterns shift away from what the model was trained on
Retraining pipelines so the model can be updated on a schedule as new data accumulates, rather than going stale after launch
Retraining pipelines so the model can be updated on a schedule as new data accumulates, rather than going stale after launch
Documentation explaining what the model does, its known limitations, and the conditions under which its predictions should be treated with caution
Documentation explaining what the model does, its known limitations, and the conditions under which its predictions should be treated with caution
What's not included
Machine learning development does not include general-purpose language model integration for tasks like summarisation or drafting — see AI integration for that, which is faster and cheaper when a pre-trained model already does the job. It does not include training frontier-scale foundation models, which is a different category of research investment entirely. It does not include ongoing compute costs for running the trained model in production, which depend on your hosting choice and are scoped separately.
Frequently asked questions
How we scope and build a model
Discovery here starts with a data audit, not a modelling approach — we look at what data you actually have, how clean it is, and whether it covers enough history and variety to support the prediction you want, before committing to a build. This precedes the standard discovery stage behind every AI development project and is the single biggest factor in whether a machine learning project succeeds. If your data is not yet sufficient, we say so and outline what would need to change before a model is worth building, rather than starting anyway. Build proceeds in sprints covering feature engineering, training, and validation, with clear go/no-go checkpoints based on measured accuracy rather than assumed progress.