[ The model is the smaller half of the job ]
A large share of machine learning models never make it into production, and the reason is rarely the modelling. It is that nobody owned deployment, nobody monitored drift, and the pipeline depended on a data scientist's laptop. Techtaru Digital is a machine learning development company that treats the model as the smaller half of the job — building custom ML systems with the deployment, monitoring and retraining attached.
A large share of machine learning models never make it into production, and the reason is rarely the modelling. It is that nobody owned deployment, nobody monitored drift, and the pipeline depended on a data scientist's laptop. Techtaru Digital is a machine learning development company that treats the model as the smaller half of the job — building custom ML systems with the deployment, monitoring and retraining attached.
Send us a description of the decision you want to improvemodels built for your data and decision, not an off-the-shelf score.
from feature engineering through training, validation and packaging.
problem framing, data readiness assessment, approach selection and honest feasibility review.
serving infrastructure, versioning, CI/CD for models, rollback and monitoring.
the application layer around the model, because a prediction nobody can act on has no value.
drift detection, performance tracking and scheduled or triggered retraining pipelines.
deployment inside your infrastructure with governance, lineage and auditability.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Demand and sales forecasting, churn and attrition prediction, lead scoring, credit and risk scoring, maintenance prediction and inventory optimisation — with confidence intervals presented rather than a single misleadingly precise number. Where the value is unproven, validate with a PoC first.
Fraud and transaction anomalies, network and system irregularities, quality deviations in production and unusual behaviour in operational telemetry, tuned for the false-positive rate your team can actually handle.
Product, content and next-best-action recommendations using collaborative filtering, content-based approaches or hybrid models, with cold-start handling designed rather than ignored.
Where the problem genuinely needs it — complex sequence modelling, multimodal inputs, or pattern recognition beyond what gradient boosting handles. We say plainly when it does not.
Document and ticket routing, customer segmentation, risk tiering and eligibility decisions, with explainability where a decision affects a person.
Demand, capacity, price and load forecasting with seasonality, holiday effects and external regressors handled properly.
Automated model search to establish a strong baseline quickly, which is often the honest benchmark a custom model must beat to justify its cost.
Feature stores, experiment tracking, model registries, deployment pipelines and monitoring — the infrastructure that turns one-off models into a repeatable capability.
what decision the prediction supports, what accuracy is needed to change that decision, and what the current baseline performs at.
volume, quality, labelling, leakage check and feature availability at prediction time.
baseline first, then complexity only where it earns its place, validated on held-out and time-based splits.
serving infrastructure, feature pipeline, versioning, rollback and CI/CD for models.
drift detection, performance dashboards, alerting and a retraining schedule with clear ownership.
Frameworks: scikit-learn, XGBoost and LightGBM for tabular problems, PyTorch and TensorFlow for deep learning, Prophet and statsmodels for time series. Serving: FastAPI, TorchServe, TensorFlow Serving, ONNX Runtime for portable inference. MLOps: MLflow, DVC, Feast, Kubeflow or SageMaker Pipelines. Data: Spark, dbt, Airflow, PostgreSQL, Snowflake, BigQuery. Monitoring: Evidently, custom drift detection, and alerting into the channels your team already watches. Cloud: AWS SageMaker, Azure ML, GCP Vertex AI, or on-premise where data cannot move.
The model is roughly 10% of the work. Data pipelines, feature engineering, serving infrastructure, monitoring and retraining are the rest. A notebook with 94% accuracy is not a system. We scope the full pipeline from the start, which makes our estimates look larger than a modelling-only quote and makes them accurate.
Data readiness decides feasibility more than algorithm choice. Volume, labelling quality, class balance, leakage and whether historical data actually reflects current conditions. Most of the honest "no" answers we give come from this assessment, and giving them in week one is cheaper for everyone.
Beware target leakage. A model performing suspiciously well in validation is usually seeing information at training time that will not exist at prediction time. It is the single commonest reason a model that looked excellent collapses in production, and finding it requires someone who has been burned by it before.
Models degrade, and nobody notices without monitoring. Customer behaviour shifts, upstream data formats change, the world moves. Without drift detection and performance tracking, a model quietly gets worse and continues being trusted. Monitoring is not an optional phase two.
Start with a baseline you must beat. The current rules engine, the human decision, or a simple logistic regression. If a complex model cannot meaningfully beat a simple one, the simple one wins — it is cheaper to run, easier to explain and less likely to break.
Explainability where decisions affect people. Credit, hiring, insurance and healthcare decisions attract regulatory and ethical obligations. SHAP values, feature importance and documented decision logic are part of the build, not an afterthought — and high-risk uses fall under the EU AI Act for deployers as well as developers.
Fixed-scope PoC; phased pricing for production ML systems; monthly squads for teams building ongoing ML capability; retainers for monitoring, retraining and model maintenance.
Indicative cost: an ML proof of concept runs $8,000–$20,000. A production ML system including pipeline, deployment and monitoring typically lands at $30,000–$80,000. MLOps platform builds are larger and phased. Ongoing monitoring and retraining retainers generally start around $1,500 per month per model family, which is far cheaper than discovering drift through a business incident.
Before committing to an ML build, the useful step is a short data readiness assessment — volume, labelling, leakage risk and realistic accuracy ceiling. Send us a description of the decision you want to improve and we will tell you whether the data supports it.
It depends on the problem. Tabular classification can work with a few thousand well-labelled examples; deep learning on complex patterns usually needs far more. We assess this in the first week and tell you honestly if the data will not support the target.
Let’s talk about your machine learning project. No obligation, just a conversation.
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