Predictive analytics
Demand, sales, churn, risk and price forecasting with clear accuracy reports and explanations.
Machine Learning Development
Machine Learning Development Company turning your data into decisions
We build machine learning models that forecast demand, spot fraud, recommend the right product and read images, then put them into production with monitoring, so they keep working on real data.
- Models trained on your data
- From data audit to MLOps
- Clear accuracy targets up front
450+Projects launched
7+Years of experience
20+Dedicated developers
96%Client retention
In short
Dinoustech is a machine learning development company that builds custom ML models for forecasting, recommendations, fraud detection, customer churn, pricing and computer vision. We handle the full lifecycle: data audit, feature engineering, model training with Python, PyTorch and scikit-learn, deployment as APIs, and MLOps monitoring and retraining. A first production model typically takes 6 to 12 weeks.
The basics
Machine learning vs generative AI: which one do you need?
Machine learning finds patterns in your historical data and uses them to predict or classify: how many units will sell next week, which customers may leave, which transaction looks like fraud, which product a user will buy next, or what is in a photo.
Generative AI, by contrast, creates new content such as text and images. Many strong products combine both: an ML model scores a lead or a loan application, and a generative model explains the result in plain language to the user.
If your question is a number, a yes or no, a ranking or a category, machine learning is usually the right, cheaper and more accurate tool. We help you decide before any code is written.
What we build
Machine Learning Development Services
Practical ML that runs in production, not models that stay in a notebook.
Recommendation systems
Personal product, content and match recommendations that lift clicks and order value.
Fraud and anomaly detection
Real-time scoring of payments, logins and claims to stop fraud with fewer false alarms.
Computer vision
Object detection, OCR, quality inspection, face and ID verification for apps and factories.
NLP and text analytics
Classify tickets, extract data from documents and measure sentiment across reviews and calls.
MLOps
Pipelines, model registry, monitoring, drift alerts and automatic retraining on AWS, Azure or GCP.
Use cases
Machine Learning Use Cases
High-return ML projects we deliver across industries.
| Use case | Industry | What it improves |
|---|---|---|
| Demand forecasting | Retail, FMCG, food delivery | Less stock-out and waste, better staffing |
| Credit scoring and fraud detection | Fintech, lending, insurance | Lower defaults and fraud losses |
| Churn prediction | SaaS, telecom, subscription apps | Keeps more customers with timely offers |
| ETA and route optimisation | Taxi, logistics, delivery | Faster deliveries, lower fuel cost |
| Medical image and report analysis | Healthcare, diagnostics | Faster triage support for doctors |
| Dynamic pricing | Travel, eCommerce, ride-hailing | Better margins with competitive prices |
How we work
Our Machine Learning Development Process
A clear, step-by-step process with a working demo early, so you always know what you are getting.
- 01
Data audit
We review the data you have, its quality and gaps, and confirm the problem can be solved with ML.
- 02
Baseline and target
We set a simple baseline and an agreed accuracy target, so success is measurable.
- 03
Model development
Feature engineering, model training and testing, with explainability for business users.
- 04
Deployment
The model goes live as a secure API or batch job, connected to your app or dashboard.
- 05
MLOps and retraining
Monitoring for accuracy and data drift, with scheduled retraining to stay accurate.
Technology
AI Tools and Technologies We Use
We pick proven, well-supported tools for each project, and keep your product free to switch models later.
ML and data
- Python
- scikit-learn
- XGBoost
- PyTorch
- TensorFlow
- Pandas
Vision and NLP
- OpenCV
- YOLO
- Hugging Face
- spaCy
- Tesseract OCR
MLOps
- MLflow
- Airflow
- Docker
- Kubernetes
- SageMaker
- Vertex AI
Data stores
- PostgreSQL
- BigQuery
- Snowflake
- MongoDB
- Redis
Pricing
Machine Learning Development Cost
Starting prices for common ML engagements. Data quality has the biggest effect on time and cost, so every project starts with a data audit.
| Scope | What you get | Timeline | Starting from |
|---|---|---|---|
| Data audit and ML feasibility | Data review, baseline model, accuracy report, roadmap | 2-3 weeks | $3,000 |
| Production ML model | One model trained, deployed as API, dashboard, monitoring | 6-12 weeks | $15,000 |
| ML platform | Several models, data pipelines, full MLOps and retraining | 3-6 months | $40,000 |
Cloud training and hosting costs depend on data size and traffic and are billed at actual cost.
Why Dinoustech
Why Choose Dinoustech for Machine Learning Development
450+ projects launched
Apps, platforms and software shipped for startups and enterprises in India, the USA, UK, UAE, Canada, Australia and Singapore.
Your data stays yours
NDA from day one, private model hosting when needed, and no training of public models on your data. GDPR and HIPAA-ready builds.
Working demo in weeks
We ship a usable prototype on your real data early, so you judge results, not slides, before the full build.
Support after launch
Monitoring, accuracy reviews, cost tuning and model upgrades are part of the plan, so the AI keeps getting better.
Free, no obligation
Get Your Free Machine Learning Development Plan
Share your idea and an AI expert will reply within 24 hours with:
- The best AI approach for your goal
- Features, tech stack and timeline
- A clear cost estimate
- NDA before we discuss details
FAQs
Machine Learning Development FAQs
What does a machine learning development company do?
A machine learning development company turns business data into models that predict, classify or recommend, such as demand forecasts, fraud scores or product recommendations, and runs those models in production with monitoring and retraining.
How much does machine learning development cost?
A data audit with a baseline model usually starts around $3,000. A single production ML model deployed as an API starts around $15,000 and takes 6 to 12 weeks, and a full ML platform with several models and MLOps starts around $40,000.
How much data do I need for machine learning?
It depends on the problem. Simple forecasting can work with one to two years of daily records, while image models may need thousands of labelled photos. A short data audit tells you exactly what is enough and how to fill gaps.
What is MLOps and why does it matter?
MLOps is the practice of deploying, monitoring and retraining ML models reliably. Real-world data changes over time, so without monitoring and retraining a model's accuracy quietly drops. MLOps keeps it accurate and auditable.
Can machine learning be added to my existing software?
Yes. We deploy models as secure APIs or scheduled jobs that plug into your existing app, ERP, CRM or dashboard, so your team sees predictions where they already work.
Machine learning or generative AI, which should I choose?
Choose machine learning when you need a number, category, score or ranking from your data, such as a forecast or fraud check. Choose generative AI when you need new text, images or answers. Many products use both together.
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