Machine Learning
Models trained on your own data to forecast, classify and flag what matters.
Machine learning here means training models on your own historical data. Examples are forecasting demand, predicting which customers will leave, or flagging suspicious transactions. Most of this work is done in Python with scikit-learn for tabular data and PyTorch for deep learning.
Good ML results depend more on clean data and a clear question than on clever algorithms. Hire someone who will check whether you have enough useful data before building anything, and who will tell you when a simple rule would do the job.
What We Build With It
- Clean, explore and prepare data with pandas and SQL
- Build classification and regression models with scikit-learn and gradient boosting
- Build time-series forecasts for sales, demand and stock
- Train deep learning models in PyTorch for images, text or sequences
- Measure model accuracy honestly on held-out data and explain results
- Deploy models as APIs and monitor them for drift
Where It Fits Best
- Sales and demand forecasting
- Customer churn and lead scoring
- Fraud and anomaly detection
- Product recommendations
- Image or document classification
Let's build somethingsimple & powerful
Frequently Asked Questions
How much data do we need for machine learning?
There is no fixed number. It depends on the problem and how noisy the data is. A developer should look at your data first and tell you honestly whether a model is likely to beat simple rules or averages.
Should we use machine learning or an LLM like ChatGPT?
Use machine learning for numeric predictions from your own records, such as forecasts, scores and risk flags. Use an LLM for language tasks such as answering questions, summarising or pulling data from documents. Many projects use both.
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