AI/ML Development
Custom models for prediction, classification and extraction, deployed and monitored
Services › AI Development › AI/ML Development
Not every problem needs a large language model. Forecasting demand, scoring risk, classifying transactions or detecting anomalies are often better served by focused machine learning models trained on your data. What matters is getting them into production with monitoring, not leaving them in a notebook.
We build the full path: data preparation, feature engineering, model training and evaluation, deployment as an API or batch job, and monitoring for drift. We work on Databricks, AWS and Azure with MLflow and standard MLOps practices.
What we do
- Problem framing and success metrics agreed with the business
- Data preparation and feature pipelines
- Model development, evaluation and explainability
- Deployment as APIs, batch scoring or in-platform models
- Monitoring for accuracy, drift and cost, with retraining plans
This is for you if
- You have historical data and a decision that is made repeatedly
- A rule-based system is becoming impossible to maintain
- A data science team has models that never made it to production
- You need predictions inside an existing application or report
How we work
- 1
Frame
Define the prediction, the metric and how it will be used.
- 2
Build
Prepare data, train and compare models, explain results.
- 3
Deploy
Ship as an API, job or in-platform model with tests.
- 4
Monitor
Track performance and retrain when it drifts.
Technologies we use
- Python
- scikit-learn
- PyTorch
- Databricks
- MLflow
- AWS SageMaker
- Azure ML
- PySpark
Common questions
Do we need a lot of data?
Enough history to represent the patterns you want to predict. We tell you honestly if it is not enough.
Can you explain the model's decisions?
Yes. We use explainability tools and prefer simpler models where the accuracy is similar.
Who owns the model?
You do. Code, model and documentation are handed over.
Ready to talk about AI/ML Development?
Call us or send a message and we will come back within one working day.