AI Forecasting & Machine Learning Services in Australia
JDML designs, trains, and deploys machine learning and AI forecasting systems for Australian businesses. Forecasting, classification, anomaly detection, churn prediction, demand planning, and custom decision-support systems: built as production APIs, evaluated against real outcomes, monitored after launch, and designed to improve over time.
AI and ML for the Australian market
Australian businesses face the same competitive pressure from AI as the rest of the world, often with smaller internal teams and fewer vendor options tuned for local data. We build machine learning systems specifically for the problems that matter here: commodity and property price forecasting, demand planning for seasonal Australian markets, fraud and anomaly detection for financial and e-commerce products, and churn prediction for subscription and service businesses.
From first model to production system
Most ML projects fail between the notebook and production. We work from problem definition through data preparation, model selection, training, evaluation, and deployment as a monitored API on Google Cloud. We use PyTorch, scikit-learn, XGBoost, and LightGBM depending on the problem, and deploy via Vertex AI or Cloud Run with experiment tracking and CI/CD from day one.
Evaluation, testing, and MLOps
A model that passes offline evaluation but degrades in production is a liability. We build evaluation harnesses, regression test suites, data drift monitors, and performance dashboards into every ML system. Before deployment, models go through rigorous offline and online evaluation. After deployment, we monitor predictions, data distributions, and business metrics so degradation is caught early.
- →Time-series forecasting (prices, demand, churn)
- →Custom model training with PyTorch and scikit-learn
- →Fine-tuning open-weight and proprietary models
- →Evaluation harnesses, benchmarking, and regression tests
- →Anomaly and outlier detection
- →Model serving via Vertex AI and Cloud Run
- →MLOps: experiment tracking, CI/CD, monitoring
- →Applied AI research and R&D engagements
Forecasting, planning, and decision-support workflows
Products that need ML tied to measurable business outcomes
Teams that need evaluation, monitoring, and model improvement over time
Questions we get.
What kinds of forecasting problems do you work on?
Price forecasting, demand planning, inventory optimisation, churn prediction, lead scoring, revenue forecasting, and anomaly detection are the most common. If you have a prediction problem and data to train on, we can assess whether ML is the right approach.
How do you make sure the model keeps working after launch?
We build data drift monitoring, prediction distribution tracking, and business-metric alerting into every deployment. When model performance degrades, we find out before you do.
Do you work with smaller Australian businesses, not just enterprises?
Yes. Most of our ML work is with growing Australian businesses and startups who want a production ML system but don't have the internal team to build and operate one. We scope engagements to match the stage of the business.
Ready to get started?
Tell us about your project. We reply within 24 hours, always from the engineers.
Get in touch