Machine-learning forecasting taken from notebooks to a monitored production pipeline — with automated retraining, evaluation and drift detection so predictions stay trustworthy over time.
Forecasts now refresh automatically and are monitored for drift, with clear ownership and rollback.
Forecasting lived in analysts’ notebooks — hard to deploy, monitor or trust as the underlying data drifted.
We built an MLOps pipeline with reproducible training, automated evaluation gates, scheduled retraining, and drift and health monitoring dashboards the client’s team can operate.
Forecasts refresh on schedule and are continuously monitored, with clear ownership and safe rollback. [[Replace with an approved metric.]]
Let's scope what a proof of concept would look like on your data.
