Traditional AI/ML
Lifecycle stages:
MLOps journey description: The Traditional AI/ML journey is best suited for structured tabular data, enabling statistical analysis and predictive modeling for business-critical tasks. Typical use cases include classification, regression, and time-series forecasting. In Equinor’s AI Platform, this journey is powered by a managed Azure Machine Learning (ML) infrastructure—a fully managed service that supports the end-to-end ML lifecycle while abstracting away the complexity of compute and environment setup.
Use case examples: Traditional ML applied to structured business and operational data.
- Forecasting oil production and equipment downtime
- Classifying rock types or formations from drilling data
- Predicting energy consumption or cost per barrel
- Analyzing drilling efficiency and rate of penetration
- Clustering wells by performance characteristics