Overview
Choose one of the following MLOps journeys for the corresponding AI Platform documentation.
Traditional AI/ML
Managed Azure Machine Learning
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 cases:
- 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
Advanced AI
Custom MLOps
When your project requirements surpass Traditional AI/ML, use Advanced Al to set up your MLOps tools and configurations. Advanced Al covers a wide range of MLOps journeys. To learn more about each journey and their particular setup and tools, refer to the links on this page.
Use cases:
- Generative & Knowledge AI
- Computer Vision (CV)
- Autonomous Machines & Robotics
- EdgeML (EDGE)
- Predictive Maintenance & Anomaly Detection
- AI Optimization (OPT)