CSSU-OPT-KAI
This project corresponds to AI Platform's Generative AI & Knowledge AI MLOps journey.
Project Goals
The aim of this project is to enable operating assets to improve risk management and deliver more reliable plans to the offshore organization by utilizing Natural Language Processing (NLP) models. This project envisions to automaticaly discover lessons learned from previous safety incidents relevant to activities in the operational plans.
This tool mines the content of Equinor's Synergi database to help determine:
- What activities were being performed and where?
- On which systems / equipment?
- Under which conditions when the incident occurred?
You can find more information about this tool at:
Summary of Results
Data scientists using nlp to find the most relevant reports for operational planning purposes. They run a continuous batch job requiring high CPUs and GPUs.
Project Team
Jennifer Sampson (Data Scientist)
Bjarte Johansen (Data Scientist)
Peter Koczca (Data Scientist)
Terje Elde (K8 Developer)
MLOps Challenges
Large Compute Need
The team needed an ability to deploy continously running ML model workload to process reports.
Run Batch Jobs Contineously
During deployment, batch jobs needed to be run continuously while will process plant reports using NLP models.
MLOps Solutions
K8 Deployments for Processing
The NLP models to process the plant incident reports were deployed using K8 Deployment on AI Platform's Kubernetes cluster.
GitHub Repos
https://github.com/equinor/cognitive_ssu/
https://github.com/equinor/cognitive_ssu-k8s