Dear visitor, please be informed that this is the ITEA staging environment. No actions here will be updated to production, feel free to test the system
Automated ML Data Pipeline
- Project
- 17002 AutoDC
- Type
- New standard
- Description
- Collect, analyze, train and produce prediction models.
- Monitor and maintain the model accuracy.
- Predict a range of environmental or cost states.
- Deploy on-prem or in the cloud.
- Contact
- Alex Petrovic
- alex@marinerpartners.com
- Technical features
Input(s):
- Sensory data from HVAC equipment, environmental data (temperature, humidity, etc), energy cost data, etc.
Main feature(s):
- An automated process for ML-based predictive modules. The framework includes data extraction, training, model creation, approval, monitoring and validation components.
Output(s):
- Models for predicting temperature, energy cost, etc for multiple zones within a facility.
- Integration constraints
N/A
- Targeted customer(s)
Organizations running datacenters or other commercial facilities.
- Conditions for reuse
N/A
- Confidentiality
- Public
- Publication date
- 30-09-2021
- Involved partners
- Mariner Partners Inc (CAN)