JetBlue Airways accelerates innovation through the utilization of Databricks. Incorporating analytics, AI, and ML within their operations, JetBlue provides its customers with an efficient travel experience. This is achieved by handling the large volume of data generated by aircraft IoT sensors, operational changes, weather conditions, and customer interactions, amongst others. Using the Azure Multi-Cloud Data Warehouse and lakehouse architecture, internal and external data are enhanced continuously within the Databricks Data Intelligence Platform. The platform has a range of data tables with low latency, which are vital for decision-making in the fast-paced aviation environment.
- Integration of AI and ML for efficient management of vast data quantities and real-time decision making.
- Utilization of the Databricks Data Intelligence Platform for continuous enrichment of crucial operational data.
- Deployment of Delta Live Tables for data extraction, loading, and transformation - crucial for fulfilling diverse latency requirements.
- Adoption of the internally built BlueML library and MLflow, which enables efficient AI and ML model training and inference.
- Usage of the Generative AI technology allowing users to access role-specific KPIs and information with reduced training times and simpler access.
JetBlue Airways accelerates innovation through the utilization of Databricks. Incorporating analytics, AI, and ML within their operations, JetBlue provides its customers with an efficient travel experience. This is achieved by handling the large volume of data generated by aircraft IoT sensors, operational changes, weather conditions, and customer interactions, amongst others. Using the Azure Multi-Cloud Data Warehouse and lakehouse architecture, internal and external data are enhanced continuously within the Databricks Data Intelligence Platform. The platform has a range of data tables with low latency, which are vital for decision-making in the fast-paced aviation environment.
- Integration of AI and ML for efficient management of vast data quantities and real-time decision making.
- Utilization of the Databricks Data Intelligence Platform for continuous enrichment of crucial operational data.
- Deployment of Delta Live Tables for data extraction, loading, and transformation - crucial for fulfilling diverse latency requirements.
- Adoption of the internally built BlueML library and MLflow, which enables efficient AI and ML model training and inference.
- Usage of the Generative AI technology allowing users to access role-specific KPIs and information with reduced training times and simpler access.