GE Digital, a pioneer in the Industrial IoT (IIoT) landscape, wants to harness the power of IoT data streams to drive digital transformation across various sectors of the global economy. This task involves integrating vast amounts of data from diverse sources, ensuring real-time processing, and providing actionable insights, all while maintaining data security and scalability.
GE Digital needed a resilient and scalable SaaS architecture to manage and integrate IoT data, including real-time data ingestion, processing, and integration with various systems. The company also needed to ensure data security, especially in multi-tenant environments where competitors might share the same platform. Complicating matters further, the rapidly evolving landscape of signals, sensors, and analytics demands agility and flexibility.
All of this existed against the need for monetizing digital products and business models via IoT while ensuring seamless integration with enterprise IT.
To improve GE Digital’s control over both the structure and direction of its architecture, CloudGeometry instituted multiple AI Machine Learning improvements, including:
- Advanced Analytics and Machine Learning: Leveraged Python and AI to provide real-time insights from the data, enabling GE Digital to achieve data-driven efficiency, productivity, and profitability. Specifically, these changes enabled predictive maintenance, anomaly detection, and optimization algorithms.
- Data Pipeline Logic and Integration: AWS Sagemaker services provide a flexible data pipeline that integrates event data for downstream analytics, ranging from BI dashboards to machine learning models, enables data quality checks, and data enrichment, as well as drift detection and automated retraining alerts and triggers.
- Real-time Stream Processing: Unified edge sensors and centralized systems use real-time stream processing, employing AI for real-time decision-making and event predictions.
- Multi-Tenancy and Data Security: A secure multi-tenant application platform allows multiple customers and groups to share the same platform. It also provides the opportunity for future AI algorithms to provide intrusion detection, access control, and threat analysis.
- Automation and Orchestration: Kubernetes provided a platform for deployment, scaling, and orchestration, enabling AI to optimize resource allocation based on predicted workloads.
Transitioned to a future-proof technology strategy, GE Digital can now scale and manage IoT data integration effectively and enjoy continuous, rapid introduction of new features and improvements, optimizing software changes with high velocity and confidence.
<div class="case__txt--cols"><div><h4>Real-time Insights</h4><p>Achieve faster access to data, providing actionable insights across physical plants, customers, and platform partners.</p></div><div><h4>Operational Efficiency</h4><p>Automate IIoT workload deployment, reducing onboarding time for new industrial customers.</p></div><div><h4>Data Security</h4><p>Ensure data privacy and security in multi-tenant environments, giving confidence to industry competitors sharing the platform.</p></div></div>