Founded in 2011 by Technion graduates Gal Shaul and Saar Yoskovitz, Augury launched as a cloud-based solution deploying IoT devices connected to manufacturing machines worldwide. The founders realized that if people could hear machine problems, machines could be taught to hear them too. These devices continuously send data to the cloud where machine learning algorithms analyze it, providing immediate insights to customers about machine health and performance.
Augury needed to rebuild its IoT platform to scale for enterprise customers while handling massive volumes of real-time data from manufacturing machines. The company required a stable cloud solution that could offer superior scalability and a broad range of IoT technologies. They also needed to improve the flow between research, development, and production environments to deliver faster insights to customers.
Augury completed a full migration to Google Cloud, moving all microservices to Google Kubernetes Engine (GKE) with DoiT as implementation partner. The company leveraged Google Cloud IoT Core, Cloud Dataflow, BigQuery, and Cloud Pub/Sub to process telemetry from IoT devices on factory floors. GKE's autoscaling features ensured balanced algorithm processing and met SLA requirements even during high data volume periods.
With help of DoiT, our transition to Google Cloud was smooth and efficient. DoiT engineers were there to answer all of our questions, consulted with us on best practices for Google Cloud deployment, and helped us solve any issues that arose, quickly and efficiently.
Gal Shaul, Co-founder and CTO
Google Cloud enables Augury to use the same data for research and production environments. Working with open source pipeline technologies like Cloud Dataflow and Apache Beam allows the team to work on algorithms with an agile mindset while contributing back to the open source community. This approach enables rapid movement from research to production, delivering value quickly to customers experiencing digital transformation.
Google Cloud IoT Core, Cloud Pub/Sub, and Cloud Dataflow enable Augury to consume telemetry from IoT devices on factory floors. GKE's autoscaling features ensure solid algorithm processing latency that meets customer SLAs. Even when large facilities come back online after extended downtime, pushing huge data volumes, GKE handles the flow while maintaining balanced performance.
Augury customers receive real-time insights into machine health, with some manufacturing customers checking data hourly. The company relies on continuous data flows and analysis to provide seamless service. GKE ensures instances run in a balanced manner, allowing Augury to deliver uninterrupted insights that help customers make smarter decisions about maintenance and production health.
Understanding machine health serves as the foundation for digital transformation in manufacturing. Gaining visibility into machine health enables companies to transform their supply chain and culture to agile just-in-time manufacturing. This transformation saves millions of dollars in efficiency improvements while reducing environmental impact and enhancing human productivity across manufacturing operations.
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