Another year has come and gone, and still, almost every IIoT use case in manufacturing requires some sort of compute capability near the source of the data in order to solve some of the toughest challenges in Manufacturing Digital Transformation.
But yet, the currently dominant model for Industrial IoT is the Cloud-Based Platform-As-A-Service.
The issue is, while Edge Computing architectures do provide immense power and capabilities such as system resilience through delegation of computational workloads to autonomous IIoT devices in Distributed Edge Computing, it brings with it implementation complexity in manufacturing enterprises.
So, to provide you with practical guidance on Edge Computing, Architectures, and the building blocks necessary for an Edge Computing implementation in manufacturing, I invited Dominik Pilat, who is the Vice President of Customer Support & Field CTO at Hivecell, and John Kalfayan who is the Vice President of Energy, also at Hivecell.
Hivecell is a complete Edge-As-A-Service solution that allows companies to process vast amounts of raw data from smart machines and IoT Devices in real-time, at the Edge. It is both a hardware and software solution that supports the most widely used platforms today such as Kubernetes and Apache Kafka.
Outline
✔️ Key Drivers for Deployment of Compute Capabilities at the Industrial Edge
✔️ Industrial IoT Edge Computing Technology Stack
✔️ Characteristics of Distributed Edge Computing Model for IIoT
✔️ Management and Monitoring of Edge Deployed Software
✔️ Data Governance in Industrial Edge Computing
✔️ Apache Kafka Deployment at The Edge for IIoT
✔️ How Edge Compute Enables AI at the Industrial Edge
✔️ Hardware for Running AI Applications at the Edge
✔️ Practical Use Case of Industrial Edge Computing and AI
✔️ Hivecell Edge As A Services Solution
I wish you all a prosperous 2022.
Ep 23 : Digital Transformation Through Digital Twins - Dr. PG Madhavan (IoT Digital Twin, Causality, Data Science) )
Ep 22 : Building Scalable and Secure IIoT Solutions Using Open Source - Jeremy Theocharis (Co-Founder & CTO - United Manufactruing Hub) )
Ep 21: Edge AI: Artificial Intelligence and Distributed Computing at The Industrial Edge - Angelo Corsaro (CEO & CTO - ZettaScale Technology )
Ep 20: OPC UA and Time-Sensitive Networking for Field Level Communication - Bhagath Singh Karunakaran (CEO, Founder - Kalycito )
Ep 19: Application of Web 3 in Industrial IoT & Edge Computing - Rex St. John ( Jetson Software Ecosystem - NVIDIA )
Ep 17: AutomationML, OPC UA & Asset Administration Shell for Industry4.0 - Dr. Miriam Schleipen ( Chief Research Officer, EKS InTec GmbH )
Ep 16: Application of Artificial Intelligence in Process Industries - Simon Rogers ( Digital Transformation Consultant, Yokogawa )
Ep 15: Edge Analytics and Intelligent Automation for Industrial IoT - Martin Thunman ( CEO & Co-Founder, Crosser )
Ep 14: IIoT Semantic interoperability with MTConnect - Russell Waddell ( Managing Director, MTConnect Institute )
Ep 13: Time-Series Databases for IIoT [ InfluxDB ] - Brian Gilmore ( Product Manager for IoT, InfluxData )
Ep 12: Embedded Machine Learning for IIoT - Zin Kyaw ( Senior User Success Engineer, Edge Impulse )
Ep 11: Global Industry Standards for Industrial IoT - Claude Baudoin ( cébé, OMG, IIC, Cutter )
Ep 10: Open Source Software for Industrial IoT - Frédéric Desbiens ( Program Manager - IoT and Edge Computing, Eclipse )
Ep 09: Industrial Internet of Things (IoT) 101 - Benson Hougland ( VP of Product Strategy, Opto22 )
Ep 08: Technical Foundations of IoT - Dominik Obermaier ( Co-Founder & CTO, HiveMQ )
Ep 07: Kafka for Industrial IoT Streaming - Kai Waehner ( Field CTO & Global Technology Advisor, Confluent )
Ep 06: Foundations of Industrial IoT Data Architecture - John Harrington ( Co-Founder, HighByte )
Ep 05: Advanced Plant-Floor Data Analytics - Marcos Taccolini ( Founder and CTO, Tatsoft )
Ep 04: A Practical Guide to IIoT Connectivity - Stan Schneider ( CEO, Real-Time Innovations )
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