Machine Learning at the Edge with Scikit-Learn, Keras, BentoML, and HiveMQ
Explore how to run machine learning models at the edge with Scikit-Learn, Keras, BentoML, & HiveMQ using a manufacturing example on predictive quality.
Enable interoperability between OT and IT systems by translating diverse protocols into the standardized MQTT format, modernizing IIoT infrastructure and making seamless edge-to-cloud integration a reality.
HiveMQ Edge is a software-based edge MQTT gateway and protocol converter that helps organizations bridge the OT/IT divide. It helps organize data into a Unified Namespace, making managing and streaming data across your infrastructure easier.
Broker: Edge-optimized, fully standards-compliant MQTT broker. Incorporates an MQTT-SN protocol for constrained devices.
MQTT Bridge: Bidirectional MQTT bridge functionality to connect to enterprise MQTT brokers.
Protocol Adapter: Supports diverse industrial communication and automation protocols, such as OPC-UA, Modbus, Siemens S7, Beckhoff ADS.
Custom Adapter: Build custom protocol adapters using our SDK from our open-source repository.
Data Hub: Integrated policy and data transformation engine that validates, enforces, and manipulates data in motion at the edge, ensuring optimal data quality for your IT systems.
Offline Buffering: In case of an MQTT bridge connection failure, messages are stored on disk and published once the connection is restored.
Download the latest HiveMQ Edge binary package
Access the open source project at https://github.com/hivemq/hivemq-edge
HiveMQ Edge is ready to go, but you will need a trial license to try the commercial features.
HiveMQ Edge is available as a single binary with all the open source features. The commercial license key enables additional functionality.
Introduction to HiveMQ Edge
This course will introduce participants to the core concepts of HiveMQ Edge, focusing on its role in consolidating and democratizing data across diverse industrial landscapes.
Edge-Optimized Broker
MQTT Bridge
Protocol Adapters
Custom Adapters
Data Hub
Offline Buffering
Enterprise Support
Specifically designed for edge deployments, and when paired with the HiveMQ platform, it creates a single, unified data integration solution that spans from edge to cloud.
Powerful plug-and-play integrations with legacy OT protocols like Modbus and OPC-UA, and automation protocols like Siemens S7 and Beckhoff ADS. Or build your own.
With HiveMQ Data Hub, ensure data quality standards are centrally defined and enforced across all devices and messages, even at the edge.
Support for offline buffering (store and forward), which will queue and later publish messages in case of a connection failure with zero data loss.
Enable seamless data integration to a centralized data hub, capitalize on readily accessible ISA-95 profiles, and mitigate errors.
Workspace is our observability tool, an interactive canvas displaying all connected elements at the edge to help you identify what connections you have active at the edge and where messages are being sent.
HiveMQ Edge also features HiveMQ Data Hub, which can enhance the value of your IoT data. It is an integrated policy and transformation engine that validates, enforces, and manipulates data in motion to ensure data integrity and quality across your MQTT deployment from edge to cloud.
Explore how to run machine learning models at the edge with Scikit-Learn, Keras, BentoML, & HiveMQ using a manufacturing example on predictive quality.
Explore why use open standards, like MQTT, and specifications, like Sparkplug, at the Industrial Edge to manage data.
A guide to build a file-based protocol adapter for HiveMQ Edge that reads content from a file and publishes it as base64 encoded JSON format.
Discover how Lumo (https://lumo.ag) revolutionized smart agriculture to save growers time, money, and water by leveraging MQTT and HiveMQ.
A webinar about how solid data management is crucial for AI success in Manufacturing. Gain insights for optimizing your data infrastructure.
Discover MQTT-SN, a specialized IoT protocol for embedded devices on non-TCP/IP networks, designed to save power and scale in Industrial IoT.