Edge AI
What Is Physical AI? Where Cloud-Based LLMs Meet the Physical World
Artificial Intelligence is moving out of the browser and into the physical world. Over the past few years, Large Language Models (LLMs) transformed how…
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How is MQTT Used at the Edge? Deploying Lightweight Messaging at Scale
As data volumes continue to grow at the device edge, sending all data to the cloud is no longer feasible. Increasingly large amounts of…
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Edge Computing: 5 Years of Navigating a Category In the Making
The first five years of Avassa have been anything but predictable, because the edge computing market hasn’t been very predictable itself. When we started…
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Rethinking the Cloud-First Mandate: Why Modern Enterprises Are Rebalancing Towards On-Prem and Edge
This article is cowritten by Stefan Wallin with Avassa and Cristian Klein with Elastisys. From an information security perspective, 2025 started rough. First, we…
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What is Edge AI? Key Benefits & Why You Should Use It
Edge AI is a term we hear increasingly often within the category of edge computing. In this article, we’ll look closer at the definition…
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A Need For Speed, Without the Cost: Avassa and Plainsight’s Collaboration
This is a guest post by Kit Merker, CEO of Plainsight. Industrial automation systems are constantly seeking ways to streamline processes, reduce costs, and…
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Optimizing Edge AI: Combining MLOps and Edge Orchestration for Success
Deploying AI models to the edge brings AI closer to real-world applications, addressing challenges such as latency, privacy, and autonomy. Edge deployments enable fast,…
Read morePackaging and deploying an ML serving system to the edge
There once was a lifecycle of a machine learning servable on the edge… The rapid uptake of applied machine learning across many tasks and industries is largely driven by how accessible and cheap the underlying technologies have become. There is a vibrant and growing set…
Read moreHow to trace Edge Applications with OpenTelemetry in the Avassa Edge Platform
Edge sites often have a set of communicating applications. An end-user transaction on the site results in a sequence of calls between the edge applications. Response times and issues vary per site. Therefore it is useful to be able to trace application calls per site.…
Read moreDeploying HiveMQ Edge in multiple sites
Edge computing and IoT have a happy marriage. IoT focuses on devices at the edge and data collection. Edge computing enables local processing of that data. There is a range of protocols that are relevant like Modbus and OPC UA. HiveMQ provides a containerized bridge…
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