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All posts tagged Edge MLOps

Two engineers pointing at a monitor showing a data visualization and code in a server room

Industry at the Edge

September 17, 2026

Alex Cantos

min

Agentic AI for Dummies

Most conversations about Agentic AI in industry start three levels too high: autonomous factories, agents talking to other agents. This is the small version that actually works, five building blocks that let an operator ask the plant a question and get a real answer.

Weathered gravestone with red flowers on top in a grassy cemetery with bare trees

Industry at the Edge

July 22, 2026

Alex Cantos

min

Requiem for the AI Model

Everyone is chasing generative AI and autonomous agents, but the industrial AI that already delivers, predictive maintenance and computer vision, never left the factory floor. A field reality check on hype versus what actually works, and why the best engineering starts with the problem, not the technology.

Aerial view of two circular water treatment clarifier tanks with the text Edge MLOps

Industry at the Edge

May 6, 2025

Barbara

min

Edge MLOps with Barbara: Operationalizing AI at the Edge

As industries seek faster insights and real-time responsiveness, Edge MLOps (Machine Learning Operations at the Edge) is emerging as a game-changer. Unlike traditional MLOps that rely on centralized cloud infrastructure, Edge MLOps enables machine learning models to be deployed, monitored, and retrained close to the data source.

Barbara graphic over solar panels with the text MLOps at the Edge: Predicting Solar Radiation in Photovoltaic Plants

Industry at the Edge

May 5, 2025

Enrique Ramírez

min

MLOps at the Edge: Predicting Solar Radiation in Photovoltaic Plants

This article takes you inside a real-world deployment where MLOps at the edge powers hourly solar radiation forecasts in a photovoltaic plant. By combining AI with real-time data flows, and cloud integration, the solution delivers higher prediction accuracy, faster response times, and optimized plant performance. Discover how Edge MLOps is transforming solar forecasting.

Barbara graphic with an Azure Ingester app screenshot and the text Optimizing Industrial Efficiency with Barbara and Azure IoT

Partnerships

March 3, 2025

Enrique Ramírez

min

Optimizing Industrial Efficiency with Barbara and Azure IoT

The convergence of Edge AI and cloud computing is revolutionizing industrial operations. By integrating Barbara’s Edge AI platform with Azure IoT, businesses can achieve real-time data processing, enhanced security, and scalable automation. A key enabler of this seamless integration is Azure Ingester, available now in the Barbara Marketplace. Discover more in the following article.

Barbara graphic with an Ollama llama icon and the text Deploying LLaMA in Industrial Settings

Partnerships

February 16, 2025

Enrique Ramírez

min

Deploying LLaMA in Industrial Settings with Barbara

LLaMA (Large Language Model Meta AI) is a family of efficient, open-source language models developed by Meta. Engineered for lightweight performance, LLaMA delivers powerful natural language processing capabilities while optimizing for scalability and efficiency. In this article, we explore how easily LLaMA can be deployed at the Edge with Barbara.

Close-up of wet cement pouring from an orange mixer chute into a grey container

News

January 13, 2025

Barbara

min

Edge AI in Cement: Deploying Real-Time Predictive Models for Fuel Savings in Clinker Kilns

By implementing Barbara's Cybersecure Edge Computing platform, Argos achieved real-time operational control, significant cost reductions, and unlocked scalable solutions across its facilities. Key results included $200,000 in annual fuel savings per plant, a 60% reduction in bandwidth costs, and the ability to deploy new applications on its existing Edge infrastructure. Explore the full story here.

Architecture diagram of an edge node with OPC UA, MQTT, model serving, InfluxDB and Grafana connected to Azure Cloud

Industry at the Edge

November 4, 2024

Jaime Vélez

min

How to Deploy Models in Multiple Locations?

Deploying machine learning models across multiple locations is becoming critical for scaling AI. Whether you're building infrastructure or serving diverse clients, this guide covers key strategies, challenges, and best practices for successful multi-site model deployment.

Barbara and Acciona logos with the text Customer Story: Deploying Adaptive AI in Distributed Water Plants

Industry at the Edge

June 10, 2024

Barbara

min

Optimizing Chemical Usage in Water Plants Through Machine Learning at the Edge

By implementing real-time optimized Machine Learning algorithms at each of its desalination plants, ACCIONA a global infrastructure operator, managed to minimize the use of reactive chemicals, eliminate associated regulatory penalties, and leverage an edge infrastructure to implement new predictive applications for water quality control. In this article, we explore the intricacies of the project.

Edge MLOps

All posts tagged Edge MLOps

Agentic AI for Dummies

Requiem for the AI Model

Edge MLOps with Barbara: Operationalizing AI at the Edge

MLOps at the Edge: Predicting Solar Radiation in Photovoltaic Plants

Optimizing Industrial Efficiency with Barbara and Azure IoT

Deploying LLaMA in Industrial Settings with Barbara

Edge AI in Cement: Deploying Real-Time Predictive Models for Fuel Savings in Clinker Kilns

How to Deploy Models in Multiple Locations?

Optimizing Chemical Usage in Water Plants Through Machine Learning at the Edge

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