Technology posts

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.

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.

In the world of Industry 4.0 and distributed infrastructures, real-time data processing is key to efficient decision-making. Splunk, a leading analytics platform, now integrates with Barbara's edge orchestration platform, enabling deployment on edge nodes and combining its capabilities with artificial intelligence (AI) models to maximize its potential.

In edge computing, system reliability is crucial. Barbara's Alert Manager ensures proactive monitoring and alerts to address issues before they escalate. Fully integrated into the Barbara Ecosystem, the Alert Manager enhances your operational efficiency and minimizes downtime.‍ Discover more in the following article.

The MING stack empowers edge computing by combining efficient data handling, automation, and visualization, enabling organizations to unlock the full potential of IoT and real-time applications in distributed environments. In this article we explore how the MING Stack works at the Edge and can be easily deployed using Barbara.

By 2025, analysts predict that 50% of enterprises will have adopted edge computing, up from just 20% in 2024. At Barbara, we’ve seen this momentum firsthand, with inquiries about Edge Computing and AI increasing fourfold in 2024 alone. While the potential of Edge AI is undeniable, its widespread adoption brings both opportunities and challenges. In this post, we dive into key predictions for Edge AI in 2025 and examine the challenges the industry must overcome to unlock its full potential.