Photo by Randy Fath on Unsplash

The journey of taking an open-source artificial intelligence (AI) model from a laboratory setting to real-world implementation can seem daunting. However, with the right understanding and approach, this transition becomes a manageable task.

This blog post aims to serve as a compass on this technical adventure. We'll demystify key concepts, and delve into practical steps for implementing anomaly detection models effectively in real-time scenarios.

Let's dive in and see how open-source models can be implemented in production, bridging the gap between research and practical applications.

We are pleased to announce the release of the latest minor version of ReductStore, 1.7.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

To download the latest released version, please visit our Download Page.

What's new in 1.7.0?​

ReductStore v1.7.0 introduces two new features that make it easier to provision resources and write data in batches, which can improve your performance and efficiency when using ReductStore for edge computing and AI applications.

Benchmarks don't lie, let's put the systems to the ultimate test.

ReductStore vs. MinIO & InfluxDB on Edge Device HX401

For anyone deeply immersed in the engineering world of Edge Computing, Computer Vision, or IoT, you'll want to read further to understand why a time series database for blob data is needed and where it stands out.

Enter our contest: First, we have ReductStore—a time series database for blob data—specifically designed for edge devices.

Its counterpart? The duo of MinIO and InfluxDB, each optimized for their niche in blob storage and time-series data respectively.

In a head-to-head comparison, which system really leads in performance and wins the speed race?

Let's roll up our sleeves and deep-dive into this benchmarking analysis to separate fact from fiction.