In the thriving world of IoT, integrating MLOps for Edge AI is important for creating intelligent, autonomous devices that are not only efficient but also trustworthy and manageable.

MLOps—or Machine Learning Operations—is a multidisciplinary field that mixes machine learning, data engineering, and DevOps to streamline the lifecycle of AI models.

In this field, important factors to consider are:

• explainability, ensuring that decisions made by AI are interpretable by humans;

• orchestration, which involves managing the various components of machine learning in production–at scale; and

• reproducibility, guaranteeing consistent results across different environments or experiments.

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.