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10 posts tagged with "computer vision"

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Performance comparison: ReductStore Vs. Minio

· 7 min read
Alexey Timin
Software Engineer - Database, Rust, C++

In this article, we will compare two data storage solutions: ReductStore and Minio. Both offer on-premise blob storage, but they approach it differently. Minio provides traditional S3-like blob storage, while ReductStore is a time series database designed to store a history of blob data. We will focus on their application in scenarios that require storage and access to a history of unstructured data. This includes images from a computer vision camera, vibration sensor data, or binary packages common in industrial data.

Handling Historical Data

S3-like blob storage is commonly used to store data of different formats and sizes in the cloud or internal storage. It can also accommodate historical data as a series of blobs. A simple approach is to create a folder for each data source and save objects with timestamps in their names:

bucket
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Open-Source Alternatives to Landing AI

· 7 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

Photo by Luke Southern

Photo by Luke Southern

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.

Implementing AI for Real-Time Anomaly Detection in Images

· 8 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

Photo by Randy FathPhoto 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.