How to Read, Write, Display Images in OpenCV and Converting Color Spaces

When working with images, some of the most basic operations that are essential to get a grip on include reading the images from disk, displaying them, accessing their pixel values, and converting them from one color space to another. This tutorial explains these basic operations, starting first with a description of how a digital image …

A Gentle Introduction to OpenCV: An Open Source Library for Computer Vision and Machine Learning

If you are interested in working with images and video and would like to introduce machine learning into your computer vision applications, then OpenCV is a library that you will need to get hold of.  OpenCV is a huge open source library that can interface with various programming languages, including Python, and which is extensively …

How to choose the best AI platform

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.  AI plays a pivotal role as a catalyst in …

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Answering billions of reporting queries each day with low latency

Posted by Jagan Sankaranarayanan, Senior Staff Software Engineer, and Indrajit Roy, Head of Napa Product, Google Google Ads infrastructure runs on an internal data warehouse called Napa. Billions of reporting queries, which power critical dashboards used by advertising clients to measure campaign performance, run on tables stored in Napa. These tables contain records of ads …

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML …