In this post, you will learn about how metamorphic testing could be used for performing quality control checks/testing on Machine Learning models. It is primarily meant for data science specialists to plan the test cases to test the Machine Learning (ML) model implementation from a QA perspective.
Testing Machine Learning models from a quality assurance perspective is different from testing Machine Learning models for accuracy/performance.
This post intends to propose a technique termed as Dual Coding for testing or performing quality control checks on Machine Learning models from quality assurance (QA) perspective. This could be useful in performing black box testing of ML models.
The proposed technique is based on the principles of Dual Coding Theory (DCT) hypothesized by Allan Paivio of the University of Western Ontario in 1971. According to Dual Coding Theory, our brain uses two different systems including verbal and non-verbal/visual to the gather, process, store, and retrieve (recall) the information related to a particular subject. One of the key assumptions of dual coding theory is the connections (also termed as referential connections) that link verbal and nonverbal representations into a complex associative network. For example, let’s say we are shown flower images and also told about the name of these flowers (such as rose, lotus etc). At a later point in time, when told about one of these flowers by name, or shown one of the images, we end up classifying them as flowers. Pay attention to the fact of one of the two systems (verbal or non-verbal/visual) get activated appropriately to classify the subject (word or images) in the correct manner. The following diagram represents different representations of a dual-coding theory.
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Yesterday, I was going through different articles as usual and one site attracted my attention.
We keep hearing about new solutions for test automation and continuous testing. Such solutions aim to increase the test automation authoring as well as the maintenance associated with these tests as the product evolves.
With this trend, many software quality engineers, SDET, and test automation architects are asking themselves whether their job is at risk and what the future holds for them.
Previously, we saw some of the very basic image analysis operations in Python. In this last part of basic image analysis, we’ll go through some of the following contents.
The following contents are the reflection of my completed academic image processing course in the previous term. So, I am not planning on putting anything into the production sphere. Instead, the aim of this article is to try and realize the fundamentals of a few basic image processing techniques. For this reason, I am going to stick to using SciKit-Image – numpy mainly to perform most of the manipulations, although I will use other libraries now and then rather than using most wanted tools like OpenCV
Factories have witnessed a sea change in the past three decades. The 80s and 90s witnessed Industrial Automation and robots came to the forefront. During the past decade, multiple game-changing technologies are reshaping the factories. Machine Learning, Internet of Things (IoT), Big Data, Virtual Reality (VR), and Artificial Intelligence (AI) are fundamentally alerting the way factories work. Their impact is not limited to manufacturing, they are influencing almost every industry. This article tries to explain Machine Learning and its significance in the world of manufacturing.
First, let’s try and explain Machine Learning. Simply put, it refers to algorithms improving on their own. Normally, when a program is written, it is expected to deliver a prespecified output for a given set of inputs. Over time, they identify patterns and “learn” to generate an output corresponding to a different set of inputs. In cases, algorithms learn to respond to new situations, e.g. algorithms on the trading floor “learn” to respond to different market situations. Machine Learning can be implemented through Decision Tree Learning, Association Rule Learning, Artificial Neural Networks etc.
In this article, we will introduce you to Machine Learning with Python. Moreover, we will discuss Python Machine Learning tasks, steps, and applications. Then, we will take a look at 10 tech giants that adopt Python Machine Learning to improve what they do.
So, let’s begin!
We have this funny little tagline about how we’re pushing the boundaries of test automation. It’s a simple enough thing when you say it, but what do we really mean by it?
Recently, we were recognized by several industry analysts for the work we’ve been doing pushing those boundaries. At voke, they said, "Parasoft is a company borne of innovation with a relentless focus on software quality," and Forrester said, " Regarding AI, Parasoft has an impressive and concrete roadmap to increase test automation from design to execution, pushing autonomous testing."
In this post, you will learn about the definition of quality of AI/Machine Learning (ML) models. Getting a good understanding of what is the high and low quality of AI models would help you design quality control checks for testing Machine Learning models and related quality assurance (QA) practices. This post would be a good read for QA professionals in general. However, it would also help set perspectives for data scientists and Machine Learning experts.
The following are some of the key quality traits that are described in detail for assessing the quality of AI models:
Primarily, Machine Learning is the part of Artificial Intelligence that brings the computer systems a greater ability to enhance and study automatically from experience. Over the past few years, it has been creating very serious waves. Very recently, the applications of smartphones and other small-screen experiences have started to take shape that drives millions of interactions with their mobile devices. More importantly, the Machine Learning platform can make your smartphone very smarter by just increasing a host of processes as well as functions instantly. In reality, many smartphones are already using some kind of Machine Learning or intelligent automation application, which helps mobile phones in becoming more effective and efficient as well.
Overall, the businesses are ramping up their Machine Learning investment. Traditionally, the Machine Learning needs a fabulous quantity of power in which the mobile devices simply did not have. Still now, most of the businesses can install the special chips in automobiles, drones, and also in smartphones, which enables them to consume 90% less power. In the end, these mobile devices, even without an online connection, can do a wide array of complex tasks that include:
