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Culture & Methods

How I Contributed as a Tester to a Machine Learning System: Opportunities, Challenges and Learnings

Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers themselves. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make. This is a journey of assuring quality of ML-based systems as a tester.

Shivani Gaba
on Feb 16, 2023
AI, ML & Data Engineering

Understanding and Debugging Deep Learning Models: Exploring AI Interpretability Methods

ML interpretability refers to a user's ability to explain decisions made by an ML system. Interpretability increases confidence in the model, reduces bias, and ensures that model is compliant and ethical. In this article, author Andrew Hoblitzell discusses several methods of ML interpretability and dives deep into Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Values.

Andrew Hoblitzell
on Feb 10, 2023
Culture & Methods

Software Testing, Artificial Intelligence and Machine Learning Trends in 2023

Technology has taken significant leaps within the last few years, introducing advancements that have taken us further into the digital age, impacting the software testing industry, and we're seeing advances in machine learning, artificial intelligence, and the neural networks making them possible. These new technologies will change how software is developed and tested like never before.

Adam Sandman
on Jan 23, 2023
Development

InfoQ Software Trends Report: Major Trends in 2022 and What to Watch for in 2023

2022 was another year of significant technological innovations and trends in the software industry and communities. The InfoQ podcast co-hosts met last month to discuss the major trends from 2022, and what to watch for in 2023. This article is a summary of the 2022 software trends podcast.

Daniel Bryant Wesley Reisz Thomas Betts Shane Hastie Srini Penchikala
on Jan 13, 2023
AI, ML & Data Engineering

Apache DolphinScheduler in MLOps: Create Machine Learning Workflows Quickly

In this article, author discusses data pipeline and workflow scheduler Apache DolphinScheduler and how ML tasks are performed by Apache DolphinScheduler using Jupyter and MLflow components.

Zhou Jieguang
on Oct 14, 2022
AI, ML & Data Engineering

AutoML: the Promise vs. Reality According to Practitioners

Automation to improve machine learning projects comes from a noble goal, but true end-to-end automation is not available yet. As a collection of tools, AutoML capabilities have proven value but need to be vetted more thoroughly. Findings from a qualitative study of AutoML users suggest the future of automation for ML and AI rests in the ability for us to realize the potential of AutoMLOps.

Doris Xin
on Aug 29, 2022
AI, ML & Data Engineering

Streaming-First Infrastructure for Real-Time Machine Learning

This article covers the benefits of streaming-first infrastructure for two scenarios of real-time ML: online prediction, where a model can receive a request and make predictions as soon as the request arrives, and continual learning, when machine learning models are capable of continually adapting to change in data distributions in production.

Chip Huyen
on Aug 22, 2022
AI, ML & Data Engineering

AI, ML, and Data Engineering InfoQ Trends Report—August 2022

In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.

Srini Penchikala Dr Einat Orr Rags Srinivas Roland Meertens Anthony Alford Daniel Dominguez
on Aug 02, 2022
.NET

Building Neural Networks with TensorFlow.NET

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow. In this article, the author explains how to use Tensorflow.NET to build a neural network.

Robert Krzaczyński
on Jul 11, 2022
AI, ML & Data Engineering

What You Should Know before Deploying ML in Production

What should you know before deploying machine learning projects to production? There are four aspects of Machine Learning Operations, or MLOps, that everyone should be aware of first. These can help data scientists and engineers overcome limitations in the machine learning lifecycle and actually see them as opportunities.

Francesca Lazzeri
on Jun 09, 2022
Culture & Methods

Using Machine Learning for Fast Test Feedback to Developers and Test Suite Optimization

Software testing, especially in large scale projects, is a time intensive process. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are available. The article explores optimizing test execution, saving machine resources, and reducing feedback time to developers.

Gregor Endler Marco Achtziger
on Feb 22, 2022
Mobile

InfoQ Mobile and IoT Trends Report 2022

This report summarizes the views of the InfoQ editorial team and of several practitioners from the software industry about emerging trends in a number of areas that we collectively label the mobile and IoT space. This is a rather heterogeneous space comprising devices and gadgets from smartphones to smart watches, from IoT appliances to smart glasses, voice-driven assistants, and so on.

Sergio De Simone Abhijith Krishnappa Tridib Bolar
on Feb 21, 2022