InfoQ Homepage Artificial Intelligence Content on InfoQ
-
/articles/software-testing-ai-ml-2023/en/smallimage/logo-1674221439156.jpg)
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.
-
/articles/infoq-software-trends-report-2022/en/smallimage/InfoQ-Trend-Report-logo-small-1672911593669.jpg)
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.
-
/articles/apache-dolphinscheduler-mlops/en/smallimage/logo-1665586844807.jpg)
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.
-
/articles/auto-ml-promise-reality/en/smallimage/logo-1661333792384.jpg)
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.
-
/articles/ai-ml-data-engineering-trends-2022/en/smallimage/InfoQ-Trends-Report-1659439712027.jpeg)
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.
-
/articles/mlops-deploying-ml-production/en/smallimage/logo-1654663703953.jpg)
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.
-
/articles/ai-for-software-developers/en/smallimage/logo-1652883200735.jpg)
AI for Software Developers: a Future or a New Reality?
In this article, author Nikita Povarov discusses the role AI/ML plays in software development and how tasks like code completion, code search, and bug detection can be powered by machine learning. But he also explains why a complete replacement of programmers by algorithms isn't going happen any time soon.
-
/articles/summary-podcast-2021-review/en/smallimage/InfoQ-Trend-Report-logo-1642446751680.jpg)
The Major Software Industry Trends from 2021 and What to Watch in 2022
In this podcast summary Thomas Betts, Wes Reisz, Shane Hastie, Charles Humble, Srini Penchikala, and Daniel Bryant discuss what they have seen in 2021 and speculate a little on what they hope to see in 2022. Topics explored included: hybrid working and the importance of ethics and sustainability within technology.
-
/articles/waste-impediments-data-science/en/smallimage/logo-1641428353252.jpg)
Getting Rid of Wastes and Impediments in Software Development Using Data Science
This article presents how to use data science to detect wastes and impediments, and concepts and related information that help teams to figure out the root cause of impediments they struggle to get rid of. The knowledge discovered during research includes an expanded waste classification, and the use of trends to uncover undesired situations like hidden delayed backlog items and defects trends.
-
/articles/deep-learning-institutional-incremental-learning/en/smallimage/institutional-incremental-learning-small-image-v2-1640874831613.jpg)
Developing Deep Learning Systems Using Institutional Incremental Learning
Institutional incremental learning promises to achieve collaborative learning. This form of learning can address data sharing and security issues, without bringing in the complexities of federated learning. This article talks about practical approaches which help in building an object detection system.
-
/articles/ai-devops-takeover/en/smallimage/logo-1633456324591.jpg)
Is Artificial Intelligence Taking over DevOps?
AI tools are slowly replacing the role of the developer – just as DevOps did before – and will eventually supplant DevOps entirely. Assessing whether that prediction is true is tricky. In this article, we’ll look at what AI promises for the development process, assess whether it can really ever take over from human developers, and what DevOps is likely to look like in a decades’ time.
-
/articles/ai-ml-data-engineering-trends-2021/en/smallimage/InfoQ-Trend-Report-logo-1628263771435.jpeg)
AI, ML and Data Engineering InfoQ Trends Report - August 2021
How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.
SPONSORED BY
Live Webinar presented by Diffblue |
Q&A moderated by InfoQ
ChatGPT is fun, but the future is fully autonomous AI for code
ChatGPT and AI tools based on Large Language Model have recently grabbed headlines. But LLMs are not the only way to do AI for code: fully autonomous code-writing is possible by using reinforcement learning to write code that can compile, run and be correct. Learn more in this webinar.