Artificial Inteligence Machine Learning Testing 
InfoQ Culture Methods
JANUARY 23, 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.
Xebia
APRIL 2, 2024
After months of crunching data, plotting distributions, and testing out various machine learning algorithms you have finally proven to your stakeholders that your model can deliver business value. Serving a model cannot be too hard, right? Glossary Throughout this post some definitions will recur.
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CIO
JULY 24, 2024
Large language models (LLMs) are hard to beat when it comes to instantly parsing reams of publicly available data to generate responses to general knowledge queries. The key to this approach is developing a solid data foundation to support the GenAI model.
InfoWorld
FEBRUARY 15, 2024
Machine learning (ML) is becoming an increasingly important part of the modern application stack. Whether it’s large-scale, public large language models (LLM) like GPT or small-scale, private models trained on company content, developers need to find ways of including those models in their code.
Speaker: Eran Kinsbruner, Best-Selling Author, TechBeacon Top 30 Test Automation Leader & the Chief Evangelist and Senior Director at Perforce Software
While advancements in software development and testing have come a long way, there is still room for improvement. With new AI and ML algorithms spanning development, code reviews, unit testing, test authoring, and AIOps, teams can boost their productivity and deliver better software faster.
TechCrunch
DECEMBER 9, 2020
At a time when more companies are building machine learning models, Arthur.ai wants to help by ensuring the model accuracy doesn’t begin slipping over time, thereby losing its ability to precisely measure what it was supposed to. AWS announces SageMaker Clarify to help reduce bias in machine learning models.
InfoQ Culture Methods
MARCH 7, 2024
When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of weighted data points that form the implementation.
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