Purpose: Is used to train the machine learning model. Function: Think of it as the study material for the model. It provides examples and patterns for the model to learn from and build its internal ...
Artificial intelligence (AI) models—specifically, generative AI (GenAI) models—are becoming increasingly relevant for today’s businesses, yet many questions remain about how such models work and how ...
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...
AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...
Testing Machine Learning: Insight and Experience from Using Simulators to Test Trained Functionality
A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Securing AI pipelines against data poisoning: a practical guide for technical teams Data poisoning is one of the more practical risks in AI security because it targets the pipeline rather than the ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to perform binary classification (predicting a variable with two possible discrete values) using ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results