AWS Machine Learning - AI
APRIL 9, 2025
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.
CIO
NOVEMBER 19, 2024
From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. If the LLM didn’t create enough output, the agent would need to run again.
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Perficient
NOVEMBER 20, 2024
Introduction to Multiclass Text Classification with LLMs Multiclass text classification (MTC) is a natural language processing (NLP) task where text is categorized into multiple predefined categories or classes. Traditional approaches rely on training machine learning models, requiring labeled data and iterative fine-tuning.
Xebia
AUGUST 9, 2023
With the industry moving towards end-to-end ML teams to enable them to implement MLOPs practices, it is paramount to look past the model and view the entire system around your machine learning model. Table of Contents What is Machine Learning System Design?
O'Reilly Media - Data
DECEMBER 10, 2019
Roughly a year ago, we wrote “ What machine learning means for software development.” Up until now, we’ve built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction. In short, we can use machine learning to automate software development itself.
CIO
APRIL 25, 2024
During the summer of 2023, at the height of the first wave of interest in generative AI, LinkedIn began to wonder whether matching candidates with employers and making feeds more useful would be better served with the help of large language models (LLMs). We didn’t start with a very clear idea of what an LLM could do.”
AWS Machine Learning - AI
MARCH 5, 2025
These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.
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