CIO
MARCH 11, 2025
But how do companies decide which large language model (LLM) is right for them? LLM benchmarks could be the answer. They provide a yardstick that helps user companies better evaluate and classify the major language models. LLM benchmarks are the measuring instrument of the AI world.
CIO
DECEMBER 5, 2024
Many organizations have launched dozens of AI proof-of-concept projects only to see a huge percentage fail, in part because CIOs don’t know whether the POCs are meeting key metrics, according to research firm IDC. Many POCs appear to lack clear objections and metrics, he says. The customer really liked the results,” he says.
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Openxcell
JANUARY 20, 2025
LLM or large language models are deep learning models trained on vast amounts of linguistic data so they understand and respond in natural language (human-like texts). These encoders and decoders help the LLM model contextualize the input data and, based on that, generate appropriate responses.
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.
Speaker: Dr. Greg Loughnane and Chris Alexiuk
Register today to save your seat! December 6th, 2023 at 11:00am PST, 2:00pm EST, 7:pm GMT
CIO
APRIL 12, 2023
Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence. The underpinning architecture needs to include event-streaming technology, high-performing databases, and machine learning feature stores.
AWS Machine Learning - AI
FEBRUARY 12, 2025
Large language models (LLMs) have revolutionized the field of natural language processing with their ability to understand and generate humanlike text. Researchers developed Medusa , a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously.
Advertiser: Data Robot
The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models. How to successfully navigate the bias versus accuracy trade-off for final model selection and much more.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation metrics for at-scale production guardrails.
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