O'Reilly Media - Ideas
NOVEMBER 19, 2024
Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost.
Netflix Tech
MARCH 28, 2025
By Ko-Jen Hsiao , Yesu Feng and Sudarshan Lamkhede Motivation Netflixs personalized recommender system is a complex system, boasting a variety of specialized machine learned models each catering to distinct needs including Continue Watching and Todays Top Picks for You.
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AWS Machine Learning - AI
APRIL 10, 2025
Amazon SageMaker HyperPod resilient training infrastructure SageMaker HyperPod is a compute environment optimized for large-scale frontier model training. Before AWS, Anoop held several leadership roles at startups and large corporations, primarily focusing on silicon and system architecture of AI infrastructure.
CIO
AUGUST 19, 2024
In such systems, multiple agents execute tasks intended to achieve an overarching goal, such as automating payroll, HR processes, and even software development, based on text, images, audio, and video from large language models (LLMs). A similar approach to infrastructure can help.
AWS Machine Learning - AI
MARCH 13, 2025
Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven architecture and ways of implementing.
Xebia
DECEMBER 13, 2024
Retrieval-Augmented Generation (RAG) is a key technique powering more broad and trustworthy application of large language models (LLMs). By integrating external knowledge sources, RAG addresses limitations of LLMs, such as outdated knowledge and hallucinated responses.
AWS Machine Learning - AI
SEPTEMBER 6, 2024
Generative artificial intelligence (AI) can be vital for marketing because it enables the creation of personalized content and optimizes ad targeting with predictive analytics. LLMs don’t have straightforward automatic evaluation techniques. Therefore, human evaluation was required for insights generated by the LLM.
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