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In the fast-paced world of AI, it can be challenging to pause and reflect on how we work. Yet this reflection is the cornerstone of continuous improvement. The 2025 DORA survey offers a unique opportunity for you and your team to do just that. By taking just 10-15 minutes to participate before July 18, you …
Read more “Get better at getting better: Take the 2025 DORA survey”
The rapid progress of foundation models and large language models (LLMs) has fueled significantly improvement in the capabilities of machine learning systems that benefit from mutlimodal input data. However, existing multimodal models are predominantly built on top of pre-trained LLMs, which can limit accurate modeling of temporal dependencies across other modalities and thus limit the …
Read more “Visatronic: A Multimodal Decoder-Only Model for Speech Synthesis”
We design and implement AXLearn, a production deep learning system that facilitates scalable and high-performance training of large deep learning models. Compared to other state-of-art deep learning systems, AXLearn has a unique focus on modularity and support for heterogeneous hardware infrastructure. AXLearn’s internal interfaces between software components follow strict encapsulation, allowing different components to be …
Read more “AXLearn: Modular Large Model Training on Heterogeneous Infrastructure”
This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives. We also address the three fundamental aspects of LLM development: the core lifecycle stages, the spectrum of fine-tuning methodologies, …
Read more “Advanced fine-tuning methods on Amazon SageMaker AI”
Editor’s note: The Jina AI Reader is a specialized tool that transforms raw web content from URLs or local files into a clean, structured, and LLM-friendly format. In this post, Han Xiao details how Cloud Run empowers Jina AI to build a secure, reliable, and massively scalable web scraping system that remains economically viable. This …
Read more “How Jina AI built its 100-billion-token web grounding system with Cloud Run GPUs”
Ceramics — the humble mix of earth, fire and artistry — have been part of a global conversation for millennia. From Tang Dynasty trade routes to Renaissance palaces, from museum vitrines to high-stakes auction floors, they’ve carried culture across borders, evolving into status symbols, commodities and pieces of contested history. Their value has been shaped …
Read more “A Gaming GPU Helps Crack the Code on a Thousand-Year Cultural Conversation”
By Eugene Yemelyanau, Jake Grice Introduction Tudum.com is Netflix’s official fan destination, enabling fans to dive deeper into their favorite Netflix shows and movies. Tudum offers exclusive first-looks, behind-the-scenes content, talent interviews, live events, guides, and interactive experiences. “Tudum” is named after the sonic ID you hear when pressing play on a Netflix show or movie. …
Read more “Netflix Tudum Architecture: from CQRS with Kafka to CQRS with RAW Hollow”
As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds of thousands of customers use the fully managed infrastructure, tools, and workflows of Amazon SageMaker AI to scale and advance AI model development. Since launching …
This paper was accepted at the Workshop on Reliable and Responsible Foundation Models (RRFMs) Workshop at ICML 2025. Uncertainty quantification plays a pivotal role when bringing large language models (LLMs) to end-users. Its primary goal is that an LLM should indicate when it is unsure about an answer it gives. While this has been revealed …
Read more “Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?”
Imagine a system that can explore multiple approaches to complex problems, drawing on its understanding of vast amounts of data, from scientific datasets to source code to business documents, and reasoning through the possibilities in real time. This lightning-fast reasoning isn’t waiting on the horizon. It’s happening today in our customers’ AI production environments. The …