Deploying machine learning (ML) models into production can often be a complex and resource-intensive task, especially for customers without deep ML and DevOps expertise. Amazon SageMaker Canvas simplifies model building by offering a no-code interface, so you can create highly accurate ML models using your existing data sources and withoutContinue Reading
Amazon Nova Sonic is a foundation model that creates natural, human-like speech-to-speech conversations for generative AI applications, allowing users to interact with AI through voice in real-time, with capabilities for understanding tone, enabling natural flow, and performing actions. Multi-agent architecture offers a modular, robust, and scalable design pattern for production-levelContinue Reading
Large-scale AI model training faces significant challenges with failure recovery and monitoring. Traditional training requires complete job restarts when even a single training process fails, resulting in additional downtime and increased costs. As training clusters expand, identifying and resolving critical issues like stalled GPUs and numerical instabilities typically requires complexContinue Reading
Generative AI is rapidly reshaping the music industry, empowering creators—regardless of skill—to create studio-quality tracks with foundation models (FMs) that personalize compositions in real time. As demand for unique, instantly generated content grows and creators seek smarter, faster tools, Splash Music collaborated with AWS to develop and scale music generationContinue Reading
This post is co-written with Ross Ashworth at TP ICAP. The ability to quickly extract insights from customer relationship management systems (CRMs) and vast amounts of meeting notes can mean the difference between seizing opportunities and missing them entirely. TP ICAP faced this challenge, having thousands of vendor meeting recordsContinue Reading
This guest post was written by Mulay Ahmed and Caroline Lima-Lane of Principal Financial Group. The content and opinions in this post are those of the third-party authors and AWS is not responsible for the content or accuracy of this post. With US contact centers that handle millions of customerContinue Reading
Choosing the right large language model (LLM) for your use case is becoming both increasingly challenging and essential. Many teams rely on one-time (ad hoc) evaluations based on limited samples from trending models, essentially judging quality on “vibes” alone. This approach involves experimenting with a model’s responses and forming subjectiveContinue Reading
Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and hoping the results meet expectations without the ability to make incremental adjustments. Single-shot fine-tuning frequently leads to suboptimal results and requires starting the entire processContinue Reading
Multimodal fine-tuning represents a powerful approach for customizing vision large language models (LLMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific content, or output formatting requirements. Fine-tuningContinue Reading
Artificial Intelligence (AI) is transforming the quick-service restaurant industry, particularly in drive-thru operations where efficiency and customer satisfaction intersect. Traditional systems create significant obstacles in service delivery, from staffing limitations and order accuracy issues to inconsistent customer experiences across locations. These challenges, combined with rising labor costs and demand fluctuations,Continue Reading
