Organizations are constantly seeking ways to harness the power of advanced large language models (LLMs) to enable a wide range of applications such as text generation, summarizationquestion answering, and many others. As these models grow more powerful and capable, deploying them in production environments while optimizing performance and cost-efficiency becomesContinue Reading
This post was written by Claudiu Bota, Oleg Yurchenko, and Vladyslav Melnyk of AWS Partner Automat-it. As organizations adopt AI and machine learning (ML), they’re using these technologies to improve processes and enhance products. AI use cases include video analytics, market predictions, fraud detection, and natural language processing, all relyingContinue Reading
In production generative AI applications, responsiveness is just as important as the intelligence behind the model. Whether it’s customer service teams handling time-sensitive inquiries or developers needing instant code suggestions, every second of delay, known as latency, can have a significant impact. As businesses increasingly use large language models (LLMs)Continue Reading
The report The economic potential of generative AI: The next productivity frontier, published by McKinsey & Company, estimates that generative AI could add an equivalent of $2.6 trillion to $4.4 trillion in value to the global economy. The largest value will be added across four areas: customer operations, marketing andContinue Reading
Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. What is MLOps provides a detailed description of this concept. As ML workloads become increasingly complex and consume more energy and resources, a growing number of companies are looking for waysContinue Reading
