Efficient Learning with Distilling Step-by-Step
In an era where data is abundant yet precious, a new technique (“Distilling Step-by-Step”) transforms Large Language Models (LLMs) from mere label predictors to reasoning agents that provide intermediate rationales, bridging the gap between inputs and final answers. This mechanism enables the crafting of efficient task-specific models that require less data, less computational cost, and…
Keys to a Robust Fleet of Custom LLMs
The rising popularity of Generative AI is driving companies to adopt custom large language models (LLMs) to address concerns about intellectual property, and data security and privacy. Custom LLMs can safeguard proprietary data while also meeting specific needs, delivering enhanced performance and accuracy for improved user experiences and operations. Tailoring these models to specific requirements…
2023 AI Conference
The AI Conference is taking place in San Francisco next week, September 26-27. This in-person, vendor-neutral event offers a deep dive into a vibrant AI community, featuring engaging talks and interactive sessions. We have a diverse range of sessions that cover both the breadth and depth of AI. You’ll hear about the latest research and…
2023 Ray Summit Preview
Ray Summit is an indispensable event for anyone keen on delving into Ray’s significant role in many AI and machine learning platforms. Next week’s summit will showcase talks from leading experts, hands-on workshops, and opportunities for networking. For those eager to be at the cutting edge of AI innovations and applications, missing this summit isn’t…
Ivy: Streamlining AI Model Deployment and Development
In an age where data drives decision-making and automation, deep learning (DL) has become a cornerstone of many industries, influencing everything from healthcare to finance. DL has become pervasive with applications in a wide range of fields, including computer vision, natural language processing, voice applications, and robotics. The rise of Generative AI and Large Language…
MLOps in Action: Exploring Industry-Specific Requirements
MLOps, or Machine Learning Operations, brings together Machine Learning, DevOps, and Data Engineering, facilitating automation across the entire ML lifecycle—from data acquisition to model deployment and oversight. It streamlines the deployment, management, and scaling of machine learning models in practical applications. By integrating tools like cloud computing and containerization, MLOps aims to accelerate deployment, enhance…
Loading…
Something went wrong. Please refresh the page and/or try again.