Streaming and longer context lengths for LLMs on Workers AI

Nov 15, 2023 ·

Cloudflare Workers AI Streaming is a new platform from Cloudflare that enables developers to create and deploy machine learning models in the browser. The platform makes use of WebAssembly (WASM), a low-level language that can be deployed onto a web page for execution, and TensorFlow.js, which provides access to all the necessary libraries and tools needed for machine learning development. The platform also includes a set of APIs that allow developers to integrate their model into any existing web page or application without needing to write any additional code.

With the introduction of Workers AI Streaming, Cloudflare aims to make it easier for developers to build applications that can leverage the power of machine learning without having to rely on third-party services like Google Cloud or Amazon Web Services. This allows developers to quickly spin up and deploy ML models directly on the web, without needing to worry about managing servers or infrastructure. In addition, the platform is designed to run models in near real-time, allowing for quick response times and making it possible to process data-heavy tasks in an efficient manner.

Using an intuitive user interface, developers have access to a variety of pre-trained models that can be used to create custom applications. For example, developers can use pre-trained models to classify images, detect objects, and generate text. Additionally, the platform can be used to train custom models, allowing developers to fine-tune existing models for specific tasks. Using the platform’s powerful APIs, developers can then easily deploy these custom models to any website or application.

By leveraging the power of WebAssembly and TensorFlow.js, Cloudflare Workers AI Streaming makes artificial intelligence capabilities more accessible than ever before. With its easy-to-use user interface and powerful APIs, developers can now easily implement ML models into their own applications. With its ability to run models in near real-time, developers can take advantage of the latest advances in machine learning technology and quickly deploy the models they need to create compelling and powerful applications.