Watch to explore how to ensure data security and privacy in AI applications that employ Large Language Models (LLMs). As generative AI becomes increasingly vital for enterprises – especially in applications such as chatbots utilizing Retrieval-Augmented Generation (RAG) systems – ensuring the security and confidentiality of data within these frameworks is essential. During this webinar: We will introduce confidential computing as a method for safeguarding data, with a specific focus on its application within RAG systems for securing data during usage or processing;

When performing machine learning tasks in the cloud, enterprises understandably have concerns about the potential compromise of their sensitive data privacy as well as their model’s intellectual property. Additionally, stringent industry regulations often prohibit the sharing of such data. This makes it difficult, or outright impossible, to utilise large amounts of valuable private data, limiting the true potential of AI across crucial domains.

Modern web applications require a balance between performance, scalability, and ease of development. Valkey is an open-source (BSD) high-performance key/value datastore that supports various workloads such as caching and message queues and can act as a primary database. This makes Valkey an ideal database for running modern web applications.

Canonical MicroCloud’s 2.1.0 LTS release features support for single-node deployments, improved security posture, and more flexibility during the initialization process. In this video we guide you through the new process, starting with deploying a MicroCloud on a single node, and then expanding with two additional members for a highly available cluster. Follow along and try it yourself.

A MicroCloud is a new lightweight, featureful, and straightforward cloud for on-demand computing at the edge. MicroClouds differ from IoT which uses thousands of single machines or sensors to gather data, yet does not perform computing tasks. Instead, MicroClouds reuse proven cloud primitives with unattended, autonomous, and clustering features that resolve typical edge computing challenges.