#shorts Open source AI solutions
AI is rapidly evolving, impacting every sector from entertainment to space tech.🌟 Canonical is your ally for open source AI and MLOps tools. Explore our solutions for Kubeflow, MLflow, Spark and more.
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Introduction to Apache Kafka
Have you heard about Apache Kafka but aren’t quite sure about its functions or applications? This webinar is tailored for you. Apache Kafka is more than just a buzzword in the tech community; it’s a critical tool for data processing and management. Join our expert-led webinar to explore the world of Apache Kafka, a powerful distributed event streaming platform. Learn how Canonical simplifies Kafka operations, offering secure, automated deployments and maintenance across various clouds.
Telco-grade Sylva-compliant Canonical platforms
In December 2023, Canonical joined the Sylva project of Linux Foundation Europe to provide fully open-source and upstream telco platform solutions to the project. Sylva aims to tackle the fragmentation in telco cloud technologies and the vendor lock-in caused by proprietary platform solutions, by defining a common validation software framework for telco core and edge clouds.
Canonical announces the availability of Real-time Ubuntu for Amazon EKS Anywhere
Barcelona, Spain. 28 February 2024. Canonical today announced an expansion of its relationship with Amazon Web Services (AWS) to make Real-time Ubuntu available to Amazon Elastic Kubernetes Service Anywhere (Amazon EKS Anywhere) customers for use in Open radio access network (RAN) commercial deployments. With Real-time Ubuntu and Amazon EKS Anywhere, customers can benefit from ultra-reliable low-latency operating system performance and simplified Kubernetes cluster management.
Canonical announces the general availability of Charmed Kafka
27 February 2024: Today, Canonical announced the release of Charmed Kafka – an advanced solution for Apache Kafka® that provides everything users need to run Apache Kafka at scale. Apache Kafka is an event store that supports a range of contemporary applications including microservices architectures, streaming analytics and AI/ML use cases. Canonical Charmed Kafka simplifies deployment and operation of Kafka across public clouds and private data centres alike.
Ubuntu AI Podcast | Launch of 2nd series
Season 2 of Ubuntu AI podcast is here! After a start with great guests and great feedback from our listeners, we are ready to kickstart a new series of episodes. We will continue talking about AI and open source, focusing mostly on the machine learning lifecycle, AI on public cloud, AI at the edge and the security angle of the AI projects. This time around, we will periodically invite contributors to open source projects from the AI space to join us.
Crafting new Linux schedulers with sched-ext, Rust and Ubuntu
In our ongoing exploration of Rust and Ubuntu, we delve into an experimental kernel project that leverages these technologies to create new schedulers for Linux. Playing around with CPU scheduling policies has always been a dream for many kernel hackers and OS enthusiasts. However, such material typically remains within the domain of a few core kernel developers with extensive years of experience.
What is a Kubernetes operator?
Operators take a real-world operations team’s knowledge, wisdom, and expertise, and codify it into a computer program that helps operating complex server applications like databases, messaging systems, or web applications. Operators provide implementations for operating applications that are testable and thus more reliable at runtime.
Migrating from Cloudera to a modern data hub architecture
In the early 2010s, Apache Hadoop captured the imagination of the tech community.
Preview Confidential AI with Ubuntu Confidential VMs and Nvidia H100 GPUs on Microsoft Azure
With Ubuntu confidential AI on Azure, businesses can undertake various tasks including ML training, inference, confidential multi-party data analytics, and federated learning with confidence. The effectiveness of AI models depends heavily on having access to large amounts of good quality data. While using publicly available datasets has its place, for tasks like medical diagnosis or financial risk assessment, we need access to private data during both training and inference.