In the early 2010s, Apache Hadoop captured the imagination of the tech community.

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.

Edge AI is transforming the way that devices interact with data centres, challenging organisations to stay up to speed with the latest innovations. From AI-powered healthcare instruments to autonomous vehicles, there are plenty of use cases that benefit from artificial intelligence on edge computing. This blog will dive into the topic, capturing key considerations when starting an edge AI project, main benefits, challenges and how open source fits into the picture.

In the fast-paced world of technology, innovation is the key to staying ahead of the curve. As businesses strive for efficiency, speed, and real-time data processing, the spotlight is increasingly turning towards edge computing. Edge computing represents a paradigm shift in the way data is processed and analysed. Unlike traditional cloud computing, which centralises data processing in distant data centres, edge computing brings the processing power closer to the source of data.

Landscape has been a member of the Canonical product list for almost as long as Canonical has existed. Landscape allows administrators to manage their desktop and server instances from a single centralised portal.

Financial institutions handle vast amounts of sensitive and confidential data, including customer information, transaction details, and regulatory compliance records. A trusted database ensures the security and privacy of this sensitive information, protecting it from unauthorised access, breaches, or cyber threats. MongoDB is the ideal fit, and it’s one of the most widely used databases in the financial services industry. It provides a sturdy, adaptable and trustworthy foundation.

Data is like the crown jewels of any organisation, if lost or exposed there could be severe repercussions. Failure to protect against system failure could lead to the loss of business data rendering a business non-functional and ultimately causing it’s failure. Exposing sensitive data to unauthorised parties not only leads to reputational damage, but can also cause businesses to incur massive fines.

As the automotive industry continues to advance into the world of high-performance computing (HPC), it becomes increasingly crucial to achieve environmental parity for seamless software integration. In this blog post, we will explore the synergy between Elektrobit and Canonical at the core of ‘EB corbos Linux – built on Ubuntu‘ in the context of automotive computing.

With multiple technological innovations all converging at the same time, we are living in an exciting era for the automotive industry. From AI to 5G, and plenty in between, we can expect to see a host of groundbreaking trends emerge this year. As electric vehicles (EVs) completely disrupt the market and the OEMs’ business strategies, the customer focus is shifting away from traditional internal combustion engine (ICE) vehicles, challenging the way that cars are being built and designed.

Organisations are reshaping their digital strategies, and AI is at the heart of these changes, with many projects now ready to run in production. Enterprises often start these AI projects on the public cloud because of the ability to minimise the hardware burden. However, as initiatives scale, organisations often look to migrate the workloads on-prem for reasons including costs, digital sovereignty or compliance requirements.