Operations | Monitoring | ITSM | DevOps | Cloud

MLflow is an open source platform, used for managing machine learning workflows. It was launched back in 2018 and has grown in popularity ever since, reaching 10 million users in November 2022. AI enthusiasts and professionals have struggled with experiment tracking, model management and code reproducibility, so when MLflow was launched, it addressed pressing problems in the market. MLflow is lightweight and able to run on an average-priced machine.

In the fast-paced world of software development, the ability to deliver and iterate quickly on APIs and microservices is essential. Traditional testing methods can be time-consuming and often slow down development cycles. That is where API and service mocking tools like Microcks can come to the rescue. Microcks shipped as a new community add-on for the recent MicroK8s 1.28 release. Microcks is a Cloud Native Computing Sandbox project dedicated to API mocking and testing tools on Kubernetes.

Canonical is proud to announce that it is now a member of the Open Networking Foundation (ONF). This highlights our dedication to open source in mobile telecommunications and our continuous commitment to bringing cutting-edge solutions to the sector.

Cyber Essentials is an increasingly important security standard within the UK that allows organisations to demonstrate to their customers that they operate their business in a secure and trustworthy manner. Achieving the Cyber Essentials certification helps businesses win new customers and stand out amongst their peers. It is a requirement for any company that seeks to sell their services to the UK Government.

The industrial automation landscape is evolving rapidly, driven by a fundamental shift in how software solutions are developed and deployed. In the past, many factories had in-house IT expertise to build and maintain their basic software stacks, the present reality is quite different. Small-scale factories often lack the IT know-how to manage their increasingly complex digital infrastructure, and they often rely on contractors to set up and maintain their systems.

Containers are a lightweight, portable, and consistent way to package applications and their dependencies. Containers provide an isolated environment, ensuring an application runs reliably across different environments. Enterprises and tech-savvy individuals are using container technologies because of their benefits. However, container orchestration tools have become necessary to manage clusters with the rise in container usage.

A lot of people, like myself, believe automotive is the most innovative sector today, especially when it comes to software. We are living a critical moment in automotive, where evolution is being pushed onto the market. This rapid software shift is posing challenges that are complicating developers’ progress. Most of these challenges are hardware-related. Gaining access to target development hardware has become an impossible task, the recent global microchip shortage did not help.

Storage and container management systems are almost polar opposites of each other. One deals with permanently storing, and protecting data for as long as it’s needed. The other automatically manages highly dynamic workloads, scaling resources up and down as required. More organisations are taking a container-first approach to application deployment and management, but the underlying challenge of safely and securely storing data still remains the same.

We’re super excited to announce that we have shipped the first release of our solution for big data – Charmed Spark. Charmed Spark packages a supported distribution of Apache Spark and optimises it for deployment to Kubernetes, which is where most of the industry is moving these days.

Today, Canonical announced the release of Charmed Spark – an advanced solution for Apache Spark® that provides everything users need to run Apache Spark on Kubernetes. Apache Spark is suitable for use in diverse data processing applications including predictive analytics, data warehousing, machine learning data preparation and extract-transform-load (ETL).