Canonical, the publisher of Ubuntu, announced today the general availability of Charmed Kubeflow 1.7. Charmed Kubeflow is an open-source, end-to-end MLOps platform that can run on any cloud, including hybrid cloud or multi-cloud scenarios. This latest release offers the ability to run serverless machine learning workloads and perform model serving, regardless of the framework that professionals use.
Canonical is happy to announce that Charmed Kubeflow 1.7 is now available in Beta. Kubeflow is a foundational part of the MLOps ecosystem that has been evolving over the years. With Charmed Kubeflow 1.7, users benefit from the ability to run serverless workloads and perform model inference regardless of the machine learning framework they use.
After ChatGPT took off, the AI/ML market suddenly became attractive to everyone. But is it that easy to kickstart a project? More importantly, what do you need to scale an AI initiative? MLOps or machine learning operations is the answer when it comes to automating machine learning workflows.
With artificial intelligence and machine learning in the news of late, what might they mean for cloud computing? Michael Vitale explains how we’re positioning these technologies to benefit our customers.
The world of software is growing more complex, and simultaneously changing faster than ever before. The simple monolithic applications of recent memory are being replaced by horizontal cloud-native applications. It is no surprise that such applications are more complex and can break into infinitely more ways (and ever new ways). They also generate a lot more data to keep track of. The pressure to move fast means software release cycles have shrunk drastically from months to hours, with constant change being the new normal.
Nowadays, when most people think about the term “machine learning,” they think of advanced, refined applications such as Chat-GPT, the chatbot-based deep learning text generator, or AlphaGo, the computer program that’s currently the “world's best player” of the board game Go.
