Can it help businesses? Machine learning is an inescapable buzzword for many in the operations sector. Even friends and colleagues tend to make us aware of a new ML tool that may or may not be useful. While there are many ML tools in the market, not all are suitable for every business. Some tools, when tested, struggle to solve basic, everyday use cases. Therefore, when evaluating ML tools, other deeper questions and issues do arise.
Logz.io is one of Logz.io’s biggest customers. To handle the scale our customers demand, we must operate a high scale 24-7 environment with attention to performance and security. To accomplish this, we ingest large volumes of data into our service. As we continue to add new features and build out our new machine learning capabilities, we’ve incorporated new services and capabilities.
Kubeflow is an open-source MLOps platform that runs on top of Kubernetes. Kubeflow 1.6 was released September 7 2022 with Canonical’s official distribution, Charmed Kubeflow, following shortly after. It came with support for Kubernetes 1.22. However, the MLOps landscape evolves quickly and so does Charmed Kubeflow. As of today, Canonical supports the deployment of Charmed Kubeflow 1.6 on Charmed Kubernetes 1.23 and 1.24.
A typical bit of feedback I have had during my time at Splunk is that the Splunk Machine Learning Toolkit (MLTK) looks nice and all, but how are we supposed to get started using it? Choosing the right technique, let alone the right algorithm can be a daunting task for those who are unfamiliar with machine learning (ML). We’ve been thinking long and hard about how we can help offer more prescriptive introductions into using ML at Splunk and I’m pleased to present our set of MLTK deep dives.
Armin Müller is the co-founder of ScopeSET. ScopeSET specializes in R&D work to build and integrate tools in the model-based systems engineering domain, with a track record of more than 15 years of delivering innovative solutions for ESA and the aerospace industry. Training machine learning models takes a lot of time, so we’re always looking for ways to accelerate the process at ScopeSET. We use open source components to build research and development tools for technical companies.
Charmed Kubeflow is Canonical’s Kubeflow distribution and MLOps platform. The latest release shipped on 8 September. Our engineering team hosted a couple of livestreams to answer the questions from the community: a beta-release webcast and a technical deep-dive. In case you missed them, you can read the most frequently asked questions (FAQ) about MLOps and access helpful resources in this blog post. Note that you can also watch the videos on Youtube: Beta-release & a technical deep-dive.
Both Artificial Intelligence and Machine Learning are complex things. There are so many things to know. These days human life has changed because of AI. So, before understanding the differences, let’s know about different factors. If I have to say the difference in simple words. AI helps us solve various tasks; on the other hand, Machine Learning is the subset of AI’s specific tasks. So, you can say that all Machine Learning is AI, but all AI is not machine learning.
Kubeflow 1.6 was released on September 7, and Charmed Kubeflow 1.6 (Canonical’s distribution) came shortly after, as it follows the same roadmap. Charmed Kubeflow introduces a new version of Kubeflow pipelines as well as model training enhancements.
8 September 2022- Canonical, the publisher of Ubuntu, announces today the release of Charmed Kubeflow 1.6, an end-to-end MLOps platform with optimised complex model training capabilities. Charmed Kubeflow is Canonical’s enterprise-ready distribution of Kubeflow, an open-source machine learning toolkit designed for use with Kubernetes. Charmed Kubeflow 1.6 follows the same release cadence as the Kubeflow upstream project.
