MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. MLOps is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring.

AI/ML is reinventing the reality of many industries, including retail. From brick-and-mortar stores to online marketplaces, retail companies are all increasing their investments in artificial intelligence, in order to gain a competitive advantage, better understand their customers and solve some of their long-lasting problems.

Looking at the report that Gartner did in 2022 regarding top technology trends, AI engineering represents an important pillar in the near future. It is composed of three core technologies: DataOps, MLOps and DevOps.The discipline’s main purpose is to develop AI models that can quickly and continuously provide business value. For instance, models that enable cross-functional collaboration, automation, data analysis, and machine learning.

On 8 November 2022, at Open Source Experience Paris, Canonical announced that Charmed Kubeflow, Canonical’s enterprise-ready Kubeflow distribution, now integrates with MindSpore, a deep learning framework open-sourced by Huawei. Charmed Kubeflow is an end-to-end MLOps platform with optimised complex model training capabilities designed for use with Kubernetes.

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.