Operations | Monitoring | ITSM | DevOps | Cloud

Machine Learning


AI/ML in retail: how the shopping experience has changed

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.


A hands-on guide to work with MindSpore on Kubeflow

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.


Charmed Kubeflow now integrates with MindSpore

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.

Sponsored Post

What Is MLOps? Machine Learning Operations and Its Role in Technology Transformation

Across all industries, businesses are investing in applications and services powered by artificial intelligence (AI) and machine learning (ML) to boost productivity and gain a competitive advantage.

HEAL Software

What's the hype about Machine Learning?

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.


How Logz.io Uses Observability Tools for MLOps

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.

AIOps Provider ScienceLogic Acquires Machine Learning Analytics Provider Zebrium to Provide At-A-Glance Root Cause Visibility

Moving toward its goal of freeing up resources of enterprise IT teams and optimizing digital experiences, AIOps and hybrid-cloud IT management provider ScienceLogic has acquired machine learning analytics firm Zebrium to automatically find the root cause of complex, modern (i.e., containerized, cloud-native) application problems.

Kubeflow 1.6 on Kubernetes 1.23 and beyond

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 Deeper Dive into Machine Learning at Splunk

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.


How to build machine learning models faster with Grafana

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.