Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

For the last few years, the entire networking industry has focused on analytics and mining more and more information out of the network. This makes sense because of all the changes in networking over the last decade. Changes like network overlays, public cloud, applications delivered as a service, and containers mean we need to pay attention to much more diverse information out there.

Outlier Detection is now available as part of the Grafana Machine Learning toolkit in Grafana Cloud for Pro and Advanced users. With this feature, you can monitor a group of similar things, such as load-balanced pods in Kubernetes, and get alerted when some of them start behaving differently than their peers. There’s supposed to be a video here, but for some reason there isn’t. Either we entered the id wrong (oops!), or Vimeo is down.

As an observability provider, we are always confronted with our clients’ goal for faster resolution of problems and better overall performance of their systems. By working on large-scale projects at Logz.io, I see the same main challenge coming up for all: extracting valuable insights from huge volumes of data generated by modern systems and applications.

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.