As we’ve shown in a previous blog, search-based detection rules and Elastic’s machine learning-based anomaly detection can be a powerful way to identify rare and unusual activity in cloud API logs. Now, as of Elastic Security 7.13, we’ve introduced a new set of unsupervised machine learning jobs for network data, and accompanying alert rules, several of which look for geographic anomalies.
Kubeflow is the open-source machine learning toolkit on top of Kubernetes. Kubeflow translates steps in your data science workflow into Kubernetes jobs, providing the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks. Read more about Kubeflow
Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.
We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. The latest release includes: Let’s get started with the new operational overview dashboard which was built using Splunk’s brand new dashboard studio functionality which I highly recommend checking out. You can learn more about it in this recent tech talk which you can watch on demand.
Cerner Corp. is a supplier of healthcare information technology systems, services, and devices. The company, with $5.7 billion in annual revenue, empowers people and communities to engage in their own care. A key aspect of the business is surfacing data to enable their clients to make informed decisions about their healthcare. The 29,000 Cerner employees in 30 countries are on a mission to shape the healthcare of tomorrow.
In the digital age, the speed and performance of apps and websites have a huge impact on the customer experience. To ensure a high level of quality, Application Performance Monitoring (APM) refers to the process of tracking the performance and availability of software systems. Let’s look at what Application Performance Monitoring is, how AI and machine learning are being applied to stay ahead of the competition, and several real-world use cases.
In part 1 and part 2 of this series, we examined how AI/ML can help improve healthcare and the challenges faced by AI/ML teams in realizing the benefits respectively. In this part, we will explore how Robin and Dell can help overcome these challenges.
In part 1 of this series, we examined how AI/ML can help improve healthcare. AI/ML is an ambitious undertaking that promises to revolutionize healthcare. Getting excited is easy, but where do you start and why is it not just another empty promise? In fact, despite all these promises and futures, most AI/ML projects fail and don’t deliver. The failure rate of AI/ML projects is starting to make some wonder if this is real or hype.
The financial services (FS) industry is going through a period of change and disruption. Technology innovation has provided the means for financial institutions to reimagine the way in which they operate and interact with their customers, employees and the wider ecosystem. One significant area of development is the utilisation of artificial intelligence (AI) and machine learning (ML) which has the potential to positively transform the FS sector.
Technology has accelerated changes toward information-based healthcare delivery and management. Today’s multi-disciplinary approach to delivering better healthcare outcomes coupled with advanced imaging and genetic-based customized treatment models depend on AI/ML driven information systems. At Robin.io, we believe machine learning is the life-saving technology that will transform healthcare. AI/ML challenges the traditional, reactive approach to healthcare.
