There are many reasons why you might choose Kubernetes as your platform for hosting your application(s). In Cloud Foundry, for each application, the platform provides the isolation, OS, runtime, networking and management capabilities. This opinionated environment is ideal for some use cases. Kubernetes provides similar capabilities but gives you the control of the OS, runtime, networking rules of each service, communication between services in your cluster and more.
Watson Discovery is one year old! We are proud of how Discovery has evolved over the last year, now serving as a powerful insight engine on IBM Cloud. We released several milestone features over the year including passage retrieval, relevancy training, anomaly detection, semantic match, cross-collection federation while improving our tooling user experience, language support, […]
IBM is pleased to announce the immediate availability of our new low cost, low latency Regional Service for IBM Cloud Object Storage, for the UK-London Region, now open for all customers to use worldwide. With this new regional resiliency service, customers now have the choice to store and access their data within the UK London region for in-country data sovereignty, business continuity and high availability with low cost and low latency.
Logs help you troubleshoot issues with your clusters and apps. Sometimes, you might want to send logs somewhere for processing or long-term storage. On a Kubernetes cluster in the IBM Cloud Container Service, you can enable log forwarding for your cluster and choose where your logs are forwarded.
Many organizations have started to explore the value that machine learning can bring—from illuminating previously “dark data” such as images and videos, to creating models that help to guide or even automate business decision-making. However, very few companies have gone beyond pilots and prototypes, or made the transition from one-off projects to a scalable, repeatable workflow. Too often, machine learning exists in a bubble of its own, instead of being understood in the context of the broader data science workflow.
The IBM Streaming Analytics service is a cloud-based service for IBM Streams. Streams is an analytics platform that allows you to create applications that analyze data from a variety of sources in real time. Streaming Analytics continues to add enhancements to make it easy for you to create streaming applications however you choose. Previously, we announced integration with DSX to allow creating Streams applications in Python. Now, you can run a Beam application/pipeline in Streaming Analytics.
Imagine you’re interviewing a new job applicant who graduated top of their class and has a stellar résumé. They know everything there is to know about the job, and has the skills that your business needs. There’s just one catch: from the moment they join your team, they’ve vowed never to learn anything new again. You probably wouldn’t make that hire, because you know that lifeMachine Learning Brainlong learning is vital if someone is going to add long-term value to your team. Yet when we turn to the field of machine learning, we see companies making a similar mistake all the time. Data scientists work hard to develop, train and test new machine learning models and neural networks. However, once the models get deployed, they don’t learn anything new. After a few weeks or months, become static and stale, and their usefulness as a predictive tool deteriorates.
InfluxData is proud to announce that its InfluxCloud managed service for time-series data is now available on Bluemix. Rooted in open source, the InfluxData platform is built specifically for metrics, events, and other time-based data. In other words, a true modern time-series platform.
While most industries have enthusiastically embraced cloud computing, there is still a widespread perception in the financial services sector that adopting cloud services is either too risky from a security or availability perspective, or outright impossible under current regulatory conditions.