Kubernetes development and adoption continues to grow at a rapid pace, and keeping current can be difficult without the right process and tools. For example, IBM Cloud Container Service launched with support for Kubernetes v1.5.6 earlier this year. Since that initial launch, the Kubernetes community provided 3 minor releases (v1.6, v1.7, and v1.8) and over 25 patch releases. By year's end there's likely to be another minor release and numerous patches. So with all this change, what's the best way to keep your cluster and apps up-to-date and secure?
Due to feedback of the market we’d like to inform you about the deprecation of the IoT for Insurance service. However, IoT for Insurance is not going away -- but rather as of July 31, 2017 IBM IoT for Insurance became a SaaS offering. To utilize IBM IoT for Insurance please utilize the SaaS offering available on the IBM Marketplace at https://www.ibm.com/us-en/marketplace/ibm-iot-for-insurance .
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.
As the leanest form of container-based application computing, serverless functions as a service (FaaS) run code exactly when needed, at exactly the right scale, either through direct API invocation or as triggered by specific other events. Functions are powerfully well-suited for managing API connections across clouds, processing IoT data streams, and implementing connections between microservices […]
Change doesn’t stop, so neither should your analytics. You could capture the most crucial, valuable insight of all—but if you don’t identify and act on it while it’s still valid, or before your competitors do, it’s worth nothing. Imagine you’re an electronics company that has sunk thousands of hours and millions of dollars into building a profile of the perfect customer for a new product release. Before you can claw back your investment with a wildly successful launch, a rival comes along and disrupts the entire industry with an innovative device like no one has ever seen before. All that effort and resources expended… all for nothing.
Data science is rapidly being established as the new frontier for analytics, as it moves from niche interest to the mainstream. Combining elements of statistics, computer science, applied mathematics and visualization, it offers a powerful new set of tools and techniques to enable more effective decision-making.