Many government agencies and public administrations offer access to data, contributing to open data. Using IBM Watson Studio with Jupyter Notebooks and Apache Spark it is simple to retrieve, combine and analyze data from different sources. The result can be easily visualized. Learn what it takes with this IBM Cloud solution tutorial.
A few months ago, we reimagined data warehousing in the IBM Cloud with Flex Performance, the flagship tier of our new Flex line of offerings. Flex brings new levels of elasticity, speed, and resiliency to data warehousing on the IBM Cloud, and forms the foundation for our strategy moving forward. We're working continuously to not only strengthen and enhance its capabilities, but to also make them more accessible to you so you can better leverage them and get the most out of your data warehouse. Today, we're proud to announce a significant update to our Flex family.
In a new solution tutorial, I show you how to automatically retrieve and store GitHub traffic data the serverless way with IBM Cloud Functions and Db2. The data can then be analyzed via a Web app deployed to Cloud Foundry on IBM Cloud. The app is secured with App ID using OpenID Connect. The new service Dynamic Dashboard Embedded provides visualization of the views and clones of GitHub repositories.
With our recent cloud infrastructure and Deep-Learning-as-a-Service (DLaaS) announcements, IBM Cloud is a key contributor to the push towards AI. We’ve delivered a comprehensive suite of AI tools, high performance bare metal servers, and NVIDIA® GPUs that enables companies of all sizes to analyze complex unstructured data faster, more thoroughly and accurately, and at a far less cost than ever before.
Message Hub provides a simple communication mechanism built on Apache Kafka, enabling communication between loosely coupled Bluemix services. This article shows how to communicate with Message Hub from the Streaming Analytics Bluemix service using the messaging toolkit.
This blog post is an excerpt from our solution tutorial - "Gather, visualize, and analyze IoT data". The tutorial walks you through setting up an IoT device, gathering mobile sensor data in the Watson IoT Platform, exploring data and creating visualizations and then using advanced machine learning services to analyze data and detect anomalies in the historical data.
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, […]