This post is an excerpt from our solution tutorial that walks you through the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. All of this happening in an integrated and unified self-service experience on IBM Cloud.
The Streaming Analytics service in the IBM Cloud is an advanced analytic platform allowing user-developed applications to quickly ingest, analyze, and correlate information as it arrives from a wide variety of real-time data sources. Today, an enhanced version of the service has been released as a beta, to introduce some exciting new features.
If you follow my private blog you might remember that I have been using the IBM Watson Conversation service and DB2. My goal was to write a database-driven Slackbot, a Slack app that serves as chat interface to data stored in Db2. I will write more about that entire Slackbot soon, but today I wanted to share some chatbot tricks I learned. How to gather input data, perform checks and clean up the processing environment.
One of the most frequent questions clients ask when visiting a Cloud Garage is "Can you build us a chatbot?" This question is reflective of an industry-wide trend towards more natural language in computerised interactions, and also more automation of interactions currently handled by humans. Today, there are currently more than 33,000 chatbots on Facebook Messenger alone. Many businesses are turning to Watson Conversation to help take out cost and improve user satisfaction. Our Hursley Labs colleague Simon Burns has written an excellent series of articles on how to write great Watson chatbots, which you should definitely go read. Think of this blog as a supplement, with our experiences from the field. To address this pressing question, I’ve compiled a set of considerations for you to address when deciding whether a chatbot is truly the solution to your business needs.
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 […]
Over the past few years, we’ve seen a significant rise in popularity for intelligent personal assistants, such as Apple’s Siri, Amazon Alexa, and Google Assistant. Though they initially appeared to be little more than a novelty, they’ve evolved to become rather useful as a convenient interface to interact with service APIs and IoT connected devices.
In this post, I'll show you how to build a basic Spring app with Twitter login using Spring Social. Then we'll use Watson Tone Analyzer to determine the dominant emotion from each of the tweets on the time of the logged-in user. The project we will create will be similar to the Accessing Twitter Data Spring guide, but with a few modifications.
What if you had unlimited time to tap into a growing ecosystem of AI-infused services and runtimes to build your apps? And what if you could do it at no cost? Today, we're making that possible. We are excited to announce the IBM® Cloud Lite account - a free account that never expires. Ever. Seriously.
In this post, I'll show you how to build a basic Spring app with Facebook login using Spring Social. Then we'll use Watson Personality Insights to analyze the profile of the logged-in user. The project we will create will be similar to the Accessing Facebook Data Spring guide, but with a few modifications.