Unstructured data continues to grow exponentially, and
businesses across industries
are actively exploring or leveraging artificial intelligence (AI) technologies
to extract insights from the vast amounts of data they can access.
IBM Watson offers a wide variety of out -of-the-box
, customizable AI services
designed to extract insights from unstructured data.
Businesses can use these insights to improve a variety of business goals:
improving customer service;
understanding the emotion or tone of customer communications;
and personalizing the customer experience.
In today's tutorial, we explored two common use cases for leveraging Watson AI services in Watson Studio:
Uncovering hidden insights from Facebook usage Extracting insights from social media posts with Watson and Spark
in Watson Studio
businesses across industries
are actively exploring or leveraging artificial intelligence (AI) technologies
to extract insights from the vast amounts of data they can access.
IBM Watson offers a wide variety of out -of-the-box
, customizable AI services
designed to extract insights from unstructured data.
Businesses can use these insights to improve a variety of business goals:
improving customer service;
understanding the emotion or tone of customer communications;
and personalizing the customer experience.
In today's tutorial, we explored two common use cases for leveraging Watson AI services in Watson Studio:
Uncovering hidden insights from Facebook usage Extracting insights from social media posts with Watson and Spark
in Watson Studio
We'll focus on a fundamental theme, the performance and scalability of solutions leveraging Watson AI services. To this end, we examine how 3 different approaches can be used to augment unstructured text on sample tweets using the Watson AI service, and discuss the pros and cons of each approach.
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