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He is passionate about building secure and scalable AI/ML and bigdata solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. About the authors Ram Vittal is a Principal ML Solutions Architect at AWS.
When you think of how artificial intelligence is being used in customer service these days, chatbots may come to mind. Indeed, chatbots have redefined the way in which customers interact with brands, helping consumers find answers more quickly and make purchases more seamlessly. AI may be used to essentially predict human behavior.
The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase behaviors.
He has over 11 years of experience in developing and leading data science, machine learning, and bigdata initiatives. Prior to joining AWS, he was consulting customers in various industries such as Automotive, Manufacturing, Telecommunications, Media & Entertainment, Retail and Financial Services.
Beverage manufacturers are getting into entertainment in a big way. As a great example, think about Red Bull which today is seen more as an entertainment company that just happens to make a drink! Chatbots are providing additional resources to the already overworked customer services departments.
Understanding the basics of AI chatbots An artificial intelligence chatbot is a computer program designed to converse with users through text-based or voice-based interfaces, using Artificial Intelligence (AI) technologies such as Natural Language Processing (NLP) and Machine Learning (ML).
In this sample architecture of a chatbot application, there are five trust boundaries where controls are demonstrated, based on how AWS customers commonly build their LLM applications. Your LLM application may have more or fewer definable trust boundaries. Ram Vittal is a Principal ML Solutions Architect at AWS.
Putting the risk table from Learn how to assess the risk of AI systems into action, the severity and likelihood of risks for a ground truth dataset validating a production chatbot with frequent customer use would be greater than an internal evaluation dataset used by developers to advance a prototype.
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