Echo Dot (3rd Gen) - Smart speaker with Alexa - Charcoal

Use your voice to play a song, artist, or genre through Amazon Music, Apple Music, Spotify, Pandora, and others. With compatible Echo devices in different rooms, you can fill your whole home with music.

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Wireless Rechargeable Battery Powered WiFi Camera.

Wireless Rechargeable Battery Powered WiFi Camera is home security camera system lets you listen in and talk back through the built in speaker and microphone that work directly through your iPhone or Android Mic.

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Behind the Scene ( Part II )

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In the first part of the series we have shown how to define intents and capture dialog attributes using AWS Lex and AWS Lamda functions ( https://smartlake.ch/onboarding-virtual-assistant-for-banking-behind-the-scene-part-i/). We will dive now a little bit deeper into the mechanism we have used to be able to recommend some products based on the user answers.

There are multiple ways to implement product recommendations. I can refer to a previous post on that topic: https://smartlake.ch/personalizing-client-interaction-in-financial-services/. For this experiment we will use a simple classifier that we will train with some artificially made examples.

The classifier is implemented using Microsoft Azure Machine Leaning studio. We will pubslish a trained model from the ML Studio through an API and call it from AWS. It is not the best and most efficient way, but this is fun to show the interaction of multiple cloud services.

Lots of virtual assistants are merely questions and answers systems. They do not keep the context and hence do not have the ability to follow a structured dialogue. Most of the assistants also can not branch between one topic and the other. The assistant asks the user to repeat same answers again and again.



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