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Manage your Amazon Lex bot via AWS CloudFormation templates

AWS Machine Learning

AWSTemplateFormatVersion: "2010-09-09" Transform: AWS::Serverless-2016-10-31 Description: CloudFormation template for book hotel bot. Similarly, you can use a Lambda function for fulfillment as well, for example writing data to databases or calling APIs save the collected information. Resources: # 1.

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5 Reasons You Need a Next-Gen Chatbot for Customer Support

Solvvy

Next-gen chatbots (as opposed to the somewhat flawed ‘traditional’, rule-based chatbots from the 2010’s) leverage tools like AI, natural language processing, machine learning, and automation to deliver fast, easy, and high-quality self-service to your customers with a far lower cost-to-serve. What is a Next-Gen Chatbot?

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How to Think Like a Service Brand and Take the Lead

CSM Magazine

in 2010 to $4.1B Flexibility is key; at Rufus Leonard we frequently recommend the MACH approach (Microservices, APIs, Cloud Native and Headless CMS) because it allows you to choose the best of breed technologies, add elements independently to enable rapid innovation and carry out upgrades/maintenance more efficiently and cheaply.

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The Definitive 2021 Guide to Customer Effort Score

Nicereply

Created in 2010, the Customer Effort Score is fairly new to the scene but is becoming increasingly more popular. In 2010, a team of researchers at the CEB wanted to uncover the most effective strategies customer service teams used to build loyalty. It’s also possible to use the Nicereply API to build an entirely customized survey flow.

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6 Best Marketplaces to Sell Online: Start Your Own Business

JivoChat

It’s possible to upload your catalog via an Excel spreadsheet and integrate Mercado Libre Global Selling with your API. As part of the Alibaba Group, AliExpress was founded in 2010, is available in 220 different countries, and counts on more than 150 million buyers. The platform also provides shipping solutions. AliExpress .

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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

This notebook demonstrates how to use the JumpStart API for text classification. The dataset we fine-tune the model is SemEval-2010 Task 8. Similar to the previous sections, we use a HyperparameterTuner object to interact with SageMaker hyperparameter tuning APIs. Text classification.

Scripts 73
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Process mortgage documents with intelligent document processing using Amazon Textract and Amazon Comprehend

AWS Machine Learning

The following is a high-level overview of the steps involved: Extract UTF-8 encoded plain text from image or PDF files using the Amazon Textract DetectDocumentText API. For structured and semi-structured documents containing forms and tables, we use the Amazon Textract AnalyzeDocument API. Train a custom classifier using the CSV file.

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