Remove Analytics Remove APIs Remove Engineering Remove Self service
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Create powerful self-service experiences with Amazon Lex on Talkdesk CX Cloud contact center

AWS Machine Learning

Contact centers are using artificial intelligence (AI) and natural language processing (NLP) technologies to build a personalized customer experience and deliver effective self-service support through conversational bots. The connector was built by using the Amazon Lex Model Building API with the AWS SDK for Java 2.x.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Maintainability – The platform’s architecture and code base should be well organized, modular, and maintainable.

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Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

Large enterprises sometimes set up a center of excellence (CoE) to tackle the needs of different lines of business (LoBs) with innovative analytics and ML projects. To generate high-quality and performant ML models at scale, they need to do the following: Provide an easy way to access relevant data to their analytics and ML CoE.

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5 Tips for Planning the Growth of your Support Team in the New Year

Nicereply

In an ideal world, your self-service support would scale so effectively that you’d never have to hire another support person ever again. It can be overwhelming to sit down and start thinking about all of the things that you need to do to sort out the growth of your support team in the new year.

Metrics 98
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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Analyze and tag assets stored in Veeva Vault PromoMats using Amazon AppFlow and Amazon AI Services

AWS Machine Learning

In a previous post , we talked about analyzing and tagging assets stored in Veeva Vault PromoMats using Amazon AI services and the Veeva Vault Platform’s APIs. For example, you can use the connector to extract standardized study information from protocols stored in Vault RIM and expose it downstream to medical analytics insight teams.

APIs 71
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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Automated reminders, payment notices, and self-service payment options empower debtors to fulfill their obligations at their convenience, fostering a sense of control and cooperation. Seamlessly integrate proprietary or third-party CRM applications with our extensive APIs and data dictionary libraries.