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Call Center, Contact Center and Customer Experience Events – April 2018

Taylor Reach Group

Whether you’re interested in speaking, exhibiting or simply attending these events, we wanted to keep everybody informed on the upcoming Contact Center and CX Events. This event is a hub of ideas, inspiration and industry connections for customer service and customer contact executives who strive to innovate the customer experience.

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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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Gone Virtual: Recap of the CETX Conference

Callminer

With over a thousand registrants, our inaugural CETX and very first digital conference also marked one of our largest events to date. The two-day event – hosted by bestselling author and customer service and experience expert, Shep Hyken – featured dynamic keynote presentations from four of renowned CX experts.

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QBR in SaaS: Is the traditional QBR dead?

Totango

The traditional fixed quarterly review is being replaced by real-time performance monitoring and artificial intelligence data analysis, enabling you to stay engaged with clients between scheduled reviews. Here we’ll show you how to update your SaaS QBR strategy to keep up with the latest technology and best practices.

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Host the Spark UI on Amazon SageMaker Studio

AWS Machine Learning

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. For the SageMaker Processing job, you can configure the Spark event log location directly from the SageMaker Python SDK.

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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

Reflection time is the time it takes for a feature to be available to read after the contributing events were emitted, for example, the time between a listener liking a show and the PIT LikeCount feature being updated. Sources of the data are the backend services directly serving the app. Data Engineer for Amp on Amazon.

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New Trends in Customer Success You Need to Know About

Totango

Out-of-the-box templates automate the process of defining measurable customer goals, establishing key performance indicators, promoting best practices and tracking performance. Another of the most important new trends in customer success is the application of big data analytics methods powered by artificial intelligence.