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Seven customer service types that organizations should provide

IBM Journey to AI blog

Read the blog: How generative AI is transforming customer service Customer service types that organizations should prioritize By offering different types of customer service and several customer support channels, organizations demonstrate they are investing in customer care.

Chatbots 203
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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

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How Krista Software helped Zimperium speed development and reduce costs with IBM Watson

IBM Journey to AI blog

Krista Software helps Zimperium automate operations with IBM Watson Vamsi Kurukuri, VP of Site Reliability at Zimperium, developed a strategy to remove roadblocks and pain points in Zimperium’s deployment process. Once all parties approve the release, Krista then deploys it.

DevOps 181
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Foundational models at the edge

IBM Journey to AI blog

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai

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How IBM Consulting ushered the US Open into a new era of AI innovation with watsonx

IBM Journey to AI blog

IBM iX , the experience design arm of IBM Consulting, and IBM’s AI consultants work with the United States Tennis Association (USTA) to integrate technology from dozens of partners, automate key business processes and develop new features. Most importantly though, the teams focus on delivering world-class digital experiences to fans.

DevOps 138
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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. The reference architecture for the ML platform with various AWS services is shown in the following diagram.

ML 101
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How the DataRobot AI Platform Is Delivering Value-Driven AI

DataRobot Blog

This means they need the tools that can help with testing and documenting the model, automation across the entire pipeline and they need to be able to seamlessly integrate the model into business critical applications or workflows. blog series and deep dive into the new 9.0 features over the next few weeks.

AI 98