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IBM to help businesses scale AI workloads, for all data, anywhere

IBM Journey to AI blog

Watsonx.data will be core to IBM’s new AI and Data platform, IBM watsonx, announced today at IBM Think. “IBM and Cloudera customers will benefit from a truly open and interoperable hybrid data platform that fuels and accelerates the adoption of AI across an ever-increasing range of use cases and business processes.”

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Using John Snow Labs’ Medical Large Language Models on Azure Fabric

John Snow Labs

John Snow Labs’ Medical Language Models library is an excellent choice for leveraging the power of large language models (LLM) and natural language processing (NLP) in Azure Fabric due to its seamless integration, scalability, and state-of-the-art accuracy on medical tasks.

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

IBM software products are embedding watsonx capabilities across digital labor, IT automation, security, sustainability, and application modernization to help unlock new levels of business value for clients. AMC Networks is excited by the opportunity to capitalize on the value of all of their data to improve viewer experiences.

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How to choose the best AI platform

IBM Journey to AI blog

AI technology is quickly proving to be a critical component of business intelligence within organizations across industries. Major cloud infrastructure providers such as IBM, Amazon AWS, Microsoft Azure and Google Cloud have expanded the market by adding AI platforms to their offerings. trillion in value.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

This period also saw the development of the first data warehouses, large storage repositories that held data from different sources in a consistent format. The concept of data warehousing was introduced by Bill Inmon, often referred to as the “father of data warehousing.”

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

They work with other users to make sure the data reflects the business problem, the experimentation process is good enough for the business, and the results reflect what would be valuable to the business. What do they want to accomplish?