IBM Journey to AI blog
DECEMBER 13, 2023
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.
AWS Machine Learning Blog
FEBRUARY 27, 2024
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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IBM Journey to AI blog
JUNE 8, 2023
He then selected Krista’s AI-powered intelligent automation platform to optimize Zimperium’s project management suite, messaging solutions, development and operations (DevOps). The post How Krista Software helped Zimperium speed development and reduce costs with IBM Watson appeared first on IBM Blog.
IBM Journey to AI blog
SEPTEMBER 20, 2023
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
IBM Journey to AI blog
AUGUST 17, 2023
This year, innovation at the US Open was facilitated and accelerated by watsonx , IBM’s new AI and data platform for the enterprise. Learn more about watsonx Get to know IBM Consulting AI services The post How IBM Consulting ushered the US Open into a new era of AI innovation with watsonx appeared first on IBM Blog.
DataRobot Blog
MARCH 16, 2023
An AI platform that works well with a broad enterprise ecosystem: A platform that seamlessly integrates with the substantial investments businesses have already made in infrastructure, practitioner tools, data platforms and business applications. blog series and deep dive into the new 9.0
AWS Machine Learning Blog
OCTOBER 20, 2023
The architecture maps the different capabilities of the ML platform to AWS accounts. 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.
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