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.
IBM Journey to AI blog
DECEMBER 13, 2023
From there, the chatbot uses automation to scan the database of responses and provide the most relevant response. There, a member of an IT or DevOps team can walk through the problem with an individual and provide real-time instructions for them to fix the problem themselves.
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IBM Journey to AI blog
JUNE 8, 2023
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.
IBM Journey to AI blog
AUGUST 17, 2023
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.
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
NOVEMBER 24, 2023
This offering enables BMW ML engineers to perform code-centric data analytics and ML, increases developer productivity by providing self-service capability and infrastructure automation, and tightly integrates with BMW’s centralized IT tooling landscape. A data scientist team orders a new JuMa workspace in BMW’s Catalog.
AWS Machine Learning Blog
OCTOBER 20, 2023
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.
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