IBM Journey to AI blog
JANUARY 18, 2023
Data platform architecture has an interesting history. A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution.
AWS Machine Learning Blog
OCTOBER 20, 2023
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.
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NOVEMBER 24, 2023
With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.
IBM Journey to AI blog
APRIL 9, 2024
This allows the Masters to scale analytics and AI wherever their data resides, through open formats and integration with existing databases and tools. “Hole distances and pin positions vary from round to round and year to year; these factors are important as we stage the data.”
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While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.
IBM Journey to AI blog
DECEMBER 20, 2023
Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.
IBM Journey to AI blog
APRIL 15, 2024
Design considerations for virtualized data platforms 1. Latency and real-time analysis Challenge: Accessing stored data directly typically incurs less latency compared to virtualized data retrieval, which can impede real-time predictive maintenance analyses, where timely insights are crucial.
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