Virtual Community Day: Cloud Migration November 14. 10A - 7PM (ET)
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November 2019 | Volume 1, Issue 4 Spotlight Technique Makes it Easier for AI to ...
As a part of the IBM Data Science Elite ( DSE ) engagements, we've had many conversations with customers about enabling CI/CD in the ...
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As a part of the IBM Data Science Elite (DSE) engagements, we've had many conversations with customers about enabling CI/CD in the machine learning (ML) pipeline - as a result I've decided to summarize the steps in a blog. The blog outlines the steps for implementing CI/CD workflows using IBM Watson Studio, and IBM Watson Machine Learning...Read more
Explainability is a hot topic. Data Science models are used to trigger recommendations such as “accept” or “refuse” a loan, and the need to answer “why this recommendation?” is arising. Customers impacted by recommendations are asking “why”, and so some companies are willing to answer, and in some geographies, regulations even prevent applications being used in some areas when explainability is not available.
A practical guide to developing machine learning apps.
These 10 videos will let you explore such basic functions of IBM Watson Studio such as getting started, intro to notebooks, collaboration, data storage and connection, and more.
A simple walkthrough of a Machine Learning exercise, creating, evaluating, and deploying a Machine Learning model without writing a single line of code.
Deploying AI-imbued apps and services isn’t as challenging as it used to be, thanks to offerings like IBM’s Watson Studio (previously Data Science Experience). Watson Studio, which debuted in 2017 after a 12-month beta period, provides an environment and tools that help to analyze, visualize, cleanse, and shape data; to ingest streaming data; and to train and optimize machine learning models in real time. And today, it’s becoming even more capable with the launch of AutoAI, a set of features designed to automate tasks associated with orchestrating AI in enterprise environments.
New AutoAI capabilities designed to help advance AI development by automating and speeding time-intensive data processes, while freeing-up data scientists to focus on machine learning