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Machine learning modeling News
November 07, 2019
Booz Allen Hamilton introduced an AI platform and marketplace made for uploading, deploying and managing AI models across a scalable environment.
October 30, 2019
With the new TensorFlow for enterprises, organizations running previous versions of TensorFlow can get long-term support, including security updates and select bug fixes.
August 09, 2019
IBM's open source AI Explainability 360 toolkit packages algorithms and training examples to help humans better understand the decision-making process of machine learning models.
June 20, 2019
DataRobot machine learning tools should see a boost following the company's acquisition of ParallelM, a startup with tools for deploying, managing and monitoring AI.
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Watch this Amazon SageMaker demo on how to deploy machine learning models on AWS. From setting up IAM roles to sorting your data, this tutorial will guide you through the process. Continue Reading
Data visualization tools find increasing uses as part of AI processes to explore data in the initial stages of model development and make outputs easier to digest. Continue Reading
AI encompasses a wide range of disciplines, from advanced math to application development, and building a strong AI team starts with incredibly skilled data scientists. Continue Reading
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Data scientists offer practical insights into the role of visualization tools in building, exploring, deploying and monitoring their machine learning models. Continue Reading
In the world of AI, open source software is driving most of the innovation. But with vendor tools largely sidelined, what does this mean for things like security and technical support? Continue Reading
Machine learning can assist enterprises by quickly modeling large data sets. Choosing the right algorithm depends on the desired outcome and the makeup of your data science team. Continue Reading
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At its core, AI is a complex modeling process with layers of information. In order to be able to explain the algorithm's decision-making process, start with its input data. Continue Reading
While AI can play a role in helping researchers and conservationists address the impacts of climate change, it can also contribute to carbon emissions, making it a mixed blessing. Continue Reading
A shortage of data for machine learning training sets can halt a company's AI development in its tracks. Turning to external sources and hidden data can solve the problem. Continue Reading
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Getting started with machine learning throws multiple hurdles at enterprises. But the serverless computing trend, when applied to machine learning, can help remove some barriers. Continue Reading
Ethical AI black box problems complicate user trust in the decision making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading
To create his March Madness bracket predictions, the head of data science at DataRobot uses a host of machine learning algorithms and some predictive analytics. Continue Reading