Each year, the World Economic Forum declares a list of technology pioneers—those companies that are innovating and creating cutting-edge products and services. In the 2018 list, many companies have artificial intelligence and machine learning at their core.
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Developers know a lot about the machine learning (ML) systems they create and manage, that’s a given. However, there is a need for non-developers to have a high level understanding of the types of systems.
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It’s challenging to get the right information on machine learning development that will actually work for your business.
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Data scientists and startups are enabling machine learning to detect any potential data fraud before it takes place and building fast data storage engine
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Deep learning is the state-of-the-art method for many AI challenges. Companies are failing to adopt it because of these four key challenges.
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The next innovation cycle in machine learning is the emergence of higher-level technologies that are able to exploit the native capabilities of the cloud
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These sources of free AI and machine learning training can get you started on a new career path.
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Similar to the way DevOps tools made cloud infrastructure more accessible and manageable, toolkits such as MLflow are attempting to accelerate machine learning
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For security professionals, machine learning capabilities can increase responder productivity and enable leaner, more efficient security operations.
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How do you resolve the tension between the need to build and deploy accurate machine learning models fast, and the need to understand how those models work, what data they touch upon, and what are the implications? Immuta says data governance is the answer.
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