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7 enterprise data strategy trends

CIO

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices. Here’s a quick rundown of seven major trends that will likely reshape your organization’s current data strategy in the days and months ahead.

Trends 363
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The AI continuum

CIO

It provides smart applications for translation, speech-to-text, cybersecurity monitoring and automation. Great for: Extracting meaning from unstructured data like network traffic, video & speech. Downsides: Lower accuracy; the source of dumb chatbots; not suited for unstructured data.

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Cybersecurity Snapshot: Cyber Agencies Offer Secure AI Tips, while Stanford Issues In-Depth AI Trends Analysis, Including of AI Security

Tenable

1 - Multinational cyber agencies issue best practices for secure AI deployment Looking for best practices on how to securely deploy artificial intelligence (AI) systems? The AI Index report tracks, collates, distills, and visualizes data related to AI,” reads the report’s introduction. and the U.S.

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Cybersecurity Snapshot: How To Boost the Cybersecurity of AI Systems While Minimizing Risks

Tenable

1 - How to ensure AI helps, not hurts, cybersecurity How can organizations use artificial intelligence (AI) in a way that’s safe and that benefits cybersecurity? 1 - How to ensure AI helps, not hurts, cybersecurity How can organizations use artificial intelligence (AI) in a way that’s safe and that benefits cybersecurity?

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120+ live online training courses opened for January and February

O'Reilly Media - Ideas

Learn new topics and refine your skills with more than 120 new live online training courses we opened up for January and February on our online learning platform. Artificial intelligence and machine learning. Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook , January 7-8.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

When speaking of machine learning, we typically discuss data preparation or model building. Much less often the technology is mentioned in terms of deployment. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. This article. More time for development of new models.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Artificial intelligence is still in its infancy. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production. Why model development does not equal software development.