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120+ live online training courses opened for January and February

O'Reilly Media - Ideas

Get hands-on training in Kubernetes, machine learning, blockchain, Python, management, and many other topics. 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. Machine Learning in Practice , January 15.

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Cloud Computing Trends and Innovations

Apiumhub

Implementation: Using edge computing frameworks like AWS IoT Greengrass or Azure IoT Edge to deploy machine learning models directly on edge devices for real-time data analysis. From identity management to ensuring the integrity of supply chain data, blockchain enhances trust and accountability in cloud-based interactions.

Trends 52
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250+ live online training courses opened for January, February, and March

O'Reilly Media - Ideas

Get hands-on training in Python, Java, machine learning, blockchain, and many other topics. Learn new topics and refine your skills with more than 250 new live online training courses we opened up for January, February, and March on our online learning platform. AI and machine learning. Blockchain.

Course 59
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160+ live online training courses opened for May and June

O'Reilly Media - Ideas

Get hands-on training in machine learning, blockchain, cloud native, PySpark, Kubernetes, and many other topics. Learn new topics and refine your skills with more than 160 new live online training courses we opened up for May and June on the O'Reilly online learning platform. AI and machine learning.

Course 46
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170+ live online training courses opened for March and April

O'Reilly Media - Ideas

Get hands-on training in machine learning, microservices, blockchain, Python, Java, and many other topics. Learn new topics and refine your skills with more than 170 new live online training courses we opened up for March and April on the O'Reilly online learning platform. AI and machine learning.

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Radar trends to watch: May 2022

O'Reilly Media - Ideas

FOMO (Faster Objects, More Objects) is a machine learning model for object detection in real time that requires less than 200KB of memory. It’s part of the TinyML movement: machine learning for small embedded systems. It won’t be a cryptocurrency, and won’t be backed by a blockchain.