Digital Newsletter
Each week our editor Phil Alsop rounds up the most popular articles, videos and expert opinions. We compile this into a Digital Newsletter and send it straight to your inbox every week.
Digital Magazines
We'll let you know each time a new edition of MSP Channel Insights is released so that you're always kept up-to-date with the latest and greatest news and press releases.
Video Magazines
The MSP Channel Insights Video magazine contains the latest Zoom interviews with experts in the industry.
Enterprises currently face the challenge of how to adopt and integrate AI and ML into their operations effectively, at scale and with minimum complexity. In tandem, today’s AI workloads have become increasingly advanced and the compute power required to support them has exponentially increased.
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AI experts at CognitionX and Peltarion release new report on where the value in Deep Learning (DL) lies, and where the trend for Deep Learning is moving, based on insight across industries from AI thought-leaders in Europe.
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Databricks has introduced Model Registry, a new capability within MLflow, an open-source platform for the machine learning (ML) lifecycle created by Databricks. The new component enables a comprehensive model management process by providing data scientists and engineers a central repository to track, share, and collaborate on machine learning models. The Model Registry manages the full lifecycle of models and their stage transitions from experimentation to staging and deployment. Since...
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Company launches Dropbox Spaces, adds new machine intelligence, collaboration, and admin features.
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Platform helps enterprises fully operationalize AI quickly and is key building block of Infor’s Intelligent CloudSuite.
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Highly scalable, more secure hybrid cloud platform to help enterprises migrate, modernize and deploy next-generation applications.
Read MoreHear LinkedIn’s senior SRE, Todd Palino, share how the company continually improves the state of its infrastructure, so that the developers who are rolling out applications have a framework that they can do it within, and they can do it safely. LinkedIn currently generates over 50 terabytes a day of unique metrics on applications. No human is going to look at 50 terabytes a day of data and get anything useful out of it, so LinkedIn relies on systems give them some useful signal out of all that noise. By moving down the road of machine learning, LinkedIn can now do anomaly detection using machine learning models.
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