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9 IT skills where expertise pays the most

CIO

Cloud computing Average salary: $124,796 Expertise premium: $15,051 (11%) Cloud computing has been a top priority for businesses in recent years, with organizations moving storage and other IT operations to cloud data storage platforms such as AWS.

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Navigating the future of national tech independence with sovereign AI

CIO

Core challenges for sovereign AI Resource constraints Developing and maintaining sovereign AI systems requires significant investments in infrastructure, including hardware (e.g., Many countries face challenges in acquiring or developing the necessary resources, particularly hardware and energy to support AI capabilities.

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Microsoft acquires Fungible, a maker of data processing units, to bolster Azure

TechCrunch

In December, reports suggested that Microsoft had acquired Fungible, a startup fabricating a type of data center hardware known as a data processing unit (DPU), for around $190 million. ” A DPU is a dedicated piece of hardware designed to handle certain data processing tasks, including security and network routing for data traffic. .”

Azure 282
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Scaling out Postgres with the Citus open source shard rebalancer

The Citus Data

One of the big changes in Citus 10 —in addition to adding columnar storage , and the new ability to shard Postgres on a single Citus node—is that we open sourced the shard rebalancer. Yes, that’s right, we have open sourced the shard rebalancer! And now that—big news!—we’ve

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Rails 8.0: No PaaS Required

Ruby on Rails

That’s a job for open source, and Rails 8 is ready to solve it. Whether to a cloud VM or your own hardware. In addition to getting rid of the accessory service dependency, it also allows for a vastly larger and cheaper cache thanks to its use of disk storage rather than RAM storage. But that’s ridiculous.

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The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t

CIO

Some are relying on outmoded legacy hardware systems. Most have been so drawn to the excitement of AI software tools that they missed out on selecting the right hardware. 2] Foundational considerations include compute power, memory architecture as well as data processing, storage, and security.

Analytics 205
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Host concurrent LLMs with LoRAX

AWS Machine Learning - AI

Traditional model serving approaches can become unwieldy and resource-intensive, leading to increased infrastructure costs, operational overhead, and potential performance bottlenecks, due to the size and hardware requirements to maintain a high-performing FM. The following diagram represents a traditional approach to serving multiple LLMs.