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After talking to machine learning and infrastructure engineers at major Internet companies across the US, Europe, and China, two groups of companies emerged. One group has invested hundreds of millions of dollars into infrastructure to allow real-time machine learning and has already seen returns on their investments. The other group still wonders if there’s value in real-time machine learning.
Speedscale is proud to announce its Centralized Log Collection capability. When diagnosing the source of problems in your API, more information is better. For most engineers, the diagnosis process usually starts with the application logs. Unfortunately, logs are usually either discarded or stored in Observability systems that engineers don’t have direct access to. Compounding this issue is that the log information is typically not correlated to what calls were made against the API.
On your journey from reactive to proactive IT operations, you may be unsure where to start. If your organization is lacking visibility into your operations, that’s the best place to begin. Automation and artificial intelligence (AI) can help you gain that needed visibility. AIOps and visibility Artificial intelligence for IT operations (AIOps) offers numerous benefits, including faster response time, improved IT health, and simpler IT management.
As much as I enjoy writing firmware, I am, at heart, a hardware engineer. I love hunting for minutia in chip datasheets. I love fiddling with eval boards, tacking on wires, and reworking nets together. I love flipping through The Art of Electronics, finding a new circuit, and piecing through its operation. This is why, when invited to write for Interrupt, I jumped at the chance to write about a hardware-related topic that’s near and dear to my heart: debug tools.
Social media conglomerate Meta is the latest tech company to build an “AI supercomputer” — a high-speed computer designed specifically to train machine learning systems. The company says its new AI Research SuperCluster, or RSC, is already among the fastest machines of its type and, when complete in mid-2022, will be the world’s fastest. “Meta has developed what we believe is the world’s fastest AI supercomputer,” said Meta CEO Mark Zuckerberg in a statement.
If 2021 was the soft launch of the Decade of the Internet of Things (IoT), 2022 is set to accelerate IoT-related technologies and investments, addressing societal and economic issues. The rollout of 5G, maturing of artificial intelligence algorithms for streaming IoT data, increased computing power at the edge and cheaper/better sensor technology is the “convergence” that has supercharged IoT adoption.
The term “Industry 4.0” originated from a committee of German technocrats who wanted to make predictions about where technology was headed next. And certainly, it fits nicely in the storyline of the initial Industrial revolution with the rise of machines powered by steam and water, the second revolution sparked by the use of electricity, and the third revolution of automated production with robots.
The topic of artificial intelligence (AI) is wide-ranging and extensive, providing what seems like limitless possibilities for the world of work. As such, it’s easy for leaders to get lost in the excitement of implementing an AI model for their enterprise.
