Time-Lapse Video Storage Optimization

Last week MongoDB CEO Dev Ittycheria likened present-day AI to the “dial-up phase of the internet era.” He’s not wrong. I recently suggested we’re in the “awkward toddler phase” of generative AI (genAI). It’s full of excitement (“her first step!”), but straightforward tasks are clumsy, like basic math. Yet in some areas, the future of generative AI is happening right now. One area is coding assistants such as Amazon CodeWhisperer.To read this article in full, please click here

While graphics processing units (GPUs) once resided exclusively in the domains of graphic-intensive games and video streaming, GPUs are now equally associated with and machine learning (ML). Their ability to perform multiple, simultaneous computations that distribute tasks—significantly speeding up ML workload processing—makes GPUs ideal for powering artificial intelligence (AI) applications. The single instruction multiple data (SIMD) stream architecture in a GPU enables data scientists to break down complex tasks into multiple small units. As such, enterprises pursuing AI and ML initiatives are now more likely to choose GPUs instead of central processing units (CPUs) to rapidly analyze large data sets in algorithmically complex and hardware-intensive machine learning workloads. This is especially true for large language models (LLMs) and the generative AI applications built on LLMs.To read this article in full, please click here

In the rapidly evolving landscape of IaaS cloud computing, public cloud providers are increasingly reaching feature and function parity. This means they are beginning to look alike.Before you keyboard warriors remind me that some obscure feature in the object storage system on one provider is better than the object storage feature on another provider, I know they are not exactly the same. I think it’s okay to consider that they are all moving to a similar group of services that do the same things.How did it get this way? This development creates a competitive dynamic between these providers and offers new opportunities and challenges for enterprises. If we have three major cloud providers and three other providers that are catching up quickly, then it’s no longer reasonable to point to a single provider as the one to use. There’s no “best cloud” anymore.To read this article in full, please click here

Web development platform provider Netlify has added AI-enabled deploy assist capabilities to its Composable Web Platform.Unveiled March 7, AI-enabled deploy assist analyzes failed deployments and offers suggestions to correct errors. Netlify said the feature is intended to reduce the time developers spend manually reviewing logs and debugging failed builds, ensuring failed builds do not become bottlenecks and improving the developer experience.Netlify CTO Dana Lawson explained how the company’s AI addresses issues across toolsets. “When building composable architectures, teams are pulling in tools, content, and integrations that suit them best. When issues happen, it’s not always clear what part of your composable architecture is having issues. It could be code, configuration, etc,” Lawson said.To read this article in full, please click here

Kubernetes has become the de facto way to schedule and manage services in medium and large enterprises. Coupled with the microservice design pattern, it has proved to be a useful tool for managing everything from websites to data processing pipelines. But the ecosystem at large agrees that Kubernetes has a cost problem. Unfortunately, the predominant way to cut costs is itself a liability.The problem is not Kubernetes. The problem is the way we build applications.To read this article in full, please click here