Remove Artificial Intelligence Remove Hardware Remove Open Source Remove Tuning
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10 tips for migrating from monolith to microservices

Dynatrace

Limits of a lift-and-shift approach A traditional lift-and-shift approach, where teams migrate a monolithic application directly onto hardware hosted in the cloud, may seem like the logical first step toward application transformation. Many organizations also find it useful to use an open source observability tool, such as OpenTelemetry.

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I Actually Chatted with ChatGPT

O'Reilly

software” rather than “hardware” in our brains). Speech recognition errors : ChatGPT’s speech recognition system (presumably based on OpenAI’s open-source Whisper model ) is very good, but it does at times misinterpret what I’m saying. would do if I were on a noisy phone connection with someone and didn’t hear them clearly.

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Generative AI in the Enterprise

O'Reilly

16% of respondents working with AI are using open source models. Even with cloud-based foundation models like GPT-4, which eliminate the need to develop your own model or provide your own infrastructure, fine-tuning a model for any particular use case is still a major undertaking. We’ll say more about this later.)

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Structural Evolutions in Data

O'Reilly

Doubly so as hardware improved, eating away at the lower end of Hadoop-worthy work. And then there was the other problem: for all the fanfare, Hadoop was really large-scale business intelligence (BI). Google goes a step further in offering compute instances with its specialized TPU hardware.