Remove AWS Remove Hardware Remove Training Remove Tuning
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What Adrian Did Next?—?Part 2?—?Sun Microsystems

Adrian Cockcroft

I became the Sun UK local specialist in performance and hardware, and as Sun transitioned from a desktop workstation company to sell high end multiprocessor servers I was helping customers find and fix scalability problems. We had specializations in hardware, operating systems, databases, graphics, etc. He hasn’t changed.

Tuning 52
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Generative AI in the Enterprise

O'Reilly

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. Training models and developing complex applications on top of those models is becoming easier. of nonusers, 5.4%

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Infinitely scalable machine learning with Amazon SageMaker

All Things Distributed

For example, training on more data means more accurate models. At AWS, we continue to strive to enable builders to build cutting-edge technologies faster in a secure, reliable, and scalable fashion. Now, thousands of customers are trying Amazon SageMaker and building ML models on top of their data lakes in AWS.

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Python at Netflix

The Netflix TechBlog

takes place in Amazon Web Services (AWS), whereas everything that happens afterwards (i.e., Such applications track the inventory of our network gear: what devices, of which models, with which hardware components, located in which sites. Our Infrastructure Security team leverages Python to help with IAM permission tuning using Repokid.

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Measuring Carbon is Not Enough?—?Unintended Consequences

Adrian Cockcroft

In the simplest case, you have a growing workload, and you optimize it to run more efficiently so that you don’t need to buy or rent additional hardware, so your carbon footprint stays the same, but the carbon per transaction or operation is going down. I’ve written before about how to tune out retry storms.

Energy 52
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Bringing the Magic of Amazon AI and Alexa to Apps on AWS.

All Things Distributed

Last week, I wrote a blog about helping the machine learning scientist community select the right deep learning framework from among many we support on AWS such as MxNet, TensorFlow, Caffe, etc. Developers can build, test, and deploy chatbots directly from the AWS Management Console.

AWS 165
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Egnyte Architecture: Lessons learned in building and scaling a multi petabyte content platform

High Scalability

Egnyte is a secure Content Collaboration and Data Governance platform, founded in 2007 when Google drive wasn't born and AWS S3 was cost-prohibitive. AWS for builds. Once it's resolved our pipeline takes over and the ticket catches the next release train. We did this as AWS was cost-prohibitive. Ubuntu for server teams.