Remove Efficiency Remove Hardware Remove Training Remove Tuning
article thumbnail

Resolving technical debt helps state and local agencies improve business impact

Dynatrace

State and local agencies must spend taxpayer dollars efficiently while building a culture that supports innovation and productivity. APM helps ensure that citizens experience strong application reliability and performance efficiency. million annually through retiring legacy technology debt and tool rationalization.

article thumbnail

Infinitely scalable machine learning with Amazon SageMaker

All Things Distributed

For example, training on more data means more accurate models. Last re:Invent, to make the problem of authoring, training, and hosting ML models easier, faster, and more reliable, we launched Amazon SageMaker. Machine learning models are usually trained tens or hundreds of times. In machine learning, more is usually more.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Cloud Native Predictions for 2024

Percona

Consequently, they might miss out on the benefits of integrating security into the SDLC, such as enhanced efficiency, speed, and quality in software delivery. It comprises numerous organizations from various sectors, including software, hardware, nonprofit, public, and academic.

Cloud 77
article thumbnail

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%

article thumbnail

From Proprietary to Open Source: The Complete Guide to Database Migration

Percona

Resource allocation: Personnel, hardware, time, and money The migration to open source requires careful allocation (and knowledge) of the resources available to you. Does anyone on my team require further training before we start? Evaluating your hardware requirements is another vital aspect of resource allocation.

article thumbnail

Structural Evolutions in Data

O'Reilly

A company then needed to train up their ops team to manage the cluster, and their analysts to express their ideas in MapReduce. Doubly so as hardware improved, eating away at the lower end of Hadoop-worthy work. Google goes a step further in offering compute instances with its specialized TPU hardware.

article thumbnail

A case for managed and model-less inference serving

The Morning Paper

As we saw with the SOAP paper last time out, even with a fixed model variant and hardware there are a lot of different ways to map a training workload over the available hardware. Different hardware architectures (CPUs, GPUs, TPUs, FPGAs, ASICs, …) offer different performance and cost trade-offs.