Architecture Hardware Infrastructure Training 
Dynatrace
MAY 2, 2024
Additionally, blind spots in cloud architecture are making it increasingly difficult for organizations to balance application performance with a robust security posture. As organizations train generative AI systems with critical data, they must be aware of the security and compliance risks.
Dynatrace
SEPTEMBER 30, 2021
Generally speaking, cloud migration involves moving from on-premises infrastructure to cloud-based services. In cloud computing environments, infrastructure and services are maintained by the cloud vendor, allowing you to focus on how best to serve your customers. However, it can also mean migrating from one cloud to another.
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Percona
DECEMBER 27, 2023
Oftentimes, it is a pillar of modern infrastructure strategy to avoid cloud vendor lock-in. Standardization and collaboration are key to sharing common knowledge and patterns across teams and infrastructures. It comprises numerous organizations from various sectors, including software, hardware, nonprofit, public, and academic.
The Morning Paper
FEBRUARY 18, 2020
The following chart breaks down features in three main areas: training and auditing, serving and deployment, and data management, across six systems. Finally, an analysis of ML research directions reveals the following arc through time: systems for training, systems for scoring, AutoML, and then responsible AI.
The Morning Paper
JUNE 13, 2019
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.
The Morning Paper
MAY 14, 2019
Last time around we looked at the DeathStarBench suite of microservices-based benchmark applications and learned that microservices systems can be especially latency sensitive, and that hotspots can propagate through a microservices architecture in interesting ways. When available, it can use hardware level performance counters.
All Things Distributed
SEPTEMBER 4, 2017
And thirdly, an "algorithmic revolution" has taken place, meaning it is now possible to train trillions of algorithms simultaneously, making the whole machine learning process much faster. But ultimately, there is something to be found for everyone who wants to define models, train them, and then scale.
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