Hardware Innovation Strategy Training 
Dynatrace
MAY 2, 2024
As organizations train generative AI systems with critical data, they must be aware of the security and compliance risks. Therefore, these organizations need an in-depth strategy for handling data that AI models ingest, so teams can build AI platforms with security in mind. Check out the resources below for more information.
Percona
DECEMBER 27, 2023
According to a 2023 Forrester survey commissioned by Hashicorp , 61% of respondents had implemented, were expanding, or were upgrading their multi-cloud strategy. Nearly every vendor at Kubecon and every person we spoke to had some form of a multi-cloud requirement or strategy. We expect that number to rise higher in 2024.
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Scalegrid
JANUARY 5, 2024
This article cuts through the complexity to showcase the tangible benefits of DBMS, equipping you with the knowledge to make informed decisions about your data management strategies. Join the revolution with ScaleGrid’s DBaaS – where efficiency meets innovation. Don’t miss out on the future of database management.
All Things Distributed
MARCH 13, 2017
Previously, the top priorities for IT departments were equipping data centers with hardware, purchasing software, and further developing proprietary software. In most cases, you won't select a learning offer from a general training catalog, as in the pre-digital era. But to thrive, innovations need a certain tension.
Percona
OCTOBER 18, 2023
Free of vendor lock-in Vendor lock-in refers to the loss of freedom to scale, innovate, or switch to alternatives due to dependencies on a specific database vendor. Strategy: Choosing your path Having a strategy for your migration will make the move to open source go that much smoother. And finally… budgets.
O'Reilly
NOVEMBER 28, 2023
Training models and developing complex applications on top of those models is becoming easier. Many of the new open source models are much smaller and not as resource intensive but still deliver good results (especially when trained for a specific application). report that the difficulty of training a model is a problem.
The Morning Paper
JUNE 27, 2019
Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. Performance on accelerators matters because almost all current machine learning research, and most training of production models, uses them.
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