Artificial Intelligence Hardware Tuning 
Dynatrace
JANUARY 26, 2021
This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. Performing updates, installing software, and resolving hardware issues requires up to 17 hours of developer time every week.
Dynatrace
OCTOBER 2, 2023
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. However, the move to microservices comes with its own challenges and complexities.
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Dynatrace
NOVEMBER 15, 2021
Logs can include data about user inputs, system processes, and hardware states. Log analysis can reveal potential bottlenecks and inefficient configurations so teams can fine-tune system performance. “Logging” is the practice of generating and storing logs for later analysis. Optimized system performance.
O'Reilly
MARCH 11, 2025
These smaller distilled models can run on off-the-shelf hardware without expensive GPUs. And they can do useful work, particularly if fine-tuned for a specific application domain. Spending a little money on high-end hardware will bring response times down to the point where building and hosting custom models becomes a realistic option.
All Things Distributed
NOVEMBER 30, 2016
Effectively applying AI involves extensive manual effort to develop and tune many different types of machine learning and deep learning algorithms (e.g. automatic speech recognition, natural language understanding, image classification), collect and clean the training data, and train and tune the machine learning models.
O'Reilly
NOVEMBER 28, 2023
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. That pricing won’t be sustainable, particularly as hardware shortages drive up the cost of building infrastructure.
O'Reilly
SEPTEMBER 19, 2023
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.
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