Demo 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
AUGUST 12, 2020
With Azure Functions, engineers don’t have to worry about provisioning and maintaining underlying hardware; they simply upload their code, and it’s up and running seconds later. Read on below to explore all the benefits of Dynatrace monitoring by examining our demo Azure Functions application. So stay tuned! What’s next.
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Dynatrace
AUGUST 12, 2020
With Azure Functions, engineers don’t have to worry about provisioning and maintaining underlying hardware; they simply upload their code, and it’s up and running seconds later. Read on below to explore all the benefits of Dynatrace monitoring by examining our demo Azure Functions application. So stay tuned! What’s next.
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.
All Things Distributed
OCTOBER 8, 2014
We built DynamoDB as a fully-managed service because we wanted to enable our customers, both internal and external, to focus on their application rather than being distracted by undifferentiated heavy lifting like dealing with hardware and software maintenance. Mars rover image indexing using DynamoDB.
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.
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