Dynatrace
JULY 6, 2022
AI that is based on machine learning needs to be trained. This requires significant data engineering efforts, as well as work to build machine-learning models. This enables IT admins to spend more time on innovation, rather than constantly fighting fires. An observability platform for IT automation.
The Netflix TechBlog
SEPTEMBER 18, 2020
A role in data science eventually seemed like a natural transition, but it wasn’t without its hurdles: With my consulting background, I had to go through a few other roles first while learning how to code on the side. Julie] Chris and I have the same primary stakeholders (or engineering team that we support): Encoding Technologies.
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O'Reilly
FEBRUARY 2, 2020
First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. You need a repository for models and for the training data. Second, the behavior of AI systems changes over time.
Adrian Cockcroft
FEBRUARY 13, 2023
Provides comparison of inference workload on P4dn GPU instances vs. AWS Trainium saving 92% energy and 90% cost, and training workload on P4dn vs. AWS Inferentia 2.6x shorter training time, saving 54% energy and 75% cost. Building a data lake of detailed information about energy use of many physical devices.
Abhishek Tiwari
JULY 20, 2019
Luckily, aircraft operating manuals and training procedures are so formalised and well established that there is no scope of performance degradation even if one or more crew members are replaced. I also have a strong feeling that long-lived teams are not good for innovation and disruption.
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