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MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need forbenchmark carpentryfor scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection calledcloudmeshwith its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator). 

Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technology’s main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model. 

Today’s problems require a plethora of analytics tasks to be conducted to tackle state-of-the-art computational challenges posed in society impacting many areas including health care, automotive, banking, natural language processing, image detection, and many more data analytics-related tasks. Sharing existing analytics functions allows reuse and reduces overall effort. However, integrating deployment frameworks in the age of cloud computing are often out of reach for domain experts. Simple frameworks are needed that allow even non-experts to deploy and host services in the cloud. To avoid vendor lock-in, we require a generalized composable analytics service framework that allows users to integrate their services and those offered in clouds, not only by one, but by many cloud compute and service providers.We report on work that we conducted to provide a service integration framework for composing generalized analytics frame-works on multi-cloud providers that we call our Generalized AI Service (GAS) Generator. We demonstrate the framework’s usability by showcasing useful analytics workflows on various cloud providers, including AWS, Azure, and Google, and edge computing IoT devices. The examples are based on Scikit learn so they can be used in educational settings, replicated, and expanded upon. Benchmarks are used to compare the different services and showcase general replicability.