17 research outputs found
Datasheets for Machine Learning Sensors
Machine learning (ML) sensors offer a new paradigm for sensing that enables
intelligence at the edge while empowering end-users with greater control of
their data. As these ML sensors play a crucial role in the development of
intelligent devices, clear documentation of their specifications,
functionalities, and limitations is pivotal. This paper introduces a standard
datasheet template for ML sensors and discusses its essential components
including: the system's hardware, ML model and dataset attributes, end-to-end
performance metrics, and environmental impact. We provide an example datasheet
for our own ML sensor and discuss each section in detail. We highlight how
these datasheets can facilitate better understanding and utilization of sensor
data in ML applications, and we provide objective measures upon which system
performance can be evaluated and compared. Together, ML sensors and their
datasheets provide greater privacy, security, transparency, explainability,
auditability, and user-friendliness for ML-enabled embedded systems. We
conclude by emphasizing the need for standardization of datasheets across the
broader ML community to ensure the responsible and effective use of sensor
data
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical
to diffusing machine-learning (ML) innovation. However, today, most ML
resources and experts are siloed in a few countries and organizations. In this
paper, we describe our pedagogical approach to increasing access to applied ML
through a massive open online course (MOOC) on Tiny Machine Learning (TinyML).
We suggest that TinyML, ML on resource-constrained embedded devices, is an
attractive means to widen access because TinyML both leverages low-cost and
globally accessible hardware, and encourages the development of complete,
self-contained applications, from data collection to deployment. To this end, a
collaboration between academia (Harvard University) and industry (Google)
produced a four-part MOOC that provides application-oriented instruction on how
to develop solutions using TinyML. The series is openly available on the edX
MOOC platform, has no prerequisites beyond basic programming, and is designed
for learners from a global variety of backgrounds. It introduces pupils to
real-world applications, ML algorithms, data-set engineering, and the ethical
considerations of these technologies via hands-on programming and deployment of
TinyML applications in both the cloud and their own microcontrollers. To
facilitate continued learning, community building, and collaboration beyond the
courses, we launched a standalone website, a forum, a chat, and an optional
course-project competition. We also released the course materials publicly,
hoping they will inspire the next generation of ML practitioners and educators
and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series:
https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin
Widening Access to Applied Machine Learning With TinyML
Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies
Big data glossary
To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on first-hand experience with these tools in a production environment. This handy glossary also includes a chapter of key terms that help define many of these tool categories: NoSQL Databases-Document-oriented databases using a key/value interface rather than SQLMapReduce-Tools that support distributed computing on large datasetsStorage-Technologies for stor
Data source handbook
If you''re a developer looking to supplement your own data tools and services, this concise ebook covers the most useful sources of public data available today. You''ll find useful information on APIs that offer broad coverage, tie their data to the outside world, and are either accessible online or feature downloadable bulk data. You''ll also find code and helpful links. This guide organizes APIs by the subjects they cover-such as websites, people, or places-so you can quickly locate the best resources for augmenting the data you handle in your own service. Categories include: Website tool