[HTML][HTML] Online learning and continuous model upgrading with data streams through the Kafka-ML framework
A pipeline of constant data streams is being built by the Internet of Things (IoT) to monitor
information about the physical environment. In parallel, Artificial Intelligence (AI) is …
information about the physical environment. In parallel, Artificial Intelligence (AI) is …
The orchestration of Machine Learning frameworks with data streams and GPU acceleration in Kafka‐ML: A deep‐learning performance comparative
Abstract Machine Learning (ML) applications need large volumes of data to train their
models so that they can make high‐quality predictions. Given digital revolution enablers …
models so that they can make high‐quality predictions. Given digital revolution enablers …
[HTML][HTML] Kafka-ML: Connecting the data stream with ML/AI frameworks
Abstract Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to
train, improve, and make predictions through their algorithms. With the digital revolution and …
train, improve, and make predictions through their algorithms. With the digital revolution and …
Managing and deploying distributed and deep neural models through Kafka-ML in the cloud-to-things continuum
The Internet of Things (IoT) is constantly growing, generating an uninterrupted data stream
pipeline to monitor physical world information. Hence, Artificial Intelligence (AI) continuously …
pipeline to monitor physical world information. Hence, Artificial Intelligence (AI) continuously …
[图书][B] IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams …
This book constitutes selected papers from the Second International Workshop on IoT
Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International …
Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International …
Evolutive deep models for online learning on data streams with no storage
A Besedin, P Blanchart, M Crucianu… - ECML/PKDD 2017 …, 2017 - cea.hal.science
In recent years Deep Learning based methods gained a growing recognition in many
applications and became the state-of-the-art approach in various fields of Machine Learning …
applications and became the state-of-the-art approach in various fields of Machine Learning …
[HTML][HTML] Towards flexible data stream collaboration: Federated Learning in Kafka-ML
Federated learning is applied in scenarios where organisations lack sufficient data volume
for modelling their business logic and cannot share their data with external parties …
for modelling their business logic and cannot share their data with external parties …
A generic architectural framework for machine learning on data streams
C Augenstein, T Zschörnig, N Spangenberg… - … Conference, ICEIS 2019 …, 2020 - Springer
In the past years, the importance of processing data streams increased with the emergence
of new technologies and application domains. The Internet of Things provides many …
of new technologies and application domains. The Internet of Things provides many …
[PDF][PDF] Streammlops: Online learning in practice from big data streams & real-time applications
Continuously Learning and serving from evolving streaming data to real-time inference in
production is a challenging problem. Traditionally, data is partitioned and processed in …
production is a challenging problem. Traditionally, data is partitioned and processed in …
STARLORD: sliding window temporal accumulate-retract learning for online reasoning on datastreams
Nowadays, data sources, such as IoT devices, financial markets, and online services,
continuously generate large amounts of data. Such data is usually generated at high …
continuously generate large amounts of data. Such data is usually generated at high …