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Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments
This white paper introduces my educational community initiative to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, data sets, software and hardware.
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
In data-centric AI, active learning (AL) plays a vital role, but current AL tools 1) require users to manually select AL strategies, and 2) can not perform AL tasks efficiently.
CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking
We present CodeReef - an open platform to share all the components necessary to enable cross-platform MLOps (MLSysOps), i. e. automating the deployment of ML models across diverse systems in the most efficient way.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
It improves the training efficiency, remarkably relaxes the requirements on the hardware, and supports efficient large-scale FL experiments with stateful clients by: (1) sequential training clients on devices; (2) decomposing original aggregation into local and global aggregation on devices and server respectively; (3) scheduling tasks to mitigate straggler problems and enhance computing utility; (4) distributed client state manager to support various FL algorithms.
HarmonE: A Self-Adaptive Approach to Architecting Sustainable MLOps
We validate our approach using a Digital Twin (DT) of an Intelligent Transportation System (ITS), focusing on traffic flow prediction as our primary use case.
ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background.
The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
This article provides an overview of the Collective Knowledge technology (CK or cKnowledge).
Towards Architecting Sustainable MLOps: A Self-Adaptation Approach
In today's dynamic technological landscape, sustainability has emerged as a pivotal concern, especially with respect to architecting Machine Learning enabled Systems (MLS).
Software Engineering
Challenges in Deploying Machine Learning: a Survey of Case Studies
In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems.
