MLaaS: Machine Learning as a Service

By Mauro Ribeiro, Katarina Grolinger, Miriam A. M. Capretz. International Conference on Machine Learning and Applications, 2015

429 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ed5ab1cff7dd3a902eea4a811b15aa5ea3a36b30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Farinaz Koushanfar
2. Bita Darvish Rouhani
3. Huili Chen
4. Yossi Adi
5. Joseph Keshet
6. Xiao Wang
7. Elena Hernández-Pereira
8. Eduardo Mosqueira-Rey

In Electrical and Electronic Engineering

1. Farinaz Koushanfar
2. Xiao Wang
3. Benny Pinkas
4. Lixu Wang
5. Qi Zhu
6. Shichao Xu
7. Arturo Geigel

In Signal Processing

1. Farinaz Koushanfar
2. Bita Darvish Rouhani
3. Huili Chen
4. Sameep Mehta
5. Zhiyi Tian
6. Per Ola Kristensson
7. Suranjana Samanta
8. Joseph Keshet

Citations in the corpus

• A Review of User Interface Design for Interactive Machine Learning (2018, background): “The concept of “Machine Learning as a Service” (MLaaS) has also recently emerged in response to the demand for advanced analytics and data-driven applications within industry (Ribeiro et al. 2015).”
• A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, background): “For example, in a MachineLearning-as-a-Service systems [103], an adversary can generate adversarial examples to maliciously change corresponding predictions [41, 59].”
• BlackMarks: Blackbox Multibit Watermarking for Deep Neural Networks (2018, background): “On the other hand, black-box WMs enable IP protection for Machine Learning as a Service (MLaaS) [2] while only zero-bit watermarking methods have been proposed.”
• Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization (2021, background): “Deep Learning (DL) is the backbone of Artificial Intelligence as a Service (AIaaS) (Ribeiro et al., 2015), which is being provided in a wide range of applications including music composition (Briot et al., 2020), autonomous driving (Li et al., 2021a), smart building (Xu et al., 2020a), etc.”
• Towards Crafting Text Adversarial Samples (2017, background): “Machine Learning (ML) algorithms can be deployed as a service (MLaaS) (14) where a trained model is available to the users for the task of classification/ regression of their query data.”
• Human-in-the-loop machine learning: a state of the art (2022, methodology): “These tools are often based on cloud developments, offered through a “Software as a service” (SaaS) model, in this case renamed as “machine learning as a Service (MLaaS)” (Ribeiro et al. 2015).”
• Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring (2018, methodology): “In addition to open source models, MLaaS allows the users to build more personalized systems without much overhead [32].”