Update concept drift track

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_tracks/04_deploying_ml_models_at_scale.md

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-layout: default
+layout: track
 track-id: 4
-title: Deploying ML Models at Scale
-leader: Jan Rellermeyer
+title: Concept Drift and ML-Ops
+leader: Jan Rellermeyer, Luís Cruz
+phd: Lorena Poenaru-Olaru
 ---
 
-# Track 4: Deploying ML Models at Scale
-
-Machine learning techniques have seen a tremendous increase of adoption over the last decade and this growth has been fueled by both advances on the algorithmic level and the ability to leverage hardware acceleration for making the algorithms run at scale. However, as ML models increasingly supplement or replace traditional business logic in decision making processes, it is critical to develop an understanding of how machine learning can be deployed at scale. Important considerations are the deployment, testing, and long-term maintenance of models. The goal is to investigate the systems aspects and building tools for automating this process.
-
 The deployment of ML-applications brings in a unique set of engineering challenges. In particular if models are re-trained and re-deployed frequently, full automation of all relevant deployment tasks is needed. This includes monitoring and optimization in production, as well as data quality validation. For models requiring explainability, verification of not just outputs but also of the revised explanations will be necessary.
 In this track we will seek to address such challenges at ING scale.
 
-Related work:
+## Selected publications
+
+- Lorena Poenaru-Olaru, Luis Cruz, Jan S. Rellermeyer, Arie van Deursen: Maintaining and Monitoring AIOps Models Against Concept Drift. CAIN 2023: 98-99
+
+- Arumoy Shome, Luís Cruz, Arie van Deursen: Towards Understanding Machine Learning Testing in Practise. CAIN 2023: 117-118
+
+- Lorena Poenaru-Olaru, June Sallou, Luis Cruz, Jan S. Rellermeyer, Arie van Deursen: Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques. GREENS@ICSE 2023: 17-18
+
+- Arumoy Shome, Luís Cruz, Arie van Deursen: Data smells in public datasets. CAIN 2022: 205-216
+
+- Lorena Poenaru-Olaru, Luis Cruz, Arie van Deursen, Jan S. Rellermeyer: Are Concept Drift Detectors Reliable Alarming Systems? - A Comparative Study. IEEE Big Data 2022: 3364-3373
+
+- Haiyin Zhang, Luís Cruz, Arie van Deursen: Code smells for machine learning applications. CAIN 2022: 217-228
+
+- Bart van Oort, Luis Cruz, Babak Loni, Arie van Deursen: "Project smells" - Experiences in Analysing the Software Quality of ML Projects with mllint. ICSE (SEIP) 2022: 211-220
+
+- Ching-Chi Chuang, Luís Cruz, Robbert van Dalen, Vladimir Mikovski, Arie van Deursen:
+Removing dependencies from large software projects: are you really sure? SCAM 2022: 105-115
+
+- Lorena Poenaru-Olaru, Judith Redi, Arthur Hovanesyan, Huijuan Wang: Default Prediction Using Network Based Features. COMPLEX NETWORKS 2021: 732-743
 
-- Mark Haakman, Luís Cruz, Hennie Huijgens, Arie van Deursen. AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech. [arxiv.org/abs/2010.02716](https://arxiv.org/abs/2010.02716), 2020
+- Lorena Poenaru-Olaru: AutoML: towards automation of machine learning systems maintainability. Middleware Doctoral Symposium 2021: 4-5
 
-- Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald C. Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, Thomas Zimmermann:
-Software engineering for machine learning: a case study. ICSE (SEIP) 2019: 291-300
+- Bart van Oort, Luis Cruz, Maurício Aniche, Arie van Deursen: The Prevalence of Code Smells in Machine Learning projects. WAIN@ICSE 2021: 35-42
 
-- Jimmy Lin and Dmitriy Ryaboy. 2013. Scaling big data mining infrastructure: the Twitter experience. SIGKDD Explor. Newsl. 14, 2 (April 2013), 6-19.
+- Yuanhao Xie, Luis Cruz, Petra Heck, Jan S. Rellermeyer: Systematic Mapping Study on the Machine Learning Lifecycle. WAIN@ICSE 2021: 70-73
 
-**Track leader:** {{ page.leader }}
+- Mark Haakman, Luis Cruz, Hennie Huijgens, Arie van Deursen: AI lifecycle models need to be revised. Empir. Softw. Eng. 26(5): 95 (2021) [arxiv.org/abs/2010.02716](https://arxiv.org/abs/2010.02716).