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<89919391+editorialbot@users.noreply.github.com> Date: Fri, 1 Apr 2022 08:00:38 +0100 Subject: [PATCH 2/3] Creating 10.21105.joss.04038.crossref.xml --- joss.04038/10.21105.joss.04038.crossref.xml | 176 ++++++++++++++++++++ 1 file changed, 176 insertions(+) create mode 100644 joss.04038/10.21105.joss.04038.crossref.xml diff --git a/joss.04038/10.21105.joss.04038.crossref.xml b/joss.04038/10.21105.joss.04038.crossref.xml new file mode 100644 index 0000000000..867bfcac03 --- /dev/null +++ b/joss.04038/10.21105.joss.04038.crossref.xml @@ -0,0 +1,176 @@ + + + + 20220401T065841-056d3c2757aef1ba4d64e9c1b72e54b8ad6d261c + 20220401065841 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org/ + + + + + 04 + 2022 + + + 7 + + 72 + + + + ADaPT-ML: A Data Programming Template for Machine +Learning + + + + Andrea M. + Whittaker + https://orcid.org/0000-0001-8512-1810 + + + + 04 + 01 + 2022 + + + 4038 + + + 10.21105/joss.04038 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.6402630 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/4038 + + + + 10.21105/joss.04038 + https://joss.theoj.org/papers/10.21105/joss.04038 + + + https://joss.theoj.org/papers/10.21105/joss.04038.pdf + + + + + + Snorkel: rapid training data creation with +weak supervision + Ratner + VLDB Journal + 2-3 + 29 + 10.1007/s00778-019-00552-1 + 0949877X + 2020 + Ratner, A., Bach, S. H., Ehrenberg, +H., Fries, J., Wu, S., & Ré, C. (2020). Snorkel: rapid training data +creation with weak supervision. VLDB Journal, 29(2-3), 709–730. +https://doi.org/10.1007/s00778-019-00552-1 + + + Label Studio: Data labeling +software + Tkachenko + Tkachenko, M., Malyuk, M., +Shevchenko, N., Holmanyuk, A., & Liubimov, N. (2020-2021). Label +Studio: Data labeling software. +https://github.com/heartexlabs/label-studio + + + Developments in MLflow: A system to +accelerate the machine learning lifecycle + Chen + Proceedings of the fourth international +workshop on data management for end-to-end machine +learning + 10.1145/3399579.3399867 + 9781450380232 + 2020 + Chen, A., Chow, A., Davidson, A., +DCunha, A., Ghodsi, A., Hong, S. A., Konwinski, A., Mewald, C., +Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Singh, A., Xie, F., +Zaharia, M., Zang, R., Zheng, J., & Zumar, C. (2020). Developments +in MLflow: A system to accelerate the machine learning lifecycle. +Proceedings of the Fourth International Workshop on Data Management for +End-to-End Machine Learning. +https://doi.org/10.1145/3399579.3399867 + + + FastAPI + Ramírez + GitHub repository + 2021 + Ramírez, S. (2021). FastAPI. In +GitHub repository. https://github.com/tiangolo/fastapi; +GitHub. + + + Analyzing and visualizing twitter +conversations + Gutierrez + Proceedings of the 31st annual international +conference on computer science and software engineering + 2021 + Gutierrez, C. A. G., Whittaker, A., +Patenio, K. M., Gehman, J., Lefsrud, L. M., Barbosa, D., & Stroulia, +E. (2021). Analyzing and visualizing twitter conversations. Proceedings +of the 31st Annual International Conference on Computer Science and +Software Engineering, 4–13. + + + Snorkel Flow + Snorkel AI + 2021 + Snorkel AI, Inc. (2021). Snorkel +Flow. https://snorkel.ai/platform/ + + + Neu.ro + Neu.ro + 2022 + Neu.ro. (2022). Neu.ro. +https://neu.ro/ + + + MLRun + Iguazio + 2022 + Iguazio, Ltd. (2022). MLRun. +https://github.com/mlrun/mlrun + + + + + + From d4d7ab0087d6950faa177271b7f9f9e6b138b1ad Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 1 Apr 2022 08:00:38 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.04038.jats --- joss.04038/10.21105.joss.04038.jats | 340 ++++++++++++++++++++++++++++ 1 file changed, 340 insertions(+) create mode 100644 joss.04038/10.21105.joss.04038.jats diff --git a/joss.04038/10.21105.joss.04038.jats b/joss.04038/10.21105.joss.04038.jats new file mode 100644 index 0000000000..8c3c754e9a --- /dev/null +++ b/joss.04038/10.21105.joss.04038.jats @@ -0,0 +1,340 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +4038 +10.21105/joss.04038 + +ADaPT-ML: A Data Programming Template for Machine +Learning + + + +0000-0001-8512-1810 + +Whittaker +Andrea M. + + + + + +University of Alberta + + + + +21 +10 +2021 + +7 +72 +4038 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Docker +MLOps +data programming +machine learning +model lifecycle + + + + + + Summary +

Classification is a task that involves making a prediction about + which class(es) a data point belongs to; this data point can be text, + an image, audio, or can even be multimodal. This task can become + intractable for many reasons, including:

+ + +

Insufficient training data to create a data-driven model; + available training data may not be appropriate for the domain + being studied, it may not be of the right type (e.g. only text but + you want text and images), it may not have all of the categories + you need, etc.

+
+ +

Lack of available annotators with domain expertise, and/or + resources such as time and money to label large amounts of + data.

+
+ +

Studying a phenomenon that changes rapidly, so what constitutes + a class may change over time, making the available training data + obsolete.

+
+
+

ADaPT-ML + (Figure 1) is a + multimodal-ready MLOps system that covers the data processing, data + labelling, model design, model training and optimization, and endpoint + deployment, with the particular ability to adapt to classification + tasks that have the aforementioned challenges. ADaPT-ML is designed to + accomplish this by:

+ + +

Using Snorkel + (Ratner + et al., 2020) as the data programming framework to create + large, annotated, multimodal datasets that can easily adapt to + changing classification needs for training data-driven models.

+
+ +

Integrating Label Studio + (Tkachenko + et al., 2020-2021) for annotating multimodal data.

+
+ +

Orchestrating the Labelling Function / Label Model / End Model + development, testing, and monitoring using + MLflow + (Chen + et al., 2020).

+
+ +

Deploying all End Models using FastAPI + (Ramírez, + 2021)

+
+
+ +

Overview of how ADaPT-ML orchestrates data labelling and + the end model + lifecycle.

+ +
+
+ + Statement of Need +

Often when studying natural phenomena by creating data-driven + models, processing the data becomes the largest challenge. Without a + framework to build upon and implement one’s ideas, researchers are + forced to hastily build inflexible programs from the ground up. When + hypotheses need to be reworked or modelling a new aspect of the + phenomena becomes necessary, even more time is spent on the program + before finally being able to test out new ideas. This inherently + causes problems, with additional problems arising such as including + internal and external validation steps as an afterthought rather than + a checkstop in the pipeline.

+

ADaPT-ML aims to be the flexible framework upon which researchers + can implement their understanding of the phenomena under study. This + software was created especially for any researcher with:

+ + +

Some programming experience or interest in learning how to + write code based off of examples.

+
+ +

Access to large amounts of unlabelled data that is constantly + changing, such as social media data.

+
+ +

Domain expertise or an intuition about how they would follow + rules, heuristics, or use knowledge bases to annotate the + unlabelled data.

+
+
+

ADaPT-ML takes as much of the development work as possible out of + creating novel models of phenomenon for which we have well-developed + theories that have yet to be applied to big data.

+
+ + Related Work +

An early version of this software supported the modelling of + universal personal values and was complementary to the software + architecture described in Gutierrez et al. + (2021).

+

During the development of this software, + Snorkel progressed into + Snorkel Flow + (Snorkel + AI, 2021), a proprietary MLOps system that incorporates data + cleaning, model training and deployment, and model evaluation and + monitoring into its existing data programming framework. Unlike + ADaPT-ML’s focus on adding new classification tasks by updating + existing and creating new Python modules, + Snorkel Flow allows users to perform complex + operations with a push-button UI, like creating Labelling Functions + from ready-made builders.

+

Neu.ro + (Neu.ro, + 2022) and MLRun + (Iguazio, + 2022) are MLOps platforms that, like ADaPT-ML, orchestrate + open-source tools like Label Studio and + MLflow. However, supporting dynamic and + iterative training data creation through data programming is a core + feature of ADaPT-ML, but frameworks like + Snorkel are not integrated out-of-the-box with + Neu.ro and MLRun. + Additionally, collaboration with the Neu.ro + team is necessary to create sandboxes with the needed tools, whereas + ADaPT-ML is completely open-source and hands-on.

+
+ + Acknowledgements +

We acknowledge contributions from Mitacs and The Canadian Energy + and Climate Nexus / Le Lien Canadien de L’Energie et du Climat for + funding during the early stages of this project’s development.

+
+ + + + + + + + RatnerAlexander + BachStephen H. + EhrenbergHenry + FriesJason + WuSen + Christopher + + Snorkel: rapid training data creation with weak supervision + VLDB Journal + Springer Berlin Heidelberg + 2020 + 29 + 2-3 + 0949877X + https://doi.org/10.1007/s00778-019-00552-1 + 10.1007/s00778-019-00552-1 + + + + + + TkachenkoMaxim + MalyukMikhail + ShevchenkoNikita + HolmanyukAndrey + LiubimovNikolai + + Label Studio: Data labeling software + https://github.com/heartexlabs/label-studio + + + + + + ChenAndrew + ChowAndy + DavidsonAaron + DCunhaArjun + GhodsiAli + HongSue Ann + KonwinskiAndy + MewaldClemens + MurchingSiddharth + NykodymTomas + OgilviePaul + ParkheMani + SinghAvesh + XieFen + ZahariaMatei + ZangRichard + ZhengJuntai + ZumarCorey + + Developments in MLflow: A system to accelerate the machine learning lifecycle + Proceedings of the fourth international workshop on data management for end-to-end machine learning + Association for Computing Machinery + New York, NY, USA + 2020 + 9781450380232 + https://doi.org/10.1145/3399579.3399867 + 10.1145/3399579.3399867 + + + + + + RamírezSebastián + + FastAPI + GitHub repository + https://github.com/tiangolo/fastapi; GitHub + 2021 + + + + + + GutierrezCandelario A. Gutierrez + WhittakerAndrea + PatenioKatherine Mae + GehmanJoel + LefsrudLianne M. + BarbosaDenilson + StrouliaEleni + + Analyzing and visualizing twitter conversations + Proceedings of the 31st annual international conference on computer science and software engineering + IBM Corp. + USA + 2021 + + + + + + Snorkel AIInc. + + Snorkel Flow + 2021 + https://snorkel.ai/platform/ + + + + + + Neu.ro + + Neu.ro + 2022 + https://neu.ro/ + + + + + + IguazioLtd. + + MLRun + 2022 + https://github.com/mlrun/mlrun + + + + + +