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5 results for “MLOps”
An Analysis of MLOps Architectures: A Systematic Mapping Study
<p>This repository contains all the data, scripts, and documents that can assist the readers in replicating our research.</p> <p>The following are included in this replication package:</p> <p>The folder ‘data’ includes 4 spreadsheet files containing search results (list of initial papers), snowballing results, data extraction, and data synthesis.</p> <p>The ‘documents’ folder contains the research design of this study. The current submission is only a subset of the designed research. The protocol includes a broader scope seeking to synthesize a comprehensive architecture of state-of-the-art of MLOps systems. The current study however represents the structural view of the architecture, (RQ1) of the protocol.</p> <p>All the scripts that help the interested reader to automate parts of the process can be found in the ‘scripts’ folder.</p> <p>To replicate the process one may consult the protocol.</p> <p><strong>Please note that the protocol describes the design of a broader study and only the results of RQ1 in the protocol are presented in this submission.</strong></p>
A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package
<div> <p><strong>Title:</strong> A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the replication package artefact for the paper entitled "A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures".</p> <p><strong>Paper Abstract:</strong> In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.</p> </div>
UK RSE Conference 2022 Walkthrough - MLOps for RSEs - Sample Data
<p>This record is intended as sample data for a Walkthrough at the UK RSE 2022 Conference titled "MLOps for RSEs". The code that uses this sample dataset can be found on <a href="https://github.com/informatics-lab/ukrse_2022_mlops_walkthrough">GitHub</a>. There are 2 parts based on the sample problems in the walkthrough</p> <ul> <li>Classifying wind rotor events</li> <li>Clustering weather regimes</li> </ul> <p><strong>Rotors Dataset</strong></p> <p>This dataset is intended as a machine learning dataset, to train a model to predict the occurrence of turbulent wind gusts called "rotors". These are wind gusts happening on the leeward side of mountains. When they occur near an airfield, this can be hazardous from aviation operations. This data is intended to be used with the code on the Met Office Data Science Community of Practice GitHub repository.</p> <p>Files:</p> <ul> <li>2021_met_office_aviation_rotors.csv - Raw dataset</li> <li>2021_met_office_aviation_rotors_preprocessed.csv - Preprocessed dataset ready for machine learning</li> <li>rotors_catalog.yml - Intake Catalog file for the rotors dataset.</li> </ul> <p>More Information:</p> <ul> <li>MO Data Science Community of Practice GitHub - https://github.com/MetOffice/data_science_cop/tree/master/challenges/2021_falklands_rotors </li> <li>Met Office Youtube - What are rotors? https://www.youtube.com/watch?v=jgSZG9SqN_s <ul> <li>What are Lee Waves? https://www.metoffice.gov.uk/weather/learn-about/weather/types-of-weather/wind/lee-waves\</li> </ul> </li> </ul> <p><strong>Weather Regime Clustering</strong></p> <p>This dataset is a UK and North Atlantic cutout of the Mean Sea-level Pressure (MSLP) field in <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5 reanalysis dataset</a> produced by ECMWF. This dataset is used for demonstrating an unsupervised learning pipeline.</p> <p>Files:</p> <ul> <li><a href="https://zenodo.org/api/files/58a7da5c-82bf-4d65-92d6-dea0e2efb0e3/era5_mslp_UK_2017_2020.nc">era5_mslp_UK_2017_2020.nc</a> - Gridded dataset of ERA5 reanalysis data.</li> </ul>
MLOps Education - Challenges and Recommendations
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The Practitioners' Side: MLOps Perception and Adoption
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Allen Brain Atlas
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.