English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Following continuous software engineering practices, there has been anincreasing interest in rapid deployment of machine learning (ML) features,called MLOps. In this paper, we study the importance of MLOps in the context ofdata scientists' daily activities, based on a survey where we collectedresponses from 331 professionals from 63 different countries in ML domain,indicating on what they were working on in the last three months. Based on theresults, up to 40% respondents say that they work with both models andinfrastructure; the majority of the work revolves around relational and timeseries data; and the largest categories of problems to be solved are predictiveanalysis, time series data, and computer vision. The biggest perceived problemsrevolve around data, although there is some awareness of problems related todeploying models to production and related procedures. To hypothesise, webelieve that organisations represented in the survey can be divided to threecategories -- (i) figuring out how to best use data; (ii) focusing on buildingthe first models and getting them to production; and (iii) managing severalmodels, their versions and training datasets, as well as retraining andfrequent deployment of retrained models. In the results, the majority ofrespondents are in category (i) or (ii), focusing on data and models; howeverthe benefits of MLOps only emerge in category (iii) when there is a need forfrequent retraining and redeployment. Hence, setting up an MLOps pipeline is anatural step to take, when an organization takes the step from ML as aproof-of-concept to ML as a part of nominal activities.

Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?