Emerging practices in MLops / DataOps
An overview of current and emerging trends in MLOps/DataOps
About this Report
In this report, we will explore the different practices and approaches used by developers involved in data and machine learning (ML) operations (DataOps/MLOps), and how these practices are used together. We will also look at how differentDataOps/MLOp practices dictate the use of different backend and ML technologies, as well as ways of storing data. Finally, we will show how developers' motivations and target audiences are impacted bytheirDataOps/MLOp practices.
Key Questions Answered
What MLOps/DataOps practices are developers using, and using in combination?
What are the differences in MLOps/DataOps practices between developers in different sectors?
What impact does how the data is stored have on MLOps/DataOps practices?
How does the amount of data ML/data scientists use for training their models have on their choice and use of MLOps/DataOps practices?
How does the motivation of those involved in ML/AI have on their choice and use of MLOps/DataOps practices?
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Methodology
Data for the report comes from the 23rd edition of our Developer Nation survey, which ran between June 2022 and August 2022.
