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Results of the Survey: 📝 How is MLOps more than just tools?
As some of you may recall from our previous posts, TheSequence recently conducted a survey titled “How is MLOps more than just tools?” In this survey, we asked ML engineers, data scientists, AI practitioners, researchers, project managers, and anyone else involved in the field to share their thoughts on how MLOps is evolving across the AI industry at the beginning of 2023.
To remind everyone, MLOps is a set of guiding principles that facilitate ML pipeline optimization and interconnectedness between different ML stages. The ultimate goal of MLOps is to reduce product-to-market time without sacrificing performance.
Typically, MLOps is divided into three key components – culture, practices, and tools.
Well, the results are in! First of all, we’d like to thank everyone who contributed to the survey – we very much appreciate your participation. Secondly, we’ve since processed and aggregated all of the responses, distilling them into the most revealing facts and statistics that are now available in a succinct, easy-to-understand format.
The survey’s results point to the fact that the MLOps culture and practices are still lagging behind, while there’s a disproportionate influx of technical solutions. In addition, AI product developers tend to focus on ML models, while often overlooking quality data. Lastly, no environment currently exists that addresses all stages of the ML value chain and supports the entire AI product lifespan.