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Why MLOps is every data scientist’s dream? Part 1

We will try to provide some answers to this questions in two parts. This first article focuses on conception, data collection, exploration and application prototyping while the second one focuses on the first deployment of the solution and iterations to quickly improve it.

Improve your ML workflows with synthetic data

As a data scientist, you know that high-performance machine learning models cannot exist without a large amount of high-quality data to train and test them. Most of the time, building an appropriate machine learning model is not a problem: there are plenty of architectures available, and since it is part of your job, you know exactly which one will best suit your use case. However, having a large amount of high-quality data can be much more challenging: you need a labeled and cleaned dataset matching exactly your use case. Unfortunately, such a dataset is usually not already available. Maybe you only have a few data matching your requirements, maybe you have data but they are not matching exactly what you want (they can have biases or unbalanced classes for example), or maybe a dataset exists but you cannot access it because it contains private information. Therefore, you need to collect new data, label them and clean them, which can be a time-consuming and costly process, or even not be possible at all.