The Growing Significance Of DevOps For Data Science

Building machine learning models is fundamentally different from traditional application development. The development is not only iterative but also heterogeneous. Data scientists and developers use a variety of languages, libraries, toolkits and development environments to evolve machine learning models. Popular languages for machine learning development such as Python, R and Julia are used within development environments based on Jupyter Notebooks, PyCharm, Visual Studio Code, RStudio and Juno. These environments must be available to data scientists and developers solving ML problems.