DevOps assists the data scientist and information technology to work together. In DevOps, developers work under a chain of command. And always try to obtain a product feature as soon as possible. Data scientists think this as altering all the variables and structure of the model.
Generally, data scientists develop data models that are required to run in a production platform. Most of the DevOps works are implemented on production-based data science applications. But these methods are underrated in data science training. Most of the companies do not want to invest in the data science sector. Some other companies offer a small team to deal with basic operations because they are not aware of DevOps’ importance and practice it in data science. DevOps can be implemented in the data science workflow with a better understanding and few tunings in the DevOps tutorial.
Ways to separate data science and DevOps
To conceptualize the handoffs between product engineering, DevOps, and data science, a simple architecture called Model-API-Client is needed:
• Model: A trained model with a function that engineers can easily use without data science expertise.
• API: Infrastructure layer that takes a trained model and transforms it as a web.
• Client: applicants who interact with the web service.
In the first phase, data scientists train and export a model, including functions for generating and filtering predictions from the model.
Then it goes to the second phase-API. In this phase, it is wholly the responsibility of DevOps. To DevOps, it’s just a Python function. It needs to be turned into a containerized, microservice, and deployed. Once the model microservice is live, it becomes a web service to the engineers—the query is like any other API.
The Model-API-Client is not the only way to separate engineering and data science. But it provides a better way to draw a line between DevOps and data science without building an expensive platform and introducing extravagant overhead.
Data science adds additional responsibilities to DevOps. But to enjoy the highest benefits from ML (machine learning) paradigm, both developers and operators have to work together with data scientists.
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