Data science and Machine Learning practice have been widely accepted by a large number of companies as a potential source of transforming business decisions and solving complex business problems. But when it comes to deploying machine learning models in production, it becomes a painful job for a data scientist and machine learning engineers to automatically orchestrate the entire data science flow smoothly like a traditional software development and deployment lifecycle and process.

For a generic data science problem, there can be many common parts involved like collecting data, building ETL pipelines, performing exploratory data analysis, fixing data insufficiencies, doing feature…

Mayank Kumar

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