DevOps for Data Science

✔️DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is complementary to Agile software development.
✔️Data Science is a discipline relying on data availability, while business analytics does not completely rely on data. Data Science covers part of data analytics, particularly that part which uses programming, complex mathematical, and statistical. it is not completely overlapping Data Analytics but it will reach a point beyond the area of business analytics.
📚 Read our blog on DevOps for Data Science at https://k21academy.com/dp10032 that will help you to know about the importance and usage of DevOps in Data Science field. The topics covered are:
✏️ What is DevOps?
✏️ What are the Fundamentals Of DevOps?
✏️ DevOps Lifecycle
✏️ What is Data Science?
✏️ Data Science Lifecycle
✏️ Why do Data Scientists and DevOps need to know about Each Other?
✏️ How does DevOps support the deployment of data models?
✏️ What is MLOps?
✏️ Is DataOps the DevOps of the Future? How does MLOps feature in this narrative?
✏️ DevOps vs Data science – how these work or give benefits.
✏️ Conclusion.

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