DataOps Introduction

Common objective to deliver the data-driven personalization by Using DataOps:

DataOps steps:

What is DataOps?

DataOps is a solution to many of the challenges faced by data science and
analytics once you realize that converting raw data into useful data products can be treated as an end-to-end assembly line process that requires high levels of collaboration, automation, and continuous improvement.

DataOps is a combination of data analytics, Lean thinking, agile practice, and DevOps culture:

DataOps aims to benefit multiple data consumers through data-use cases from the simple data sharing to the full spectrum of data analytics popularized by the Gartner model: descriptive, diagnostic, predictive, and prescriptive. It brings together self- contained teams with data analytics, data science, data engineering, DevOps skills, and line of business expertise in close collaboration. The goal of DataOps for data science is to turn unprocessed data into a useful data science product that provides utility to customers through a rapid, scalable, and repeatable process. A data science product incorporates data science into the operation of a service or product. It is not a one-off project. A data science product is in continuous production with constant monitoring, iteration based on experimentation, and feedback that leads to improvement. It has an owner, it is reproducible, and it solves an end goal. Users and machines can interact with data science products in multiple ways such as APIs, visualizations, or even web or mobile app interfaces.

What Is It Not?

Who Is It For?

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