DataOps, in my opinion, is synonymous with the Data Engineering Lifecycle. It represents a culture that integrates and manages the lifecycle in a lean and agile manner, inspired by DevOps and Product thinking.
Sourced from: The Rise of DataOps. Have we found a fix for today’s data… | by Prukalpa | Sep, 2022 | Towards Data Science
# Resources
Insightful readings:
# Comparing MLOps and DataOps
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Both MLOps and DataOps share several aspects:
- Collaborative workflow: Both embrace a philosophy of harmony and speed through cross-departmental collaboration.Automation: They aim to automate processes in their respective pipelines, from data preparation to reporting in DataOps, and from model creation to deployment and monitoring in MLOps.Standardization: DataOps standardizes data pipelines, while MLOps standardizes ML workflows, establishing a common language for stakeholders.
Key Differences:
- They address distinct questions and goals in the machine learning lifecycle, requiring unique expertise and tools.DataOps can exist independently of MLOps, focusing on data extraction and transformation, while MLOps inherently relies on data operations.DataOps applies across the entire data application lifecycle, whereas MLOps focuses on managing and deploying machine learning models.The primary goal of DataOps is to streamline data management, accelerate market delivery, and ensure high-quality outputs. In contrast, MLOps centers on facilitating ML model deployment in production environments.Source
# DataOps and DevOps: A Comparison
Exploring thehttps://www.ssp.shhttps://www.ssp.sh/brain/devops.pngOps:
Also related GitOps.
Origin:
References:
Created 2022-05-22
