A curated list of publicly available and community-contributed resources to learn MLOPS.

This repository is focused on people who want to start with MLOPS from a DevOps background.

MLOPS is a practice followed to develop and deploy machine learning applications.

MLOPS = DevOps + Machine Learning

If you follow DevOps culture and practices for ML projects, you can call it as MLOPS

MLOPS is different from DevOps in the same areas how a Machine learning development is different from a traditional software development process.

As Devops engineers, we understand the complete life cycle of an application from development to production. It includes CI/CD, logging, monitoring, alerting, etc.

In the same way, for MLOPS, a DevOps engineer should understand the ML application lifecycle and core concepts around it. It enables the DevOps engineer to collaborate when multiple ML teams are involved.

Following are the key teams involved in MLOPS

1. Data Engineers
2. Data Scientists
3. Software Engineers
4. Machine Learning Architects
5. DevOps engineers

1. Why is DevOps for Machine Learning so Different?
2. Needs for DevOps for ML Data
3. MLOps and DevOps: Why Data Makes It Different

1. How Does Machine Learning Work?
2. Machine Learning Visual Explanation
3. Understanding ML Algorithm & Model

1. 150 Data Engineering Concepts

1. AWS Sagemaker Studio

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