Continuous Delivery for Machine Learning

Continuous Delivery for Machine Learning

This article will look at how Continuous Delivery that has helped traditional software solve its deployment challenges be applied to Machine Learning.

Table of contents

What is Continuous Delivery?

Continuous Integration

Continuous Delivery vs. Continuous Deployment

Machine Learning Workflow

How does Continuous Delivery help with ML challenges?

Data Management

_- [Automated Data Pipeline_](https://towardsdatascience.com/continuous-delivery-for-machine-learning-8770390db18c#df85)

Experimentation

_- [Training Code_](https://towardsdatascience.com/continuous-delivery-for-machine-learning-8770390db18c#ffb7)

_- [Training Process_](https://towardsdatascience.com/continuous-delivery-for-machine-learning-8770390db18c#40f1)

Production Deployment

_- [Application Code_](https://towardsdatascience.com/continuous-delivery-for-machine-learning-8770390db18c#9737)

Bringing it all together

People

Conclusion

Most of the principles and practices of traditional software development can be applied to Machine Learning(ML), but certain unique ML specific challenges need to be handled differently. We discussed those unique “Challenges Deploying Machine Learning Models to Production” in the previous article. This article will look at how Continuous Delivery that has helped traditional software solve its deployment challenges be applied to Machine Learning.

machine-learning devops4ml continuous-delivery mlops data-science

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