jesussantana/DeepLearning.AI-Introduction-to-Machine-Learning-in-Production

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

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Emerging

This project provides a structured learning path and code examples for anyone building and deploying machine learning models in real-world applications. It guides you through designing an end-to-end system, from initial problem scoping and data preparation to model deployment and continuous improvement. Machine learning engineers, data scientists, and MLOps practitioners will find this useful for operationalizing their models effectively.

No commits in the last 6 months.

Use this if you need to understand how to move a machine learning model from a development environment into a robust, continuously improving production system.

Not ideal if you are solely focused on developing model algorithms and do not need to learn about their deployment and lifecycle management.

MLOps machine-learning-engineering data-science-workflow production-systems model-deployment
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

10

Forks

3

Language

Jupyter Notebook

License

MIT

Last pushed

Nov 04, 2021

Commits (30d)

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