This repository contains multiple examples concerning DataRobot's Machine Learning Operations (MLOps) solution. The scripts can be followed end to end and customised depending on the problem you are trying to solve.
For each respective guide, follow the instructions in its own .ipynb or .py file. There will also be a requirements.txt file in each folder with instructions on how to create an environment to run everything successfully.
Some of the notebooks can also be executed through Google Colab.
- To learn to use DataRobot, visit DataRobot University
- For General articles on DataRobot and news, visit DataRobot Community
- End to end DataRobot API examples Tutorials for Data Scientists
- DataRobot API examples Examples for Data Scientists
- MLOps Agent Notebook: An example of how you can use DataRobot's MLOps Agents functionality to monitor external deployments. Python
- MLOps DRUM Notebook: An example of you can use the DataRobot Model Runner (DRUM) library to test your custom models before deploying them using DataRobot. Python
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Readmissions: 3 examples of custom models using the readmissions dataset. Python
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Insurance Pricing: An example of using DRUM on an insurance pricing dataset. Python
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Boston Housing: An example of using DRUM on the Boston Housing dataset. Also includes monitoring with MLOps Agents. Python
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Cats and Dogs: An example of using DRUM on cute cat and dog images! Python)
Each project folder contains its own instructions on setup and requirements. Furthermore, instructions are also conveniently added to the scripts themselves so that users do not need to share the readme file.
If you'd like to report an issue or bug, suggest improvements, or contribute code to this project, please refer to CONTRIBUTING.md.
This project has adopted the Contributor Covenant for its Code of Conduct. See CODE_OF_CONDUCT.md to read it in full.
Licensed under the Apache License 2.0. See LICENSE to read it in full.