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Continuous Delivery for Machine Learning Applications with Open Source Tools
Automate machine learning app development and deployment with open-source tools like DVC and Git. Learn how to orchestrate efficient pipelines, integrate data lakes, and improve model quality with continuous delivery and experimentation best practices.
- Automate the entire development and deployment process for machine learning applications
- Use open-source tools for version control and automation, such as DVC and Git
- Implement a continuous delivery pipeline for machine learning products
- Use a data lake as the main infrastructure to store and serve models
- Version control the data and artifacts using MD5 hashes and Git
- Automate data integrity checks and data cleaning processes
- Implement a testing pyramid with unit tests, integration tests, and end-to-end tests
- Orchestrate all processes with continuous integration and delivery
- Use a chatbot to automate simple tasks and provide customer support
- Monitor and observe theproduction system to identify bugs and improve the model
- Use A/B testing and experimentation to validate model improvements
- Continuously deliver and deploy new models and features to production
- Involve cross-functional teams in the development process to ensure collaboration and feedback
- Implement a culture of continuous delivery and experimentation