> For the complete documentation index, see [llms.txt](https://playbooks.equalexperts.com/mlops-playbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://playbooks.equalexperts.com/mlops-playbook/principles/evaluate-and-monitor-algorithms-throughout-their-lifecycle.md). # Evaluate and monitor algorithms throughout their lifecycle ML solutions are different from standard software delivery because we want to know that the algorithm is performing as expected, as well as all the things we monitor to ensure the software is working correctly. In machine learning, performance is inherently tied to the accuracy of the model. Which measure of accuracy is the right one is a non-trivial question - which we won’t go into here except to say that usually the Data Scientists define an appropriate performance measure. **This performance of the algorithm should be evaluated throughout its lifecycle:** * During the development of the model - it is an inherent part of initial algorithm development to measure how well different approaches work, as well as settling on the right way to measure the performance. * At initial release - when the model has reached an acceptable level of performance, this should be recorded as a baseline and it can be released into production. * In production - the algorithm performance should be monitored throughout the lifetime to detect if it has started performing badly as a result of data drift or concept drift. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://playbooks.equalexperts.com/mlops-playbook/principles/evaluate-and-monitor-algorithms-throughout-their-lifecycle.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.