Talk Python to Me: #261 Monitoring and auditing machine learning

Traditionally, when we have depended upon software to make a decision with real-world implications, that software was deterministic. It had some inputs, a few if statements, and we could point to the exact line of code where the decision was made. And the same inputs lead to the same decisions. <br/> <br/> Nowadays, with the rise of machine learning and neural networks, this is much more blurry. How did the model decide? Has the model and inputs drifted apart, so the decisions are outside what it was designed for? <br/> <br/> These are just some of the questions discussed with our guest, Andrew Clark, on this episode of Talk Python To Me.<br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Andrew on Twitter</b>: <a href="https://twitter.com/aclarkdata1" target="_blank" rel="noopener">@aclarkdata1</a><br/> <b>Andrew on LinkedIn</b>: <a href="https://ift.tt/2KzDOKS" target="_blank" rel="noopener">linkedin.com</a><br/> <b>Monitaur</b>: <a href="https://monitaur.ai/" target="_blank" rel="noopener">monitaur.ai</a><br/> <br/> <b>scikit-learn</b>: <a href="https://ift.tt/2L0QB8Q" target="_blank" rel="noopener">scikit-learn.org</a><br/> <b>networkx</b>: <a href="https://ift.tt/1mRuKyd" target="_blank" rel="noopener">networkx.github.io</a><br/> <b>Missing Number Package</b>: <a href="https://ift.tt/21P9cEw" target="_blank" rel="noopener">github.com</a><br/> <b>alibi package</b>: <a href="https://ift.tt/3cRz679" target="_blank" rel="noopener">github.com</a><br/> <b>shap package</b>: <a href="https://ift.tt/2rNPHWJ" target="_blank" rel="noopener">github.com</a><br/> <b>aequitas package</b>: <a href="https://ift.tt/30dAzPe" target="_blank" rel="noopener">github.com</a><br/> <b>audit-ai package</b>: <a href="https://ift.tt/2sDaqdd" target="_blank" rel="noopener">github.com</a><br/> <b>great_expectations package</b>: <a href="https://ift.tt/2Ge2Bzv" target="_blank" rel="noopener">github.com</a><br/></div><br/> <strong>Sponsors</strong><br/> <br/> <a href='https://ift.tt/3aBjB2k> <a href='https://ift.tt/3eMZyki Weekly Python Exercises</a><br> <a href='https://ift.tt/2PVc9qH Python Training</a>

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