Here is advice on getting started with doing data analysis in Python and I thought it might benefit others if published here. This is for someone new to Python that’s looking for the easiest path from zero to one. Here’s a quick summary of the important libraries you’ll interact with frequently. You will likely always need to refer to the documentation for whatever library you’re using, so just keep it open in your browser.
As more and more systems leverage ML models in their decision-making processes, it will become increasingly important to consider how malicious actors might exploit these models, and how to design defenses against those attacks. There’s a continual arms race between attacks and defenses. So what’s an average ML practitioner to do, who likely doesn’t have time to stay on the very cutting of ML security literature? The purpose of this post is to share some of my recent learnings on this topic.
When it turns out that the decisions that are based on a model are bad, it evokes a few obvious questions: Is my model broken? Is my data pipeline broken? Has the thing I’m modeling changed? Answering these questions should be no harder than looking at a dashboard. People tend to rebuild from scratch instead of reverse engineer. Here are the 5 important things that will keep your work robust and relevant, while saving you lots of time that would otherwise be wasted on unnecessary operational firefighting.