Welcome to Data Science and Machine Learning Failures
This is a place to talk about what happens when things go wrong with data science and machine learning. Yes, things go wrong, horribly wrong, and better to learn from our mistakes than to keep repeating them.
Yes, this encompasses the ethics, safety, and inclusivity considerations (dive deeper into these with Actionable AI Ethics), but it will also takes a look at some of the other failures that occur at various stages of the AI lifecycle from the ideation and conception to the deployment and maintenance of a project.
Engineering is experimental and the scientific aspect of it comes from us iterating on what we learn not only from our own work but also from the work of others. (Well, hopefully we create less of a mess but you get the picture.)
The motto for this endeavour is that we can all learn more by standing on the shoulders of giants!
This endeavour is meant to do the following:
Accelerate your data science and machine learning journey by learning from common mistakes.
Gain actionable insights that help you ace your next job interview or promotion discussion at work.
Cross-pollinate ideas from people at various stages in their careers and types of organizations.
Finally, not being afraid to fail is one of the redeeming qualities of working in data science and machine learning. We are literally called upon to create something new that hasn’t been attempted before, wading into darkness trying to find the gem that will unlock new insights and move the entire field forward.
So let’s learn together!
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You will also have the opportunity to interact in the comments with the rest of the community and gain access to exclusive posts where I speak with practitioners from research labs, startups, SMEs, and corporations. You also get access to the audio versions of these conversations that you can listen to on the go.
Some of the open content will also be cross-posted on Data Science and Machine Learning Failures.
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