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Creating a good machine learning model is really sexy. That& #39;s what& #39;s different and where everyone focuses all of their attention.
But machine learning is much more than that.
A thread with a few thoughts about the real job.
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But machine learning is much more than that.
A thread with a few thoughts about the real job.
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Machine learning engineers spend a lot of time designing and training new models, but this is just a small fraction of their job.
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In reality, dealing with data and operationalizing models is much more time-consuming and sometimes even harder and more involved than creating the models in the first place.
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The ultimate goal of any project is to provide value, and a model is just a piece of the entire puzzle.
Making that piece useful involves pulling together many different skills that machine learning practitioners bring to the table.
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Making that piece useful involves pulling together many different skills that machine learning practitioners bring to the table.
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Let& #39;s see some of the things you should expect to find on every project:
1. Define the business case for the problem you need to solve.
2. Determine the success criteria you’ll evaluate to understand whether your solution offers the expected value.
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1. Define the business case for the problem you need to solve.
2. Determine the success criteria you’ll evaluate to understand whether your solution offers the expected value.
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3. Determine which data you will use based on its availability and usefulness.
4. Come up with a plan to remediate any biases in the existing data.
5. Build a pipeline to capture, analyze, transform, and manage that data.
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4. Come up with a plan to remediate any biases in the existing data.
5. Build a pipeline to capture, analyze, transform, and manage that data.
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6. Design, train, validate, and test any models you need to solve the problem.
7. Glue together and deploy models and components into a comprehensive solution.
8. Assess any biases in the final solution and come up with ways to remediate them.
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7. Glue together and deploy models and components into a comprehensive solution.
8. Assess any biases in the final solution and come up with ways to remediate them.
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9. Monitor the solution to identify whether the model is performing as expected.
10. Design and implement a retraining pipeline to keep the model up to date.
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10. Design and implement a retraining pipeline to keep the model up to date.
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The list is not comprehensive, but it shows the breadth required to complete a valuable solution that users of the model can directly benefit from.
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Originally, I posted this thread as an article in Medium. Follow me at https://svpino.medium.com/ ">https://svpino.medium.com/">... if you enjoy this type of content.
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You can also subscribe to my free newsletter for a somewhat fresh perspective on practical machine learning every Friday: http://digest.underfitted.io"> http://digest.underfitted.io .
