From the course: Mistakes to Avoid in Machine Learning

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Assuming deployment is a breeze

Assuming deployment is a breeze

From the course: Mistakes to Avoid in Machine Learning

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Assuming deployment is a breeze

- You've put in all the work pre-processing your data, performed feature engineering, you've selected, evaluated, and tuned your model, and your customers love the work. Now you're asking yourself, how exactly do I deploy this model? A word to the wise. Don't assume that your model deployment will be a breeze. Depending on your use case, the deployment could be complicated, and you might find the work you did doesn't really translate to a production environment. So here's what I want you to do to make sure your deployments go as smoothly as possible. Start with the end in mind. I encourage you to plan deployment strategy from day one. At the very beginning of your project, think about deployment. This will help illuminate the limitations you'll need to consider when you are creating your model. For example, if you're planning for real-time predictions in your deployment, check to see if all the data you model on will…

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