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Welcome back! This will be the second article in this series on MLOps. Previously, we briefly looked at challenges faced in production and some simple solutions to tackle these challenges. If you haven’t got the chance to look at my previous article, you can check it out here.

Now, as we had discussed previously, the production part can roughly be divided into 4 phases. In this article, let’s explore these phases and what each of them really means.

1. Scoping

Scoping helps determine feasible solutions to a problem, put very simply. Let’s consider an example wherein you are working on a handwriting…

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It’s safe to say that a majority of us starting out with Machine Learning/Deep Learning are able to easily develop basic models and also achieve decent results. However, speaking from personal experience, we rarely have any sort of exposure to the deployment of our models, especially if we’ve had little to no experience with frameworks like Flask, Django, etc.

Many machine learning courses go over the basics of algorithms, code snippets, and libraries needed to tackle certain types of problems. However, I had not particularly found any courses specifically for production-related topics. That was until DeepLearning.ai released their specialization on…

Praatibh Surana

NLP | Research | MLOps | Linkedin : https://www.linkedin.com/in/praatibh-surana-912045196/

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