In this tutorial, we'll learn How To Effectively Manage Deployed Models. It's a pity if you miss this great article
Most models never make it to production. We previously looked at deploying Tensorflow models using Tensorflow Serving. Once that process is completed, we may think that our work is all done. In actuality, we’ve just started a new journey of managing our model’s lifecycle and making sure it stays up-to-date and effective.
Like most things in software, there is a need for continuous development and improvement. The task of managing a model once it is deployed is one that is often overlooked. Here we’ll look at ways to do this effectively and make our model pipelines more efficient.
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
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Many professionals and 'Data' enthusiasts often ask, “What's the difference between Data Science, Machine Learning and Big Data?”. Let's clear the air. If you are still wondering about it then this article is for you.
5 stages of learning Data Science and how to ace each of them
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant