online vs offline machine learning

online vs offline machine learning

Machine Learning (ML) and Artificial Intelligence (AI) are hot topics these days. Now Data Engineers don’t have to write custom codes to manage large scale infrastructures. Machines analyze large amounts of data and create new improved models with little to no human help. These Machine Learning Advancements have helped many industries to scale themselves up to a level that would have taken a lot of effort and time if AI had not been available. Imagine updating Facebook Algorithms without machine learning, the level of effort and time it would take to update those algorithms that machine learning can complete in a single day, there are roughly 2.9 billion Active Users on Facebook as of March 2022 and each of these users have their on personalized news feed and ADS personalization, and these algorithms are capturing more and more data from the user’s daily activities and updating the algorithms for a better ADS experience. All of this is possible due to modern Machine Learning (ML) advancements, not only large scale applications but also small applications have gained a great deal from Machine Learning. People often get confused between online vs offline machine learning and ask what is better for their business, so let’s dive into that.

Online VS Offline Machine Learning, Which Performs Better?

Online and Offline Machine learning are not exactly at opposite ends of the spectrum, but the difference lies in their working method and at some point they relate with each other too. Each have their own pros and cons and this is why we cannot compare the two directly because their performance depends on the processing speed of the processor and other factors. The faster the processing speed, the better the overall performance will be. In offline machine learning, the speed depends on the server of your local computer whereas in online machine learning the performance depends on the speed of the server’s processor. The question is what type of machine learning you should use in what situation, and that’s what we’re going to discuss now in the next section of this blog.

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Type of machine learning you should use

The decision to use online or offline machine learning doesn’t directly depend on your App type, but there are a lot of other factors that are important. When you understand those differences, you can make your mind up about whether to use online or offline machine learning. The first point that you should take into account is if you are going to deploy and launch the app directly or whether you are going to test it first. If you’re going to deploy and publish it right away, then online machine learning should be your first option. If your goal is only to test and improve it over time, then you should go for offline machine learning.

The second thing you should consider is your budget. Machine Learning (ML) requires very high spec computing systems and Cloud Solutions can be costly. For students and startups it is not worth the cost, although there are some solutions for students like the Github Student Pack. However, it can still be a little costly for students who have just started learning Artificial Intelligence (AI) and Machine Learning (ML), so for those students using offline machine learning is a better option and when their app is ready to use, they can then deploy the partial app on the cloud and run machine learning algorithms on their local machine.

You may have now decided the type of machine learning you’re going to use. If you’ve decided to use online machine learning, then you’re good to go but if you have decided to use offline machine learning then here are some more things you should know about offline machine learning.

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Offline machine learning is also called batch learning and the reason it’s called batch learning is because in offline machine learning, data is gathered from the online app and then downloaded to the location where your offline machine learning algorithm is situated then the data is divided into set of batches. These batches are then processed in the algorithm, decisions and changes are made, then the algorithms are updated and the cycle continues for the number of batches that are being created. That’s the reason why offline machine learning is called batch learning too.

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