When you install Machine Learning (ML) in your app, its main purpose is to improve certain tasks of the app without any external human instructions, but when you think about installing ML in your app there are a lot of things that you need to understand.
Most people confuse machine learning with automation but they are two completely different things and automation existed long before machine learning and tech.
Automation refers to the simplification of a specific task, whereas machine learning refers to collecting data, analyzing it, and making changes to improve the Application.
It’s not as simple as it sounds but there are a whole lot of things going on behind it. So, if you’re someone who is interested in Machine Learning, you should know the basics of Machine Learning and how it works: the term batch learning, online vs offline machine learning and much more.
If you’re still reading this article, then this is a great sign of your interest in machine learning and, as a result, you may know what Machine Learning actually is.
I’m not going to bore you with the basics, instead let’s talk a little about advanced Machine Learning concepts in simple, easy-to-understand language in case you’re not a super technical person.
Let’s start with the types. There are two main types of Machine Learning, Online Machine Learning and Offline Machine Learning.
People often confuse Batch Learning and Offline Machine Learning as two different concepts but that’s actually not true.
Offline Machine Learning is the same as Batch Learning and in this article I’ll also explain why Offline Machine Learning is called Batch Learning, but before that we need to understand the basic difference between Online and Offline.
When you install a Machine Learning algorithm in your app, the algorithm works on the runtime, the users are using the app and the algorithm is collecting data, the data which it analyzes for different possibilities and making decisions, changes in the app to increase its usability for users.
The whole process is happening on the live servers, but the issue is that sometimes it can be very expensive because Machine Learning servers are not cheap.
Online Machine Learning requires some high specs processing units, even if you’re doing some simple calculations. If you do not invest in good spec solutions, it can slow down or ruin your app.
Now that you understand a lot about Online Machine Learning, it’s time to read about Offline Machine Learning. As we discussed, some drawbacks of Online Machine Learning include high costs and high spec servers that not everyone can afford.
If you’re a student learning ML or a startup that’s testing Machine Learning, Online ML is not the ideal choice for you. So, to overcome these drawbacks, many people turn to the conventional method of Machine Learning that is Offline Machine Learning.
Offline Machine Learning algorithms work by creating the algorithms and then collecting, analyzing and making decisions.
Offline Machine Learning needs data to be downloaded from the live server to the local server and then that data is analyzed, the algorithms are updated and then the new algorithms are uploaded to the server, that’s the basic process of Offline Machine Learning. Depending on the type of app, you can change the frequency of when to download data and when to upload it.
As Offline Machine Learning has a number of benefits. However, it does also have some drawbacks too, and the main drawback is you cannot process big chunks of data in your local processing machines and the frequency of changing data on the servers can be a bit time-intensive as well.
Yes, Offline Machine Learning and Batch Learning are the same things and the reason why Offline Machine Learning is called Batch Learning is because when the data is downloaded from the live server to be processed, that data comes in batches.
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Different batches are downloaded, analyzed on the local server and these batches are then uploaded to the live servers. That’s why Offline Machine Learning is also called Batch Learning.