The world moving towards Artificial Intelligence (AI) / Machine Learning (ML) side day by day. The importance of ML engineers is also noticeable. The data shows, every year many giant companies investing some portion of their profit to the AI/ML side for being an equal part of the AI/ML bright future. As we know Python is one of the victorious part of ML implementation. Using the python language and many help full ML algorithms we can implement ML logic very smoothly in contrast to other languages. Statistical data shows, 69% of people, those working as ML developers and data science engineers are using python for development.
But the thing is, we are .NET developers and we do have not much knowledge about python, but still we want to execute ML, then how we can?
Simply the answer is using the ML.NET framework. ML.NET is a blessing for .NET developers because without moving to new technologies or languages we are able to execute ML with our own fascinating language.
So, Let’s talk more about this amazing thing…
So now those are working as a developer but don’t aware of Machine Learning (ML) for those first let’s see what is ML then we will move forward to ML.NET?
What is Machine Learning (ML)?
ML is a study of machines algorithm we can say that it will learn itself by experience. If we talk about proper definition we can say Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. Now the question is so where we can use this or which are the application in current time those are using ML so the answer is there are many applications are available in the market but some of them such as in medicine, email filtering, speech recognition, and computer vision, etc.
What is ML.NET?
Now let’s see ML.NET, which is an open-source and cross-platform machine learning framework that we can use for ML development using .NET. Using ML.NET, we are able to create practice ML models using coding languages like C# or F# and this whole process will be done without leaving the .NET ecosystem so it’s a good thing right? Main the plus point behind using ML.NET is it allows .NET developers to re-use thair knowledge, skills, code, and libraries that they already know as .NET developers so using that existing knowledge plus some overview of ML.NET they are also able to integrate ML into any web, mobile, or any IoT based thing without leaving the .NET ecosystem.
Scenarios for using ML.NET
Now the question for which different scenarios we can use ML.NET. So basically, we are able to use ML.NET for many scenarios, in that,
- Sentiment analysis
- Product recommendation
- Price prediction
- Customer segmentation
- Object detection
- Fraud detection
- Sales spike detection
- Image classification
- Sales forecasting and Many more!
As your first step towards ML.NET let’s see the installation process for ML.NET. There are only 3 to 4 steps to install the ML.NET framework.
- So first is you have to open the Visual Studio Installer over there you have to Modify the instance of Visual Studio.
- Now in the second step in that opened screen, you are able to see the option ML.NET Model Builder (Preview) that you have to choose as you can see this option in the below picture as well.
In order to use this feature, you must have to enable this in your visual studio because as of now ML.NET Model Builder is a Preview feature.
Now we have to Enable Preview Feature, So for that
- In Visual Studio, first, go to Tools then open Options in that go to Environment and at the last over there you will able to see Preview Features that you have to enable.
- Check Enable ML.NET Model Builder. We can see it in the below image.
Wohhhhh, your ML .NET framework is successfully installed to your system so now you can make a console app using ML.NET.
Now some of having questions like that is fine that we can implement ML logic without leaving the .NET ecosystem but what about speed? Can we archive good processing speed as compared to python or other Machine Learning (ML) technologies?
Let’s go through some statistical data to understand the strongness of ML .NET over other ML-related technology, if you use 9GB Amazon review data set, ML.NET will give you 95% accuracy with the analysis model of that data set and 95% is not a bad accuracy I think. If we talk about other popular machine learning framework-based technology so it is failed to process the whole big dataset due to memory limitations so it will generate memory error.
The allover performance evaluation of different ML Based frameworks finds similar results in some scenarios, like click-through rate prediction and flight delay prediction. so I think it is clearly showing that if you are happy with other popular machine learning framework speed processing then definitely you like ML.NET as well because ML.NET is quite high with processing performance.
This was all about ML.NET. I want to conclude this blog with just 2 or 3 more sentences and that is if you want to work with Machine Learning (ML) without leaving the ecosystem of .NET then ML .NET is only for you. With the same skill set that you are using as a .NET developer, you will be able to work with ML as well. Happy Learning