Microsoft’s Machine Learning.NET (ML .NET) is free of cost. It is an open source framework that contains more than 40 diverse traditional machine learning learners for the Machine Learning tasks. For example, there are as several learners for classification, clustering, regression, recommendation, classification, ranking and a lot more. Additionally, it even includes the integration to TensorFlow, which is absolutely perfect for Deep Learning cases like the Image Classification modeling. This fabulously developed ML library for C# programming language is becoming more and more popular these days. Also, the preview release of ML.NET contained things like n-gram creation. The learners to manage the binary classification were also included in it, along with the learners to handle the regression tasks. In the future versions of ML.NET a lot more will be added.
Exploring ML.NET
ML.NET was basically created by Microsoft to allow the users to make the most of the capabilities of ML in C#. The package offers all the elementary ML functionalities. ML.NET is nothing but just a library. Also, it is a collection of tools that enable the .NET developers to develop and integrate ML into their Asp.net application development. With it, you would be able to develop classification, regression, forecasting modeling, recommendation and a plenty of other things. With the help of the correct codes, you would be able to develop ML models in ML.NET. In order to generate the codes, you would have to use the command line tools, and the codes will be generated automatically.
Kick off your ML.NET journey
Starting your ML.NET journey isn’t very tough. All you have to do is, follow some steps to get started. Though, it is always suggested to first study everything about ML.NET before starting. Also, you may want to spend time on learning the features, knowing some use cases etc. before starting to use ML.NET. However, when you decide that you want to use ML.NET, then you would be required to get the NuGet package, which is known as Microsoft.ML.
After you receive the package, all you have to do is upgrade the build properties to target x64. This is done only because ML.NET doesn’t support x32.After you make the change, you would be able to initiate the process of building your very first ML pipeline. It will be needed to develop the model. Though, before getting started, you may want to gain a bit more knowledge about the data. After that, it will be easier to start making the models.
Getting to know more about ML.NET
Here’s how ML.NET works, mostly in 4 phases:
- Data Loading: High volume of data has to be fed in the ML.NET system to get started. As, only when you will have a huge bundle of data, then only there are higher chances that you will get apt predictions. In order to train the model data is very important. You can add data for train as well as test by Text using easy processes.
- Train: After the data is loaded, then the next step is to train the data. For training the data, you would be required to choose an ideal algorithm that is perfect for training the model as per your requirements. It is very important for any user to pick the right algorithm, in order to get the best possible and most correct predictions.
- Evaluate: Afterwards, you would be required to evaluate the data. For that, you would have to choose the suitable ML type for model training and prediction. In case you would want to work with segment, then in that case, you would be asked to Clustering model. And, in case you require the price of stock prediction, then in that case you would be able to choose Regression. Whereas, when it comes to the sentiment analysis, then in that case, you would be required to go for the Classification model. Therefore, choosing the model is extremely important and it is based on the prediction type.
- Predicted Results: Now, this is the end of the story. The last step or phase is to get the predictions you require. Only after the data is trained and tested, you would get the final prediction. All these predictions will be showcased with the help of the ML.NET app. Additionally, the trained model can be easily integrated with the other .NET apps. And, it will be saved as binary format.
Business should adopt ML.NET as it is easy to use
ML.NET is not the toughest tool to use. Rather, it is quite easy, but, most importantly, you would have to get the hang of it. Only after you understand the ways to use, then only you would be able to get the most out of it. ML.NET mostly makes use of the pipeline concept for stringing transformation, data-loading and a lot more. Also, it uses the pipelines for learning the stages collaboratively in a single ML sequence. Only after you use the Fit() method, the program will start training the ML model on data.
Read:Exploring the world of microservices using gRPC with Asp.Net Core 3.0
Machine Learning is one of the coolest technologies and anything related it creates a buzz in the industry, including ML.NET. Additionally, Machine Learning and everything related to it seems to have a bright future ahead, therefore, the adoption will continue to grow. ML.NET is a complete package which is developed to enable the users easily develops and makes the most of the ML models within .NET. The complete process of using ML.NET is not at all tough; it is used that you would have to understand it thoroughly. Also, Microsoft will keep evolving it so that it meets the needs of the ever-changing demand of the businesses. So, whether you want to develop a high-end Credit Card Fraud detection model or anything else, then ML.NET is surely your perfect choice as it will make it a cakewalk for you to develop the model. However, you should definitely focus on learning not just ML.NET, but the overall use and advantages of Machine Learning. And, businesses will surely reap a lot of benefits from the appropriate use of Machine Learning.
