An Ultimate Guide To Integrate AI and ML with .NET Applications

Quick Overview:
For over two decades, numerous businesses have preferred .NET applications for their exceptional speed, stability, security, and scalability. But, now, maintaining a position in the market is getting complex, and the stakeholder requirements are also changing frequently. To mitigate the gaps in operations and fulfill changing requisites, organizations need to integrate AI and ML with their dot NET applications.

In addition, they look to hire .NET developers who know AI and ML integration with Microsoft’s open-source framework. Therefore, learning about embedding AI and ML capabilities in a .NET application is the need of the hour. And here, you will gain all the essential knowledge about it.
So, let’s get started.

How To Integrate AI and ML into .NET Applications?

To leverage .NET applications with AI and ML functionalities, you must follow the below procedure :

1. Discover and Define the Use Case
2. Gather Relevant Data From Multiple Sources
3. Select the ML and AI Library
4. Develop the Application and Train the Model
5. Integrate .NET Application and ML/AI Model
6. Constantly Monitor, Update, and Train

Step 1: Discover and Define the Use Case

To begin with the AI and ML implementation, firstly, you should define the project purpose and goal. It will offer you a clear insight into the appropriate use case of your .NET application. For instance, you will know whether you need ML or AI for implementing chatbots, virtual assistants, image processing, decision-making, automation, or anything else.

Step 2: Gather Relevant Data From Multiple Sources

A machine learning algorithm needs to get trained before you deploy it in the real world. So, you must collect and organize a wide range of datasets for the ML application. Further, verify the facts and all other information present in datasets for relevancy, timeliness, and accuracy.

Step 3: Select the ML and AI Library

Select the appropriate .NET-compatible ML and AI framework, toolkit, or library according to your use case. ONNX, TensorFlow, ML.NET, PyTorch, and CNTK are the top libraries/frameworks that most .NET developers prefer. However, before you select any of them, ensure you have complete hardware and software resources to prevent issues.

Step 4: Develop the Application and Train the Model

After selecting the library or framework, start AI or ML model training. During the training, provide enormous inputs to the models and store their output. Further, examine the results and update the data as required to receive expected predictions.

Additionally, develop the .NET application, test it across devices, and ensure its functioning as required.

Step 5: Integrate .NET Application and ML/AI Model

Once your AI/ML model completes its training and you receive the expected outputs, integrate it into the .NET application. Through a relevant API (Application Programming Interface), you can effortlessly establish communication between AI/ML model and .NET software.

Moreover, test the compatibility, overall functioning, and performance before deploying it.

Step 6: Constantly Monitor, Update, and Train

Once the .NET application gets deployed, constantly monitor its health, performance, and speed. And if you find any bugs or loopholes, patch them before exploitation. Also, frequently fed your model with new data to keep it updated and to receive better outputs.

Why AI and ML are becoming necessary for dot NET Applications? 

Artificial Intelligence and Machine Learning are becoming pillars for new-age .NET applications due for the following reasons: 

Operation Automation

Machine Learning and Artificial Intelligence mechanisms help to automate repetitive tasks and reduce/eliminate errors while executing them. In addition, AI and ML-based application helps to save money, as they can also handle other machinery and systems through a centralized database.

For instance, Machine learning mechanisms are being used by traffic police departments to detect vehicles violating laws. Such systems utilize image recognition to identify the type of vehicle and its registered number and then verify owner details. As a result, fines get delivered at home, and the police department’s work gets automated. 

Predictive Analysis

With the integration of AI and ML libraries, dot NET applications can analyze heavy loads of data to detect patterns. Pattern detection is mainly used to predict users’ behavior and actions on eCommerce stores. 

For instance, when a user reaches the site, ML algorithms analyze its behavior and cross-verify it with available data. As a result, companies detect whether the customer is going to buy a product or not. In addition, such data also help to update the operations and site to improve user retention and conversions.

Fraud Detection

Numerous organizations use AI and ML features in internal .NET-based security applications. In such software, machine learning assesses the data of security threats, attackers’ behavior, and more. And then, AI takes all the processed data and automatically strengthens the security. 

As a result, credit card fraud, phishing, and identity thefts get prevented. Sometimes even sophisticated attacks such as DoS, malware, and DDoS also get mitigated through ML and AI.

Continuous Improvement

A machine learning algorithm continuously improves it by deeply analyzing different datasets. It helps it to predict more efficiently and appropriately. For instance, weather forecasting applications utilize ML algorithms to predict the weather over a specific area in a defined month. The output for such applications is appropriate when the ML model assesses weather information of the past 10 to 15 years and current climatic conditions. 

Similarly, every ML algorithm improves through data analysis, leading to more insightful predictions and better decisions. 

Chatbots and Virtual Assistants

AI-powered chatbots and assistants help to improve user interaction and satisfaction rates. And it also aids in saving money, as companies don’t have to invest heavily in hiring support executives. 

AI-based chatbots recognize user queries, examine them and provide a relevant solution from the defined policies. And even if the query is out-of-scope, it utilizes other resources to get a relevant answer. Due to it, customers get satisfied, and traffic increases. 

Moreover, when you consult with a .NET development company, it will also recommend you embed an AI-based chatbot.

Personalization 

By enabling .NET applications with AI and ML features, you can astound a user experience. It can detect user behavior and customize the interface accordingly, boosting revenue. For example, if an end-user is searching for a sci-fi book in your store, AI and ML will collaborate to provide recommendations. 

In addition, you can also enable the .NET application to modify themes, colors, and fonts accordingly. As a result, users will retain and utilize more application sources, and your user base will accelerate.  

What Differs Between the .NET Framework and the .NET Core?

The .NET Ecosystem provides numerous tools and technologies that help to build futuristic business solutions. And .NET Framework and .NET Core are the primary development technologies under it. When selecting one among them, the question always arises: “Which is better – .NET Framework or .NET Core?”. 

The Top .NET Framework and .NET Core AI and ML Libraries

You must build and train their models to integrate AI and ML in a .NET application. And to develop such models, you should know the top-notch .NET compatible libraries and frameworks.

ML.NET

ML.NET is an open-source framework that works explicitly with .NET applications. It’s a product of Microsoft and can be freely downloaded from the official site. To integrate machine learning facilities in an application, ML.NET is the first choice of developers.

In addition, it provides advanced functionalities, such as:

• Integration with Visual Studio to streamline application development.
• Cross-platform compatibility.
• Enables using PowerPoint Design Ideas, Windows Hello, and Bing Ads.
• Flawless extension with TensorFlow, Infer .NET, and ONNX.
• Besides, Microsoft Defender, Power BI, and Outlook also trust ML.NET.

TensorFlow

TensorFlow is a leading open-source library that embeds machine learning and artificial intelligence in a .NET application. It offers you a pre-trained model, which can help you can directly install and start using the software. You can also use it to create and train your own ML model for specific purposes.

Additionally, whether you use the .NET framework or .NET Core, it works seamlessly with both. Furthermore, it functions with all significant deployment models, including on-premises, devices, cloud, and browsers. All TensorFlow-based models run and update in production.

Accord .NET

Accord.NET is also a top-notch machine learning framework for .NET applications. Most .NET developers prefer it over other frameworks due to built-in image and audio processing capabilities. In addition, the complete framework is also built using C#, making it highly compatible with .NET and all its associated components.

Further, it provides a lot of advanced functionalities, such as:

• Support for vector machines
• Sequence Classification
• Hidden Markov classifiers
• Multiple linear regression
• Multivariate linear regression
• K-means, Mean-shift, Deep Belief, and more clustering algorithms
• Parametric estimations
• 35+ hypothesis tests
• Real-time face detection
• Real-time tracking
• Dynamic Template matching trackers
• Square signals and a lot more

CNTK (Cognitive Toolkit)

Microsoft Cognitive Toolkit is a library that aids in building the neural network for constantly training the machine learning algorithms. When you hire .NET developers, they use it to advance the functionalities of ML and make applications do more tedious predictive analysis.

In addition, you can use CNTK as a low and high-level API in the application. And Professionals also consider it for building neural network components and production-quality networks. Because of all such functionalities, it works smoothly with all microservices, web-based, and Windows Store applications.

PyTorch

After collaboration with Facebook, Microsoft introduced integration and support for PyTorch, a machine-learning framework with open-source architecture. As a .NET developer, it can help you quickly implement and train ML.NET-based models. And it efficiently optimizes data processing, making applications scalable and flexible.

In addition, PyTorch also offers the following:

• Tensor Computation functionalities
• NumPy features
• Its utilization for computer vision
• Natural Language Processing
• AI use cases and more

ONNX

ONNS is also a framework with open-source architecture that aids in building interoperability between other ML and AI frameworks. In addition, Open Neural Network Exchange also gets used for deep learning model encapsulation purposes. And .NET developers can leverage GPUs with it to accelerate application functioning, analysis speed, and performance.

Furthermore, ONNX is best suitable for creating console applications and predicting models with C#. Besides, it allows for building and training ML models using any library and framework in any language. And then convert such models into a single ONNX format and utilize it across different .NET applications.

Dlib

Dlib is a toolkit for .NET developers that offers multiple machine-learning algorithms for .NET applications. Unlike other libraries and frameworks, its documentation is very well written, which benefits effortlessly implement it. In addition, it provides the following features to curate dot NET applications:

• Example programs in the documentation for easy understanding.
• Zero-step installation to use the Dlib.
• Portable code across Windows, Linux, and macOS.
• SVM Classification algorithm.
• Kernel RLS algorithm.
• Semidefinite Metric Learning
• Sequence labeling, assignment problems, and object detection SVM tools.
• Deep learning.
• Classification and regression models.
• Optimized Cutting Plane algorithm
• Quadratic program solvers
• Join Tree and Gibbs sampler

LightGBM

LightGBM stands for gradient boosting framework, which you can use for taking advantage of decision trees. It also offers the following benefits for .NET applications:

• High efficiency and speed in training machine learning models
• Less memory utilization
• Improvement in the application’s accuracy and decision-making algorithm.
• Parallelly, GPU learning and distributed support.
• Ability to handle heavy loads of data from multiple sources.

Top .NET Development Company for Building AI and ML-based .NET Applications

AI and ML are advanced technology concepts only an expert .NET development company can implement. So, you must highly focus on the firm’s skill set, expertise, and experience in the development domain. And before you finalize any of the companies, check their portfolio and interview with their development team.

Furthermore, Positiwise Software Pvt Ltd is a top-notch .NET development services company. It offers a complete range of .NET services, from designing and development to migration and maintenance. In addition, it updates itself constantly as per trends and configures the best-in-class AI and ML models for business applications, optimizing and automating operations.

Wrapping Up

When looking to hire .NET Core developers to integrate AI and ML models into .NET applications, ensure they understand the use case and different compatible libraries like TensorFlow, ML.NET, CNTK, etc. They need experience collecting data to train models and integrate the outputs into .NET apps. They should also know how to monitor apps frequently, retrain models as needed, and provide support. The ideal .NET Core developers will have hands-on expertise leveraging the right AI/ML algorithms and frameworks for the task, training models with sufficient data, seamlessly integrating outputs into .NET apps, and properly maintaining and supporting AI-enabled systems over time.