Leveraging ML.NET to Solve Real-World Problems

#machinelearning #ai

Over the past six weeks, I’ve been on a transformative learning journey with ML.NET, diving deep into how machine learning can be leveraged to solve real-world problems across various industries—all while staying within the powerful .NET ecosystem.

As a .NET engineer, I’ve always been driven by the challenge of solving complex problems with innovative solutions. ML.NET has not only enhanced my technical skill set but has also opened my eyes to the potential of machine learning in applications ranging from healthcare and finance to logistics and retail.

One of the most rewarding challenges I tackled was developing a solution in the healthcare space—focused on accurate drug quantity dispensation to HMOs for end users. The solution I built uses predictive models to ensure that drug quantities are dispensed accurately and efficiently, significantly improving operational processes.

Challenges & Learnings Along the Way
As part of the solution, I experimented with various ML algorithms in ML.NET, working through multiple challenges to achieve optimal accuracy in predicting drug quantities. A few notable challenges included:
Tuning Hyperparameters: Ensuring accurate predictions by adjusting models to get the best Root Mean Squared Error (RMSE) and R-squared (R²) values for different ranges of data sets.
Model Selection: Choosing between algorithms like Regression and Decision Trees, testing their performance on both small and large datasets, and ensuring generalization without overfitting.
Cross-validation: Implementing cross-validation to evaluate model performance across different subsets of data, ensuring robustness and minimizing bias.
A screenshot of the current healthcare solution I deployed shows real-time usage of the ML.NET model—further validating how ML.NET can truly drive impactful solutions.

This project was just the beginning. As I continue to experiment and learn, I’ll be sharing my hands-on experiences in ML.NET, including:
Best practices for working with ML.NET models
Real-world use cases across various industries
Solutions to common challenges like data preprocessing, model training, and evaluation metrics

🔔 Stay tuned! Every week, I’ll be sharing fresh insights, challenges, and success stories from my ML.NET exploration. Whether you're a recruiter, fellow engineer, or machine learning enthusiast, let’s connect and discuss how ML.NET is transforming industries!
Let’s build and innovate together!