This paper presents an in-depth analysis of the extensibility, model interpretability and explainability, and automation capabilities of ML.NET, Microsoft’s open-source machine learning framework for .NET developers. We identify and address the challenges faced by third-party developers, particularly due to ML.NET’s restricted internal APIs and reliance on friend assemblies. We propose practical approaches for implementing custom estimators and evaluation metrics, enabling greater flexibility for external contributors. Furthermore, we examine the interpretability and explainability of models produced by ML.NET and its AutoML component, demonstrating both black-box and white-box strategies. Finally, we introduce an automated methodology for fair benchmarking and comparison of ML.NET’s AutoML results with alternative frameworks, leveraging JSON-based configuration and reproducible evaluation pipelines. All proposed methods are supported by open-source code and validated through experiments on standard benchmark datasets. Our findings highlight both the strengths and current limitations of ML.NET, providing actionable guidance for practitioners and researchers seeking to extend and analyze machine learning workflows within the .NET ecosystem.