Machine Learning

Machine Learning Sep 10, 2020 3 min read

Fixing Mixed-Content and CORS issues at ML Model inference time with Azure Functions

Resolve mixed-content and CORS errors when calling Azure Functions from web applications. Learn how to configure HTTPS-only access and proper CORS settings, with a bonus explanation of preflight OPTIONS requests.

Machine Learning Aug 20, 2020 5 min read

Troubleshooting an ONNX Model deployment to Azure Functions

Navigate the deployment challenges of ML models to Azure Functions, from Python version mismatches to memory constraints. Discover how to use docker-based local builds with native dependencies to overcome remote build limitations.

Machine Learning Apr 8, 2020 6 min read

Making Use of Unstructured Data with Form Recognizer

Extract structured data from unstructured forms using Azure Form Recognizer with just 5 training examples. Walk through the complete process from service creation to model training and analysis using Postman and Azure Storage.

Machine Learning Apr 6, 2020 7 min read

3 Tips for Debugging Cloud Scale Machine Learning Workloads

Learn to validate ML workloads locally before expensive cloud deployment using Azure ML's docker-based local execution. Covers environment configuration, data mounting, and testing both training and inference pipelines as if they were running in the cloud.

dotnet Apr 8, 2013 3 min read

Descriptors in numl

Deep dive into nuML's Descriptor pattern that bridges object-oriented class structures with mathematical ML algorithms. Includes both attribute-based markup and fluent interface approaches for late-binding scenarios.

dotnet Mar 1, 2013 1 min read

Codemash 2.0.1.3

Presenting the nuML machine learning library at CodeMash with a focus on accessibility and ease of use. Addresses predictive challenges, performance considerations, and provides talk materials for both supervised and unsupervised learning.

Machine Learning Aug 4, 2010 3 min read

Linear Classifiers

Visualizing how linear classifiers work by drawing separators through data points. Explores the concept of generalization and demonstrates the kernel trick for handling non-linearly separable data by projecting to higher dimensions.

dotnet Jul 12, 2010 3 min read

Supervised Learning - Classification

Breaking down supervised learning into binary classification, multi-class classification, and regression using .NET data types. Shows how the Perceptron algorithm can learn from labeled examples and includes model serialization for reuse.