Archive | Practical Machine Learning

In this article, you will learn how to train a Scikit-learn classification model, serve it with FastAPI, and deploy it to FastAPI Cloud.

In this article, you will learn how zero-shot text classification works and how to apply it using a pretrained transformer model.

In this article, you will learn how to use Python decorators to improve the reliability, observability, and efficiency of machine learning systems in production.

In this article, you will learn how machine learning is evolving in 2026 from prediction-focused systems into deeply integrated, action-oriented systems that drive real-world workflows.

In this article, you will learn how to use Python’s itertools module to simplify common feature engineering tasks with clean, efficient patterns.

This article introduces seven insightful examples of text analyses that can be easily conducted by using the Textstat library.

In this article, you will learn practical strategies for building useful machine learning solutions when you have limited compute, imperfect data, and little to no engineering support.

Build a working MCP server in Python using FastMCP with tools, resources, and prompts.

Compare PCA and t-SNE for data visualization with practical Python code and best practices.

Learn how to export PyTorch, scikit-learn, and TensorFlow models to ONNX format for faster, portable inference.