Peter Song

Peter Song is a machine learning, data engineer, and AI writer with a focus on practical ML workflows. He shares insights on machine learning, data tools, and MLOps.

Best Jupyter Notebook Extensions for Data Science

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Jupyter Notebook has become the de facto standard for data science work, offering an interactive environment that seamlessly blends code, visualizations, and documentation. However, the default Jupyter installation, while powerful, lacks many features that can dramatically improve your productivity and workflow. This is where Jupyter Notebook extensions come in—community-developed add-ons that enhance functionality, streamline repetitive … Read more

How Do I Deploy ML Models in AWS Lambda?

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Deploying machine learning models in AWS Lambda has become increasingly popular among data scientists and engineers who want to create scalable, cost-effective inference endpoints. Lambda’s serverless architecture eliminates the need to manage infrastructure while automatically scaling based on demand. However, deploying ML models to Lambda comes with unique challenges around package size limits, cold starts, … Read more

Dealing With Missing Data in Real-World ML Projects

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Missing data is the silent saboteur of machine learning projects. While academic datasets come pristine and complete, real-world data is messy—filled with gaps, nulls, and inconsistencies that can derail even the most sophisticated models. I’ve seen projects fail not because of poor algorithm choices or insufficient computing power, but because missing data was handled carelessly … Read more

How to Normalize a Vector in Python

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Vector normalization is a fundamental operation in data science, machine learning, and scientific computing. Whether you’re preparing data for a neural network, calculating cosine similarity, or working with directional data, understanding how to normalize vectors in Python is essential. In this comprehensive guide, we’ll explore multiple approaches to vector normalization, from basic implementations to optimized … Read more

Gemini vs Claude for Enterprise AI

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The enterprise AI landscape has evolved dramatically in 2025, with two powerhouse models emerging as frontrunners for business applications: Google’s Gemini and Anthropic’s Claude. As organizations increasingly integrate artificial intelligence into their core operations, the choice between these platforms has become critical for enterprise success. This comprehensive analysis examines the key differentiators, strengths, and practical … Read more

How to Reduce Overfitting in Scikit-learn

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Overfitting is one of the most common challenges you’ll face when building machine learning models. It occurs when your model learns the training data too well—including its noise and peculiarities—resulting in poor performance on new, unseen data. If you’ve ever built a model that achieves 99% accuracy on training data but barely 60% on test … Read more

How to Normalize vs Standardize Data in Scikit-Learn

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Data scaling is one of those preprocessing steps that can make or break your machine learning model, yet it’s often treated as an afterthought. The terms “normalization” and “standardization” are frequently used interchangeably, but they’re fundamentally different transformations that serve different purposes. Understanding when to use each technique—and how to implement them correctly in scikit-learn—is … Read more

How to Convert Jupyter Notebook to Python Script for Production

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Jupyter notebooks are phenomenal for exploration, prototyping, and communicating results. But when it’s time to move your work to production, that beautifully interactive notebook becomes a liability. Production systems need reliable, testable, modular code that can run without a browser interface—and notebooks simply weren’t designed for that. I’ve seen too many teams struggle with this … Read more

Best PyTorch Tricks for Tabular Data

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PyTorch has revolutionized deep learning for images and text, but many data scientists still hesitate to use it for tabular data. The common wisdom suggests sticking with gradient boosting methods like XGBoost or LightGBM for structured data. While those tools are excellent, PyTorch offers unique advantages when you know the right tricks. With proper techniques, … Read more

Machine Learning Project Structure Best Practices

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A well-organized machine learning project can mean the difference between a smooth path to production and a chaotic mess that nobody wants to maintain. I’ve seen countless ML projects that started with brilliant ideas but became unmaintainable nightmares because of poor structure. The code worked—at least initially—but when it came time to add features, retrain … Read more