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.

Gemini AI Model Parameters and Performance Benchmarks

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Google’s Gemini represents a significant leap forward in artificial intelligence, introducing native multimodal capabilities that process text, code, images, audio, and video within a unified architecture. Understanding Gemini’s technical specifications and performance characteristics is essential for developers, researchers, and organizations evaluating AI solutions. This article examines the model parameters, architectural choices, and benchmark performance that … Read more

Step-by-Step Linear Regression in Jupyter Notebook

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Linear regression is the foundation of predictive modeling and machine learning. Whether you’re predicting house prices, sales figures, or temperature trends, linear regression provides a powerful yet interpretable approach to understanding relationships between variables. This comprehensive guide will walk you through implementing linear regression in Jupyter Notebook from start to finish, covering everything from data … Read more

Understanding Confusion Matrix for Beginners

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When you build a machine learning model, knowing whether it works well is just as important as building it in the first place. But “working well” isn’t always straightforward—especially when dealing with classification problems. This is where the confusion matrix becomes your best friend. Despite its intimidating name, a confusion matrix is actually a simple … Read more

Saving and Loading Sklearn Models the Right Way

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Training machine learning models takes time and computational resources. Once you’ve built a model that performs well, the last thing you want is to retrain it from scratch every time you need to make predictions. Model persistence—saving trained models to disk and loading them later—is a fundamental skill in production machine learning. While scikit-learn makes … Read more

Logging Machine Learning Experiments with MLflow

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Machine learning development is inherently experimental. You try different algorithms, tweak hyperparameters, preprocess data in various ways, and iterate through dozens or even hundreds of model variations. Without systematic experiment tracking, this process becomes chaotic—you lose track of what worked, can’t reproduce promising results, and waste time re-running experiments you’ve already tried. MLflow provides a … Read more

Fine-Tuning HuggingFace Transformers in Jupyter Notebook

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Fine-tuning pre-trained transformer models has become the cornerstone of modern NLP development. While cloud-based platforms and production pipelines have their place, Jupyter Notebook remains the preferred environment for experimentation, rapid prototyping, and iterative model development. The interactive nature of notebooks combined with HuggingFace’s Transformers library creates a powerful combination for adapting state-of-the-art models to your … Read more

Airflow vs Prefect for Machine Learning Pipelines

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Building robust machine learning pipelines requires orchestration tools that can handle complex workflows, manage dependencies, and scale with your data science operations. Apache Airflow and Prefect have emerged as two leading contenders in this space, each bringing distinct philosophies and capabilities to ML pipeline orchestration. Understanding their core differences is essential for data science teams … Read more

MLOps Pipeline with Terraform and Kubernetes Step by Step

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Building a robust MLOps pipeline with Terraform and Kubernetes step by step has become essential for organizations seeking to deploy, manage, and scale machine learning models in production environments. This comprehensive approach combines Infrastructure as Code (IaC) principles with container orchestration to create scalable, reproducible, and maintainable ML workflows that can handle enterprise-grade workloads. The … Read more

How to Preprocess Text Data for Sentiment Analysis

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Text preprocessing is the invisible foundation upon which successful sentiment analysis models are built. Raw text data—whether from social media posts, customer reviews, or survey responses—arrives chaotic and inconsistent. Typos, slang, punctuation variations, and irregular capitalization create noise that can confuse machine learning models and degrade performance. The difference between a sentiment classifier achieving 75% … Read more

PyTorch vs TensorFlow for Beginners

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Choosing your first deep learning framework is one of the most consequential decisions you’ll make as a machine learning beginner. The framework you learn shapes how you think about neural networks, influences what resources and communities you can access, and determines how quickly you can move from tutorials to real projects. PyTorch and TensorFlow dominate … Read more