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.

Data Engineering vs Data Science vs Machine Learning

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The data ecosystem has exploded over the past decade, creating distinct career paths that often confuse aspiring professionals and even established organizations. While data engineering, data science, and machine learning are deeply interconnected, they represent fundamentally different disciplines with unique skills, responsibilities, and outcomes. Understanding these differences is crucial whether you’re planning your career path, … Read more

Small LLM vs Large LLM Tradeoffs in Inference Cost

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The explosion of large language models has created a critical decision point for organizations: should you deploy massive models that deliver cutting-edge performance, or opt for smaller, more efficient alternatives? This isn’t just a technical question—it’s fundamentally about economics. Inference costs—the expenses incurred every time a model generates a response—can make or break the viability … Read more

How to Build End-to-End ML Pipelines with Airflow and DBT

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Building production-ready machine learning pipelines requires orchestrating complex workflows that transform raw data into model predictions. Apache Airflow and dbt (data build tool) have emerged as a powerful combination for this task—Airflow handles workflow orchestration and dependency management, while dbt brings software engineering best practices to data transformation. Together, they enable teams to build maintainable, … Read more

How Gemini Uses Deep Learning and Neural Networks

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Google’s Gemini represents a significant leap forward in artificial intelligence, built on sophisticated deep learning architectures and neural networks that enable it to understand and generate human-like responses across multiple modalities. Understanding how Gemini leverages these technologies reveals the intricate engineering behind one of the most advanced AI systems available today. The Foundation: Transformer Architecture … Read more

LLM Cost Reduction Strategies: Practical Techniques to Slash Your AI Spending

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Large language models have revolutionized how businesses operate, but their costs can quickly spiral out of control. Organizations frequently discover that their initial API bills of a few hundred dollars have ballooned into monthly expenses exceeding tens of thousands—sometimes even hundreds of thousands—of dollars. The good news? Most companies can dramatically reduce their LLM costs … Read more

Using Optuna for Hyperparameter Tuning in PyTorch

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Deep learning models are notoriously sensitive to hyperparameter choices. Learning rates, batch sizes, network architectures, dropout rates—these decisions dramatically impact model performance, yet finding optimal values through manual experimentation is time-consuming and inefficient. Optuna brings sophisticated hyperparameter optimization to PyTorch workflows through an elegant API that supports advanced search strategies, pruning of unpromising trials, and … Read more

What is the Role of Data Engineering in Machine Learning

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Machine learning has captured headlines with impressive achievements in image recognition, natural language processing, and predictive analytics. Yet behind every successful ML model lies an often-overlooked foundation: data engineering. While data scientists develop algorithms and tune models, data engineers build the infrastructure that makes machine learning possible at scale. Understanding this role reveals why many … Read more

Data Engineering Basics for Machine Learning Projects

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Data engineering forms the critical foundation of every successful machine learning project, yet it’s often underestimated by teams eager to jump into model development. The reality is that machine learning models are only as good as the data pipelines feeding them. Understanding data engineering basics can mean the difference between a model that thrives in … Read more

How to Use Snowflake for Machine Learning Data Pipelines

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Snowflake has emerged as a powerful platform for building machine learning data pipelines, offering unique advantages that address common challenges data scientists and ML engineers face. Understanding how to leverage Snowflake’s capabilities can dramatically streamline your ML workflow, from raw data ingestion through model training and deployment. Setting Up Your Snowflake Environment for ML Pipelines … Read more

Text Classification with Transformers

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Text classification has undergone a revolutionary transformation with the advent of transformer architectures. From simple rule-based systems to sophisticated neural networks, the field has evolved dramatically, with transformers now representing the state-of-the-art approach for understanding and categorizing textual content. This comprehensive guide explores how transformers have reshaped text classification, their underlying mechanisms, and practical implementation … Read more