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.

Cloud Storage Optimization for Large ML Datasets

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Machine learning projects have evolved dramatically in scale and complexity, with datasets now routinely reaching petabyte sizes. Organizations working with computer vision, natural language processing, and deep learning models face unprecedented challenges in storing, accessing, and managing these massive datasets efficiently. Cloud storage optimization for large ML datasets has become a critical discipline that directly … Read more

Fine-Tuning GPT Models for Niche Domains

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Transform Generic AI into Domain Experts Unlock the full potential of GPT models with specialized fine-tuning techniques Fine-tuning GPT models for niche domains represents one of the most powerful approaches to creating specialized AI systems that understand industry-specific language, terminology, and context. While pre-trained language models like GPT-3.5 and GPT-4 demonstrate impressive general capabilities, they … Read more

How to Use Feathr vs Feast for Feature Stores in Production

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Feature stores have become essential infrastructure for machine learning teams looking to manage, serve, and share features across different models and applications. Two prominent open-source solutions in this space are Feathr and Feast, each offering unique approaches to solving feature management challenges in production environments. Understanding how to effectively use these platforms can significantly impact … Read more

ML Model Rollback Strategies After Failed Deployment

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Machine learning model deployments don’t always go according to plan. When a newly deployed model starts producing unexpected results, degrades in performance, or causes system instability, having robust ML model rollback strategies becomes critical for maintaining business continuity and user trust. The complexity of modern ML systems means that rollback procedures require careful planning, automated … Read more

Step-by-Step Guide to Creating a Transformer from Scratch in PyTorch

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Building a Transformer model from scratch is one of the most rewarding experiences for any deep learning practitioner. The Transformer architecture, introduced in the groundbreaking paper “Attention Is All You Need,” revolutionized natural language processing and became the foundation for modern language models like GPT and BERT. In this comprehensive guide, we’ll walk through implementing … Read more

Time Series Forecasting with Prophet vs ARIMA

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Time series forecasting remains one of the most critical applications in data science, enabling businesses to predict future trends, plan inventory, forecast sales, and make informed strategic decisions. When it comes to choosing the right forecasting method, two approaches consistently emerge as leading contenders: Facebook’s Prophet and the traditional ARIMA (AutoRegressive Integrated Moving Average) model. … Read more

Prompt Engineering for Machine Learning Engineers

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As machine learning engineers, we’ve mastered the intricacies of neural networks, optimization algorithms, and data pipelines. However, the rise of large language models (LLMs) has introduced a new skill that’s becoming increasingly crucial: prompt engineering. This discipline bridges the gap between traditional ML engineering and the emerging world of generative AI, requiring a unique blend … Read more

Shadow Deployment vs Canary Deployment for ML Models

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When deploying machine learning models to production, choosing the right deployment strategy can make the difference between seamless updates and catastrophic failures. Two of the most powerful approaches for safely rolling out ML models are shadow deployment and canary deployment. While both strategies aim to minimize risk and ensure model reliability, they operate on fundamentally … Read more

Rolling Back Failed Machine Learning Model Deployments

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When machine learning models fail in production, the ability to quickly and effectively roll back to a previous stable version can mean the difference between minor service disruption and catastrophic business impact. Rolling back failed machine learning model deployments is a critical skill that every ML operations team must master, yet it presents unique challenges … Read more

Common Pitfalls in Deploying Deep Learning Models to Production

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The excitement of achieving promising results with your deep learning model during development can quickly turn into frustration when deploying to production. While training and validating models in controlled environments is challenging enough, the transition from research to real-world deployment introduces an entirely new set of complexities that can derail even the most promising AI … Read more