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Machine learning systems design

Machine Learning Systems Design is the iterative process of defining components like data, infrastructure, and algorithms to satisfy production requirements. While many learn ML in an academic setting focused on model performance, deploying systems in the wild requires understanding that production objectives differ significantly from research. In this video, we explore the complete lifecycle of production-ready applications, from project scoping and data engineering to monitoring and business analysis. We dive deep into data engineering fundamentals, explaining why data is the foundation of modern intelligent systems. You will learn about various data sources, the importance of choosing the right data formats like Parquet over CSV for efficiency, and the nuances of the ETL process. We also cover critical challenges such as ••• data leakage, class imbalance, and the trade-offs between model accuracy and latency. Finally, we discuss feature engineering best practices and model evaluation methods, including slice-based evaluation to detect hidden biases in your system. Whether you are a data scientist or an ML engineer, these insights will help you build reliable, scalable, and maintainable systems. Subscribe for more deep dives into MLOps and system design! #aiagents #aiforbusiness #aiintro #MachineLearning #mlops #dataengineering #Al #datascience #ChipHuyen #MLSystems #modeldeployment #featureengineering #datascience #bigdata #artificiallntelligence #softwareengineering #MachineLearningSystems #techeducation