Data Science Essentials
The discussion dives into the critical steps of data science, emphasizing the importance of data extraction and analysis. It highlights the need to ensure the right data is available to solve specific problems while considering privacy and data sources. Additionally, the conversation touches on the iterative nature of data preparation, where understanding the model can influence feature engineering and preprocessing techniques.In this clip
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MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3
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