topic Feature Engineering for Data Engineers: Building Blocks for ML Success in Community Articles

Feature Engineering for Data Engineers: Building Blocks for ML Success

MichTalebzadeh — Sat, 03 Aug 2024 18:28:12 GMT

For a UK Government Agency, I made a Comprehensive presentation titled " Feature Engineering for Data Engineers: Building Blocks for ML Success". I made an article of it in Linkedlin together with the relevant GitHub code. In summary the code delves into the critical steps of feature engineering, demonstrating how to handle missing values, encode categorical data, and prepare numerical features for modelling. By employing techniques like mean imputation and one-hot encoding, we establish a solid foundation for training complex models such as Variational Autoencoders (VAEs). This comprehensive approach empowers data scientists and data engineers to extract meaningful insights and build high-performing machine learning pipelines.

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Feature Engineering for Data Engineers: Building Blocks for ML Success | LinkedIn

Re: Feature Engineering for Data Engineers: Building Blocks for ML Success

Anushree_Tatode — Mon, 16 Sep 2024 06:48:52 GMT

Hi,
Excellent presentation and article! Your insights on feature engineering and practical code examples are incredibly useful for building strong ML models. Thanks for sharing!

Thanks,
Anushree

Re: Feature Engineering for Data Engineers: Building Blocks for ML Success

MichTalebzadeh — Tue, 17 Sep 2024 11:16:25 GMT

Many thanks for your kind words Anushree. Much appreciated.

Re: Feature Engineering for Data Engineers: Building Blocks for ML Success

Mantsama4 — Fri, 07 Feb 2025 03:48:43 GMT

This is a fantastic post! The detailed explanation of feature engineering, from handling missing values to using Variational Autoencoders (VAEs) for synthetic data generation, provides invaluable insights for improving machine learning models. The approach of combining various preprocessing techniques is a great solution for building robust, high-performance ML pipelines.

Could you kindly share on how we can create a feature store in in Databricks?