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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

KDnuggets

By Jayita Gulati on July 16, 2025 in Machine Learning Image by Editor In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. Feature engineering can impact model performance, sometimes even more than the choice of algorithm itself. AutoML frameworks : Tools like Google AutoML and H2O.ai

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Scaling Pinterest ML Infrastructure with Ray: From Training to End-to-End ML Pipelines

Pinterest Engineering

Feature Development Bottlenecks Adding new features or testing algorithmic variations required days-long backfill jobs. Feature joins across multiple datasets were costly and slow due to Spark-based workflows. Reward signal updates needed repeated full-dataset recomputations, inflating infrastructure costs.

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Foundation Model for Personalized Recommendation

Netflix Tech

However, as we expanded our set of personalization algorithms to meet increasing business needs, maintenance of the recommender system became quite costly. This scenario underscored the need for a new recommender system architecture where member preference learning is centralized, enhancing accessibility and utility across different models.

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10 Amazon SageMaker Project Ideas and Examples for Practice

ProjectPro

From data exploration and processing to later stages like model training, model debugging, and, ultimately, model deployment, SageMaker utilizes all underlying resources like endpoints, notebook instances, the S3 bucket, and various built-in organization templates needed to complete your ML project.

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Adaboost Algorithm Explained in Depth

ProjectPro

This blog serves as a comprehensive guide on the AdaBoost algorithm, a powerful technique in machine learning. This wasn't just another algorithm; it was a game-changer. Before the AdaBoost machine learning model , most algorithms tried their best but often fell short in accuracy. Freund and Schapire had a different idea.

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Netflix Tudum Architecture: from CQRS with Kafka to CQRS with RAW Hollow

Netflix Tech

Page Data Service utilized a near cache to accelerate page building and reduce read latencies from the database. RAW Hollow is an innovative in-memory, co-located, compressed object database developed by Netflix, designed to handle small to medium datasets with support for strong read-after-write consistency. seconds to ~0.4

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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

Filling in missing values could involve leveraging other company data sources or even third-party datasets. Data Normalization Data normalization is the process of adjusting related datasets recorded with different scales to a common scale, without distorting differences in the ranges of values.