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Evolution of ML Fact Store

The Netflix TechBlog

We built Axion primarily to remove any training-serving skew and make offline experimentation faster. We make sure there is no training/serving skew by using the same data and the code for online and offline feature generation. Our machine learning models train on several weeks of data.

Storage 187
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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

These include ETL pipelines, ML model training workflows, batch jobs, etc. As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company.

Java 202
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