MLlib is Apache Spark's scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best ML workflow with MLlib.
In this episode...
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In this episode...
- Why stick with Spark libraries vs. a single node operation?
- What algorithms are not in Spark Lib?
- What is the min. package set to use for supervised learning?
- Modeling and validation
- Down-sampling your data
- MLlib vs. scikit-learn
- Resources
Sponsors
Links
- MLlib | Apache Spark
- What is PySpark? | Domino Data Science Dictionary
- UCI Machine Learning Repository
Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
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