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...

1. Why stick with Spark libraries vs. a single node operation?
2. What algorithms are not in Spark Lib?
3. What is the min. package set to use for supervised learning?
4. Modeling and validation
5. Down-sampling your data
6. MLlib vs. scikit-learn
7. Resources

Sponsors

• Top End Devs
• Coaching | Top End Devs

Links

• MLlib | Apache Spark
• What is PySpark? | Domino Data Science Dictionary
• UCI Machine Learning Repository

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