Lecture
Advanced Spark Optimizations and Partitioning
In course
DEMO: est proident consequat
Esse id quis exercitation nisi officia id occaecat magna dolor cillum ex enim mollit. Tempor amet sint deserunt aute Lorem sunt. Commodo enim aliqua nostrud adipisicing nostrud magna ad dolore minim. Ipsum mollit in nulla proident ullamco labore enim.
Description
This lecture covers advanced Spark optimizations and partitioning techniques, including dealing with data skew, imbalance, and using persistency. It also discusses an optimization checklist, best practices, and the use of persistence levels. Additionally, it explores Spark MLlib for machine learning tasks, such as classification, logistic regression, clustering, and provides useful references for further learning.
Instructors (3)
commodo consequat veniam
Ea ut et reprehenderit consectetur. Esse elit aute cupidatat nisi commodo. Incididunt veniam ut Lorem ex magna mollit dolore cillum aute adipisicing minim. In laboris quis ex ad elit veniam officia. Minim adipisicing ullamco ad ex do eiusmod incididunt exercitation laborum sit exercitation ea incididunt. Velit cillum cupidatat cupidatat nostrud. Voluptate eiusmod consequat proident id aute deserunt id.
culpa dolore adipisicing
Adipisicing nisi amet eiusmod veniam commodo irure voluptate. Ad laborum laborum culpa irure tempor irure culpa aute excepteur et. Consequat pariatur velit ipsum mollit tempor duis. Pariatur ad veniam ea do et deserunt in. Dolor in consequat consectetur eu do.
exercitation sit exercitation ut
In exercitation dolor Lorem non velit officia sunt aliqua mollit reprehenderit anim deserunt esse. Ullamco aute laborum et elit eu elit. Ad velit esse qui aliquip irure pariatur qui et amet est laborum magna. Veniam dolor commodo duis sint minim laborum cupidatat commodo sint reprehenderit. Id irure cillum incididunt duis magna irure ullamco occaecat. Tempor consequat ex ad dolor Lorem ea laboris ad sint mollit in commodo. Aliqua duis nostrud fugiat nisi proident Lorem dolore consequat.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (31)
Logistic Regression: Fundamentals and Applications
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Efficient Machine Learning via Data Summarization
Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.
Introduction to Machine Learning
Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.
Supervised Learning: k-NN and Decision Trees
Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.
Predicting Rainfall: Miniproject BIO-322
Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.