Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX (Half Day)
Jayant Shekhar, Amandeep Khurana, Krishna Sankar, and Vartika Singh guide participants through techniques for building machine-learning apps using Spark MLlib and Spark ML and demonstrate the principles of graph processing with Spark GraphX.
| Talk Title | Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX (Half Day) |
| Speakers | Jayant Shekhar (Sparkflows Inc.), Amandeep Khurana (Cloudera), Krishna Sankar (U.S.Bank), Vartika Singh (Cloudera) |
| Conference | Strata + Hadoop World |
| Conf Tag | Big Data Expo |
| Location | San Jose, California |
| Date | March 29-31, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Jayant Shekhar, Amandeep Khurana, Krishna Sankar, and Vartika Singh guide participants through techniques for building machine-learning apps using Spark MLlib and Spark ML and demonstrate the principles of graph processing with Spark GraphX. Jayant, Amandeep, Krishna, and Vartika begin with the use cases for machine learning with Apache Spark. You’ll explore the various algorithms available in Spark MLlib and Spark ML, including those for doing basic statistics, classification and regression, collaborative filtering, clustering, dimensionality reduction, and frequent pattern mining. Along the way, you’ll solve problems using the mentioned algorithms and cover streaming k-means clustering. You’ll also learn use cases for graph processing and get an overview of programming with Spark GraphX, followed by hands-on coding examples of graph-processing problems using GraphX.
