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DE Presentation v2
- 2. Motivation ● Interested in hearing live music, but don’t know where to go?
- 3. Demo
- 4. Pipeline
- 5. Data Sources
- 6. Data Sources
- 7. Data Sources
- 8. Data Sources
- 9. Data Sources
- 10. Pipeline
- 12. Database
- 13. Pipeline
- 14. Scaling 500gb Artist Data 9 Hours 500gb Event Data
- 15. Lessons Learned (the hard way!) ● Scala ● Parallelized ML algorithms
- 16. About Me B.A., Mount Holyoke College Major: Mathematics Minor: Computer Science Education Interests Reading Art History Hiking Stephanie Stark
- 17. Future Work Implement TF/IDF compatibility for project Use PCA Implement cosine similarity for feature clustering Cluster within metro area Use Redis as a cache for feature vectors
Editor's Notes
- Some picture relating to actual project … maybe 3 or 4 related to each other?? Music + choices.
- Pictures???
- advantage of using kafka for just batch. EXPLAIN or delete!!!!! Add in HDFS.
- vector in n-dimensional space for better visualization
- vector in n-dimensional space for better visualization
- vector in n-dimensional space for better visualization
- explain what songkick, echonest, freq, & wt ; size of data
- explain what songkick, echonest, freq, & wt ; size of data
- advantage of using kafka for just batch. EXPLAIN or delete!!!!! Add in HDFS.
- Talk about trying to implement PCA… ugh
- what is partition key, clustering column, why??
- advantage of using kafka for just batch. EXPLAIN or delete!!!!! Add in HDFS.