BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//hacksw/handcal//NONSGML v1.0//EN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260514T065638Z
DESCRIPTION:Click for Latest Location Information: http://edw2021.dataversi
 ty.net/sessionPop.cfm?confid=133&proposalid=12883\nFINRA was an early adopt
 er of cloud computing and machine learning using big data sets. FINRA proce
 sses up to 135 billion market events a day. How much is 135 billion, you ma
 y wonder? If one were to save $10,000 a day it would take 37,000 years to r
 each $135 billion! Human analysis and traditional software development proc
 esses cannot meet this challenge, but machine learning can. \nWe&rsquo
 ;ve made great strides in this area and would like to share our experiences
  with you! This hands-on workshop will review case studies from real-world 
 problems solved by FINRA technologists, and will demonstrate how to apply t
 he best practices we’ve established to common, real-world data sets. 
 You do not need to have experience with cloud or ML to find this session us
 eful.  \nAttendees to this three-hour tutorial will learn how to:
 \n\n
 Design a data infrastructure to support an effective machine learning platf
 orm: What’s a data lake, why is it important, and how do I find thing
 s in it?\n
 Choose an algorithm: An explanation of basic algorithms, what makes an algo
 rithm suitable for a data set, and how you apply them to data\n
 Data labeling: Harness your company’s intellectual property by labeli
 ng your data, and how labeling can improve ML model outcomes\n
 Ensure data quality: Quality is not about looking for needles in haystacks,
  it’s knowing what besides needles are hidden in there and how to fin
 d them\n
 Operational excellence: Advanced data quality checks for large complex
  big data workloads\n
 How machine learning can help with data anomaly predictions with big data\n
 \n
DTSTART:20210420T080000
SUMMARY:T15: Machine Learning – Harnessing Actionable Intelligence to Tame 
 the Data Tsunami
DTEND:20210420T105959
LOCATION: See Description
END:VEVENT
END:VCALENDAR