Presentation: What One Should Know About Spark MLlib

Track: Hands-on Codelabs & Speakers Office Hours

Location: Mission

Duration: 4:00pm - 4:10pm

Day of week: Tuesday

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Abstract

The goal of Spark MLlib is make practical machine learning scalable and easy. In addition to providing a set of common learning algorithms such as classification, regression, clustering, and collaborative filtering, it also provides a set of tools to help with building maintainable Machine Learning pipelines. This talk will dive into the concepts, details of these tools as well as the benefits they provide.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Hien Luu

Engineering Manager @Linkedin focused on Big Data

Hien Luu is an engineering manager at LinkedIn and he is a big data enthusiast. He is particularly passionate about the intersection between Big Data and Artificial Intelligence. Teaching is one his passions and he is currently teaching Apache Spark course at UCSC Silicon Valley Extension school. He has given presentations at various conferences like QCon SF, QCon London, Hadoop Summit, JavaOne, ArchSummit and Lucene/Solr Revolution.

Find Hien Luu at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.