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

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

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