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Deploying Machine Learning Models with Pulsar Functions

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Timothy Spann
StreamNative, Developer Advocate

Tim Spann is a Developer Advocate @ StreamNative where he works with Apache Pulsar, Apache Flink, Apache NiFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.

https://www.datainmotion.dev/p/about-me.html
https://dzone.com/users/297029/bunkertor.html
https://conferences.oreilly.com/strata/strata-ny-2018/public/schedule/speaker/185963

David Kjerrumgaard
StreamNative, Developer Advocate

David Kjerrumgaard is a committer on the Apache Pulsar project and serves as a Developer Advocate for StreamNative with a focus on helping developers solve their streaming data challenges using Apache Pulsar.He was formerly the Global Practice Director at Hortonworks, where he was responsible for the development of best practices and solutions for the professional services team, with a focus on Streaming technologies including Kafka, NiFi, and Storm. Furthermore, he has over 15 years of experience working with open-source projects in the Big Data, Stream Processing, and Distributed Computing spaces.


In this talk I will present a technique for deploying machine learning models to provide real-time predictions using Apache Pulsar Functions. In order to provide a prediction in real-time, the model usually receives a single data point from the caller, and is expected to provide an accurate prediction within a few milliseconds. 

Throughout this talk, I will demonstrate the steps required to deploy a fully-trained ML that predicts the delivery time for a food delivery service based upon real-time traffic information, the customer's location, and the restaurant that will be fulfilling the order.