Apache PredictionIO

This presentation gives an overview of the Apache PredictionIO project. It covers areas like architecture, features, model deployment and development.

Links for further information and connecting

http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/

https://nz.linkedin.com/pub/mike-frampton/20/630/385

https://open-source-systems.blogspot.com/

Music by

"Little Planet", composed and performed by Bensound from http://www.bensound.com/

Transcript

1. What Is PredictionIO ? • A Machine Learning Server • Uses Customizable Model Templates • Powered By Apache Spark • Offers SDK's for Java, PHP, Python, Ruby • Apache 2 License – Open Source • Query in Real Time
2. PredictionIO Architecture
3. PredictionIO Architecture PredictionIO Contains • PredictionIO Platform – Stack For Engine Management • Event Server – Process Events / Data From Multiple Sources • Template Gallery – Source Engine Templates
4. PredictionIO Event Server • Event Server Collects App Data • Sends Data to Model Engines • Real Time or Batch • Supports Multiple Apps • Send Data via HTTP to Server • Send Data via SDK's to Server
5. PredictionIO Engine • Engines Make Predictions • Deployed As Web Service • Responds Via REST API • Uses Training Data to Create Models • See Template Gallery For Engines
6. PredictionIO Dependencies • Scala • Apache Spark 2.0.2 + • Hadoop 2.6.5 + ( Optional ) • Java 8 • One Of The Following – PostgreSQL 9.1 – MySQL 5.1 – Apache HBASE 0.98.5 / ElasticSearch 1.7.6
7. PredictionIO Configuration • PredictionIO Has Three Repositories – Event, Meta, Model Data • Storage Can Be – PostreSQL ( All Data ) – HBase ( Event Data ) – ElasticSearch ( Meta Data ) – MySQL ( All Data ) – Local File System ( Model Data ) – HDFS ( Model Data ) – AWS S3 ( Model Data )
8. PredictionIO Engine • Download Engine Template • Using poi Tool – Build – Updates Engine – Train – Creates Predictive Model – Deploy – As Web Service • Use Existing Template (or) • Customize Template (or) • Build New Template
9. PredictionIO Engine Development • Engines Use D-A-S-E components – Data Source / Data Preparator – Algorithm ( ML ) – Serving ( Prediction Queries ) – Evaluation Metrics ( Accuracy ? ) • Deploy As Web Service • Serves Apps Via REST API
10. PredictionIO Engine Development
11. Available Books • See “Big Data Made Easy” – Apress Jan 2015 • See “Mastering Apache Spark” – Packt Oct 2015 • See “Complete Guide to Open Source Big Data Stack – “Apress Jan 2018” – • Find the author on Amazon – www.amazon.com/Michael-Frampton/e/B00NIQDOOM/ • Connect on LinkedIn – www.linkedin.com/in/mike-frampton-38563020
12. Connect • Feel free to connect on LinkedIn – www.linkedin.com/in/mike-frampton-38563020 • See my open source blog at – open-source-systems.blogspot.com/ • I am always interested in – New technology – Opportunities – Technology based issues – Big data integration