The document presents an overview of Apache Flink, highlighting its advantages for data-intensive applications, particularly in machine learning. It discusses Flink’s capabilities, its ecosystem including FlinkML and other related frameworks, and Radicalbit's contributions to enhance these functionalities. The session concludes with an emphasis on the future potential of Flink for real-time applications and distributed learning.

1. Milan – July 13 2016
Data Intensive Applications with Apache Flink
Simone Robutti
Machine Learning Engineer at Radicalbit
@SimoneRobutti

2. Agenda
1. Brief Introduction to Apache Flink
○ Why
○ What
○ How
2. Machine Learning on Flink
○ Present landscape
○ Future of the Ecosystem
3. Closing notes on Radicalbit (shameless plug ahead)

3. 100% Buzzword-free guaranteed
Big Data
Machine
Intelligence
Web-scale
400x
It’s like the
human brain
Exactly-once
Exactly-once

4. Why Flink (and not Spark/Storm/Samza...)
Because it’s
production-ready
streaming-first
low-latency
fault-tolerant
high-throughput
processing engine

5. Flink: what is it?
From Flink’s Documentation

6. Connectors and integrations

7. Flink’s Runtime
From Flink’s Documentation

8. Flink’s DataFlow
From Flink’s Documentation
Written by the user through DataSet/DataStream API
Compiled and optimized in the client

9. Flink’s DataFlow
From Flink’s Documentation
The compiled job is translated to distributed tasks by
the master and executed by workers

10. Machine Learning on Flink

11. Ready and awesome for parallel ML
Work in progress for distributed ML
ML on Flink

12. Flink for Model Evaluation Pipelines
Source
Data
Preparation
Evaluation Sink
Source
Post
process
-ing
Composable, modular Flink Operator

13. Evaluation with Flink-JPMML
Source
Operator
Flink -
JPMML
Operator
Sink
Operator
Source
Operator
model.pmml
Small library that implements basic model eval.
Data
Preparation

14. “I have seen people insisting on using Hadoop for
datasets that could easily fit on a flash drive and could
easily be processed on a laptop.”
- Yann LeCun
-
ML on Flink

16. FlinkML
What: Out-of-the-box workhorse algorithms (ALS,
SVM, LinReg, LogReg …)
Status: early phase, slow development

17. FlinkML
Pro: available out of the box, written with Flink API
Cons: reinvents the wheel, only a few algorithms,
no model persistence

18. Samsara
What: Linear algebra framework
Status: mature

19. Samsara
Pro: generic algorithms with platform-specific
bindings, skilled community
Cons: covers only a few use cases

20. SAMOA
What: Online learning algorithm framework (VHT,
AMR, …)
Status: early phase, complicated relationship with
the industry

21. SAMOA
Pro: many powerful generic online learning
algorithms, backed by academics (MOA, Weka)
Cons: not production ready, academic focus

22. ML on Flink: the future of the ecosystem

23. Apache Beam
Programming model for data processing pipelines
● Streaming first, batch as a bounded stream
● Layered API: What, Where, When, How
● Platform agnostic: same program, different
runners

24. Apache Beam - Runners
● Flink
● Spark (Partial)
● Google Cloud Dataflow
● Plain Java
● Gearpump (WIP)
● Apex (WIP)

25. BeamML: a runner-agnostic ML library

26. FlinkML Roadmap
● More algorithms!
● Evaluation framework
● Persistence/export
● Online Learning Framework

27. Proteus
Online Learning Platform - based on Flink
Source: Proteus’ website

28. The role of Radicalbit

29. Contributions
● Cassandra Connector
● Scala API extensions
● FlinkML (Linear Algebra Framework, MinHash)
● Akka Connector

30. Our vision
Flink can become the ideal choice to build real-time decision-
heavy applications with high data-throughput
To achieve this:
● Ambitious applications (aim for real-time services)
● Reliable distributed online learning (Proteus?)
● A Pipelining Framework (experiment fast, increase testability and
modularity)

31. Q&A

32. THANKS!
Simone Robutti
Mail: simone.robutti@radicalbit.io Medium: @simone.robutti
Twitter: @SimoneRobutti — @weareradicalbit