The Machine Learning (ML) library for Flink is a new effort to bring scalable ML tools to the Flink
community. Our goal is is to design and implement a system that is scalable and can deal with
problems of various sizes, whether your data size is measured in megabytes or terabytes and beyond.
We call this library FlinkML.
An important concern for developers of ML systems is the amount of glue code that developers are
forced to write  in the process of implementing an end-to-end ML system. Our goal with FlinkML
is to help developers keep glue code to a minimum. The Flink ecosystem provides a great setting to
tackle this problem, with its scalable ETL capabilities that can be easily combined inside the same
program with FlinkML, allowing the development of robust pipelines without the need to use yet
another technology for data ingestion and data munging.
Another goal for FlinkML is to make the library easy to use. To that end we will be providing
detailed documentation along with examples for every part of the system. Our aim is that developers
will be able to get started with writing their ML pipelines quickly, using familiar programming
concepts and terminology.
Contrary to other data-processing systems, Flink exploits in-memory data streaming, and natively
executes iterative processing algorithms which are common in ML. We plan to exploit the streaming
nature of Flink, and provide functionality designed specifically for data streams.
FlinkML will allow data scientists to test their models locally and using subsets of data, and then
use the same code to run their algorithms at a much larger scale in a cluster setting.
We are inspired by other open source efforts to provide ML systems, in particular
scikit-learn for cleanly specifying ML pipelines, and Spark’s
MLLib for providing ML algorithms that scale with problem and
The roadmap below can provide an indication of the algorithms we aim to implement for the library.
Items in bold have already been implemented:
- Pipelines of transformers and learners
- Data pre-processing
- Feature scaling
- Polynomial feature base mapper
- Feature hashing
- Feature extraction for text
- Dimensionality reduction
- Model selection and performance evaluation
- Model evaluation using a variety of scoring functions
- Cross-validation for model selection and evaluation
- Hyper-parameter optimization
- Supervised learning
- Optimization framework
- Stochastic Gradient Descent
- Generalized Linear Models
- Multiple linear regression
- LASSO, Ridge regression
- Multi-class Logistic regression
- Random forests
- Support Vector Machines
- Decision trees
- Optimization framework
- Unsupervised learning
- K-means clustering
- Principal Components Analysis
- Text analytics
- Statistical estimation tools
- Distributed linear algebra
- Streaming ML
How can I help?
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary,
and M. Young. Machine learning: The high interest credit card of technical debt. In SE4ML:
Software Engineering for Machine Learning (NIPS 2014 Workshop), 2014.