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| <h1><a href="../ml">FlinkML</a> - Vision and Roadmap</h1> |
| |
| |
| |
| <ul id="markdown-toc"> |
| <li><a href="#vision" id="markdown-toc-vision">Vision</a></li> |
| <li><a href="#roadmap" id="markdown-toc-roadmap">Roadmap</a></li> |
| </ul> |
| |
| <h2 id="vision">Vision</h2> |
| |
| <p>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.</p> |
| |
| <p>An important concern for developers of ML systems is the amount of glue code that developers are |
| forced to write [1] 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.</p> |
| |
| <p>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.</p> |
| |
| <p>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.</p> |
| |
| <p>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.</p> |
| |
| <p>We are inspired by other open source efforts to provide ML systems, in particular |
| <a href="http://scikit-learn.org/">scikit-learn</a> for cleanly specifying ML pipelines, and Spark’s |
| <a href="https://spark.apache.org/mllib/">MLLib</a> for providing ML algorithms that scale with problem and |
| cluster sizes.</p> |
| |
| <h2 id="roadmap">Roadmap</h2> |
| |
| <p>The roadmap below can provide an indication of the algorithms we aim to implement in the coming |
| months. If you are interested in helping out, please check our <a href="contribution_guide.html">contribution guide</a>. |
| Items in <strong>bold</strong> have already been implemented:</p> |
| |
| <ul> |
| <li>Pipelines of transformers and learners</li> |
| <li>Data pre-processing |
| <ul> |
| <li><strong>Feature scaling</strong></li> |
| <li><strong>Polynomial feature base mapper</strong></li> |
| <li>Feature hashing</li> |
| <li>Feature extraction for text</li> |
| <li>Dimensionality reduction</li> |
| </ul> |
| </li> |
| <li>Model selection and performance evaluation |
| <ul> |
| <li>Cross-validation for model selection and evaluation</li> |
| </ul> |
| </li> |
| <li>Supervised learning |
| <ul> |
| <li>Optimization framework |
| <ul> |
| <li><strong>Stochastic Gradient Descent</strong></li> |
| <li>L-BFGS</li> |
| </ul> |
| </li> |
| <li>Generalized Linear Models |
| <ul> |
| <li><strong>Multiple linear regression</strong></li> |
| <li>LASSO, Ridge regression</li> |
| <li>Multi-class Logistic regression</li> |
| </ul> |
| </li> |
| <li>Random forests</li> |
| <li><strong>Support Vector Machines</strong></li> |
| </ul> |
| </li> |
| <li>Unsupervised learning |
| <ul> |
| <li>Clustering |
| <ul> |
| <li>K-means clustering</li> |
| </ul> |
| </li> |
| <li>PCA</li> |
| </ul> |
| </li> |
| <li>Recommendation |
| <ul> |
| <li><strong>ALS</strong></li> |
| </ul> |
| </li> |
| <li>Text analytics |
| <ul> |
| <li>LDA</li> |
| </ul> |
| </li> |
| <li>Statistical estimation tools</li> |
| <li>Distributed linear algebra</li> |
| <li>Streaming ML</li> |
| </ul> |
| |
| <p><strong>References:</strong></p> |
| |
| <p>[1] D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, |
| and M. Young. <em>Machine learning: The high interest credit card of technical debt.</em> In SE4ML: |
| Software Engineering for Machine Learning (NIPS 2014 Workshop), 2014.</p> |
| |
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