Bootstrapping Machine Learning
Machine learning systems have matured to a point that they are now indispensable across sectors. Many machine learning applications are driven by the wealth of open source tools that are available today, allowing practitioners to focus on their particular application. Open high quality implementations, driven by both research and industry, are setting higher and higher standards, while providing easy-to use interfaces and are readily deployable.
In this talk, Andreas will give an overview of current systems for analysis and learning, such as scikit-learn (Python) and MLlib (Spark) and show how these have contributed to creating smarter applications. While these tools significantly lowered the barrier to entry to machine learning for researchers and businesses, emerging directions, such as machine learning as a service, will make development and deployment of machine learning algorithm even easier.
Andreas Mueller received his PhD in machine Learning from the University of Bonn. He is currently working as a machine learning researcher working on computer vision applications at Amazon. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.