Recent & Upcoming Talks

2019

Challenges for Machine Learning Systems toward Continuous Improvement

When executing machine learning pipelines for trainings and inferences, the systems and machine learning infrastructures vary depending …

How do you debug/test your Workflow?

Developping and testing for workflows productively is hard. In this session, I talk about how to develop heavy data dependent workflow …

2017

Train, predict, and serve: How to put your machine learning model into production

Adopting a machine learning system is an essential step for enterprise companies to progress to the next stage of their business. …

Invited talk: データサイエンティストからみた統合されたデータ分析基盤の恩恵

In this session, we will introduce the benefits of the integrated data analysis platform, which is important for using data in the …

Cloudera Data Science WorkbenchとPySparkを使って好きなPythonライブラリを分散で使う

An introduction of using artibary Python packages on PySpark with Cloudera Data Science Workbench

A data enginnering and data science platform based on Hadoop/Spark

Using Cloudera Enterprise, it is possible to build and operate an enterprise-grade Hadoop/Spark platform. To make use of big data, what …