Data science is typically done by engineers writing code in Python, R, or another scripting language. Lots of engineers know these languages, and their ecosystems have great library support. But these languages have some issues around deployment, reproducibility, and other areas. The programming language Golang presents an appealing alternative for data scientists. Daniel Whitenack transitioned from doing most of his data science work in Python to writing code in Golang. In this episode, Daniel explains the workflow of a data scientist and discusses why Go is useful. We also talk about the blurry line between data science and data engineering, and how Pachyderm is useful for versioning and reproducibility. Daniel works at Pachyderm, and listeners who are more curious about it can check out the episode I did with Pachyderm founder Joe Doliner. The post Go Data Science with Daniel Whitenack appeared first on Software Engineering Daily.

Translation is a classic problem in computer science. How do you translate a sentence from one human language into another? This seems like a problem that computers are well-suited to solve. Languages follow well-defined rules, we have lots of sample data to train our machine learning models. And yet, the problem has not been solved–largely because languages don’t always follow rules. We have idioms and subtle contextual clues that make it hard to provide a computer with hard and fast rules for translation. Unbabel is a company whose solution to translation puts a human in the loop to correct the error-prone translations that computers often make. In this episode, Vasco Pedro joins the show to explain Unbabel’s approach to translation, its technology stack, and the business applications for translation. The post Translation with Vasco Pedro appeared first on Software Engineering Daily.

Medical imaging is used to understand what is going on inside the human body and prescribe treatment. With new image processing and machine learning techniques, the traditional medical imaging techniques such as CT scans can be enriched to get a more sophisticated diagnosis. HeartFlow uses data from a standard CT scan to model a human heart and understand blockages of blood flow using simulations of fluid dynamics. In today’s episode, Razik Yousfi and Leo Grady from HeartFlow describe the data processing pipeline for the company and what their technology stack looks like. The post Medical Machine Learning with Razik Yousfi and Leo Grady appeared first on Software Engineering Daily.

Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for data science and the different attempts at creating a data visualization library. Jake VanderPlas is the Director of Research for Physical Sciences at the University of Washington’s eScience institute, where he also received his PhD in Astronomy. In addition to contributing to many Python data science libraries like scikit-learn, scipy, numpy, and matplotlib, he’s written multiple books that have been published by O’Reilly and has given many talks on data science tools and techniques. He’s also the co-creator of the Altair project, which is a declarative data visualization library for Python built on the Vega-Lite visualization grammar. The post Python Data Visualization with Jake VanderPlas appeared first on Software Engineering Daily.

Data engineering is the software engineering that enables data scientists to work effectively. In today’s episode, we explore the different sides of data engineering–the data science algorithms that need to be processed and the implementation of software architectures that enable those algorithms to run smoothly. The PANCAKE STACK is a 12-letter acronym that Chris Fregly gave to a collection of data engineering technologies including Presto, Cassandra, Kafka, Elastic Search, and Spark. In his current life, Chris travels around the world giving workshops on how to deploy and use the PANCAKE STACK. Before that, he was an engineer at Netflix, where he received an Emmy for Streaming Engineering Excellence. The post PANCAKE STACK Data Engineering with Chris Fregly appeared first on Software Engineering Daily.

Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert. Srini Kadamati hosts today’s show and interviews Andreas Mueller, a core committer to scikit-learn. Srini and Andreas discuss the background and implementation of scikit-learn and walk through some prototypical workflows for using it. The post Scikit-learn with Andreas Mueller appeared first on Software Engineering Daily.

Machine learning can be used to generate music. In the case of Feynman Liang’s research project BachBot, the machine learning model is seeded with the music of famous composer Bach. The music that BachBot creates sounds remarkably similar to Bach, although it has been generated by an algorithm, not by a human.   BachBot is a research project on computational creativity. Feynman Liang created BachBot using Python machine learning tools to build a long-short term memory model. Our conversation explores artificial intelligence, music, and his approach to this research project. The post Music Deep Learning with Feynman Liang appeared first on Software Engineering Daily.

You have probably read a news article that was written by a machine. When earnings reports come out, or a series of sports events like the Olympics occurs, there are so many small stories that need to be written that a news organization like the Associated Press would have to use all of its resources to write enough content to cover it all.   Wordsmith is a tool for automated content generation, and today’s guest Robbie Allen is the CEO of Automated Insights, the company that makes Wordsmith. He talks today about the wide range of uses for automated content, as well as how to engineer a product that takes data from a spreadsheet and turns it into a human-readable sentence. Robbie is also speaking at the O’Reilly Artificial Intelligence Conference in New York, September 26-27. The post Automated Content with Robbie Allen appeared first on Software Engineering Daily.

Research in artificial intelligence takes place mostly at universities and large corporations, but both of these types of institutions have constraints that cause the research to proceed a certain way. In a university, basic research might be hindered by lack of funding. At a big corporation, the researcher might be encouraged to study a domain that is not squarely in the interest of public good–such as targeted advertising.   Oren Etzioni is the CEO of the Allen Institute for Artificial Intelligence, and in this episode we discuss AI research–from the doomful premonitions of Nick Bostrom to the unbridled optimism of Ray Kurzweil, as well as the realities of how AI research actually proceeds. Projects at the Allen Institute are defined and structured to solve problems in an intelligent, scalable fashion, so that engineering can proceed steadily from the local maxima of a problem domain to the global maxima. The Allen Institute seeks to bridge the gap by providing ample funding for open source AI research for the common good.   Oren is also speaking at the O’Reilly Artificial Intelligence Conference in New York, September 26-27. The post Artificial Intelligence with Oren Etzioni appeared first on Software Engineering Daily.

TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the process of deploying a machine learning model from research to production, so we also talk about that, as well as how TensorFlow can be used effectively in combination with Google’s open-source cluster manager, Kubernetes. The post TensorFlow in Practice with Rajat Monga appeared first on Software Engineering Daily.