Machine Learning Archives - Software Engineering Daily

“Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High Interest Credit Card of Technical Debt. Questions How do you define technical debt? Why does technical debt tend to compound like financial debt? Is machine learning the marriage of hard-coded software logic and constantly changing external data? What types of anti-patterns should be avoided by machine learning engineers? What is a decision threshold in a machine learning system? What advice would you give to organizations that are building their prototypes and product systems in different languages? Links Technical Debt Adapter pattern and glue code D’s research page Sponsors Hired.com is the job marketplace for software engineers. Go to hired.com/softwareengineeringdaily to get a $600 bonus upon landing a job through Hired. Digital Ocean is the simplest cloud hosting provider. Use promo code SEDAILY for $10 in free credit. The post Machine Learning and Technical Debt with D. Sculley appeared first on Software Engineering Daily.

Current infrastructure makes it difficult for data scientists to share analytical models with the software engineers who need to integrate them. Yhat is an enterprise software company tackling the challenge of how data science gets done. Their products enable companies and users to easily deploy data science environments and translate analytical models into production code. Greg Lamp is the Co-founder and CTO of Yhat and previously worked as a product manager in financial services. Yhat was part of the Y Combinator winter 2015 class. Questions At a software company, what is the typical relationship between data scientists and software engineers? Does Yhat turn data scientists into HTTP endpoints? What was the most counterintuitive advice you received at Y-Combinator? What is the moonshot goal for Yhat? Is it easier to teach data science to an engineer or engineering to a data scientist? Links Yhat’s Products Yhat Blog Greg’s Website Beer Recommender Talk The post Bridging Data Science and Engineering with Greg Lamp appeared first on Software Engineering Daily.

Data science competitions are an effective way to crowdsource the best solutions for challenging datasets. Kaggle is a platform for data scientists to collaborate and compete on machine learning problems with the opportunity to win money from the competitions’ sponsors. Ben Hamner is the co-founder and CTO of Kaggle. Questions What is Kaggle? How does the experience of an individual competitor compare to the experience of a data science team? What is Kaggle’s tech stack? Do companies collect too much data? How do you use machine learning to convert neural patterns into control signals? Links Kaggle Kaggle Scripts Ben Hamner on Quora Ben Hamner on Twitter The post Kaggle with Ben Hamner appeared first on Software Engineering Daily.

There is a need for more data scientists to make sense of the vast amounts of data we produce and store. Dataquest is an in-browser platform for learning data science that is tackling this problem. Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat. Questions What is data science? How does data science compare to software engineering? How does someone new to data science go about starting off at Kaggle? In machine learning, there is unsupervised learning and supervised learning. Could you contrast these two? What are the biggest world problems that will be solved with data science? Links Dataquest How to actually learn data science Kaggle The post Teaching Data Science with Vik Paruchuri appeared first on Software Engineering Daily.