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O'Reilly Data Show - O'Reilly Media Podcast

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The O'Reilly Data Show explores the opportunities and techniques driving big data, data science, and AI. Through interviews and analysis, we highlight the people putting data to work.

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Machine learning for operational analytics and business intelligence
The O’Reilly Data Show Podcast: Peter Bailis on data management, ML benchmarks, and building next-gen tools for analysts.In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN Lab.We had a great conversation spanning many topics, including: His personal blog, which contains some of the best explainers on emerging topics in data management and distributed systems. The role of machine learning in operational analytics and business intelligence. Machine learning benchmarks—specifically two recent ML initiatives that he’s been involved with: DAWNBench and MLPerf. Trends in data management and in tools for machine learning development, governance, and operations. Related resources: "Setting benchmarks in machine learning": Dave Patterson, Peter Bailis, and other industry leaders discuss how MLPerf will define an entire suite of benchmarks to measure performance of software, hardware, and cloud systems. “The quest for high-quality data” “RISELab’s AutoPandas hints at automation tech that will change the nature of software development” Jeff Jonas on “Real-time entity resolution made accessible” “What are model governance and model operations?” “We need to build machine learning tools to augment machine learning engineers”
Machine learning and analytics for time series data
The O’Reilly Data Show Podcast: Arun Kejariwal and Ira Cohen on building large-scale, real-time solutions for anomaly detection and forecasting.In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.We had a great conversation spanning many topics, including: Why businesses should care about anomaly detection and forecasting; specifically, we delve into examples outside of IT Operations & Monitoring. (Specialized) techniques and tools for automating some of the relevant tasks, including signal processing, statistical methods, and machine learning. What are some of the key features of an anomaly detection or forecasting system. What lies ahead for large-scale systems for time series analysis. Related resources: “Product management in the machine learning era” - a new tutorial at the Artificial Intelligence Conference in London “One simple chart: Who is interested in Apache Pulsar?” Ira Cohen: “Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series” “Got speech? These guidelines will help you get started building voice applications” “RISELab’s AutoPandas hints at automation tech that will change the nature of software development” Ameet Talwalker: “How to train and deploy deep learning at scale”
Understanding deep neural networks
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.Analyzing deep neural networks with WeightWatcher. Image by Michael Mahoney and Charles Martin, used with permission. We had a great conversation spanning many topics, including: The class of problems in big data, machine learning, and data analysis that he has worked on at Yahoo, Stanford, and Berkeley. The new UC Berkeley FODA (Foundations of Data Analysis) Institute. HAWQ (Hessian AWare Quantization of Neural Networks with Mixed-Precision), a new framework for addressing problems pertaining to model size and inference speed/power in deep learning. WeightWatcher: a new open source project for predicting the accuracy of deep neural networks. WeightWatcher stems from a recent series of papers with Charles Martin, of Calculation Consulting. Related resources: “Deep learning at scale: Tools and solutions” - a new tutorial at the Artificial Intelligence Conference in San Jose Ameet Talwalker on “How to train and deploy deep learning at scale” Greg Diamos on “How big compute is powering the deep learning rocket ship” “RISELab’s AutoPandas hints at automation tech that will change the nature of software development” Reza Zadeh on “Scaling machine learning” “Becoming a machine learning company means investing in foundational technologies” “Managing risk in machine learning” “What are model governance and model operations?” “Product management in the machine learning era”: a tutorial at the Artificial Intelligence Conference in San Jose
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    Podcast Details
    Started
    Sep 10th, 2015
    Latest Episode
    Oct 10th, 2019
    Release Period
    Weekly
    No. of Episodes
    117
    Avg. Episode Length
    39 minutes
    Explicit
    No

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