Machine learning for operational analytics and business intelligence
O'Reilly Data Show
by Mac Slocum
4y ago
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 in ..read more
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Machine learning and analytics for time series data
O'Reilly Data Show
by Mac Slocum
4y ago
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 c ..read more
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Understanding deep neural networks
O'Reilly Data Show
by Mac Slocum
4y ago
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 applic ..read more
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Becoming a machine learning practitioner
O'Reilly Data Show
by Mac Slocum
4y ago
In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit. Anatomy of an Alexa skill. Image by Kesha Williams, used with permission.We had a great conversation spanning many topics, including: H ..read more
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Labeling, transforming, and structuring training data sets for machine learning
O'Reilly Data Show
by Mac Slocum
4y ago
In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services. Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new proj ..read more
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Make data science more useful
O'Reilly Data Show
by Mac Slocum
4y ago
In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes “decision intelligence” as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations’ core businesses. We had ..read more
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Acquiring and sharing high-quality data
O'Reilly Data Show
by Mac Slocum
4y ago
In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O’Reilly’s Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, Fair value and decentralized governance of data. Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases ..read more
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Tools for machine learning development
O'Reilly Data Show
by Mac Slocum
4y ago
In this week’s episode of the Data Show, we’re featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML). Here are a few highlights: Tools for productive collaboration A data catalog, at a high level, basically answers questions around the data that’s available and who is using it so an enterprise can understand access patterns. … The term “data catalog” is generally used when ..read more
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Enabling end-to-end machine learning pipelines in real-world applications
O'Reilly Data Show
by Mac Slocum
4y ago
In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end machine learning pipelines. We had a great conversation spanning many topics, including: AI Fairness 360 (AIF360), a set of fairness metrics for data sets and machine learning models Adversarial Robustness Tool ..read more
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Bringing scalable real-time analytics to the enterprise
O'Reilly Data Show
by Mac Slocum
4y ago
In this episode of the Data Show, I spoke with Dhruba Borthakur (co-founder and CTO) and Shruti Bhat (SVP of Product) of Rockset, a startup focused on building solutions for interactive data science and live applications. Borthakur was the founding engineer of HDFS and creator of RocksDB, while Bhat is an experienced product and marketing executive focused on enterprise software and data products. Their new startup is focused on a few trends I’ve recently been thinking about, including the re-emergence of real-time analytics, and the hunger for simpler data architectures and tools.  Borthakur ..read more
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