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O'Reilly's Ben Lorica chats with Michael Stack to get an update on the Apache HBase project.

Coming Full Circle With Bigtable and HBase

O'Reilly's Ben Lorica chats with Anima Anandkumar about tensor decomposition techniques for machine learning.

The Tensor Renaissance In Data Science

O'Reilly's Ben Lorica chats with Patrick Wendell, release manager of Apache Spark and co-founder of Databricks, about how he came to join the UC Berkeley AMPLab, the current state of Spark ecosystem components, Spark’s future roadmap, and interesting applications built on top of Spark.

Apache Spark: Powering Applications On-Premise and in the Cloud

In this episode of the O’Reilly Data Show, I spoke with Mikio Braun, delivery lead and data scientist at Zalando. After spending previous years in academia, Braun recently made the decision to switch to industry. He shared some observations about building large-scale systems, particularly deploying data applications in production systems. Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.

Practical Machine Learning Techniques for Building Intelligent Applications

In this episode of the O’Reilly Data Show, I spoke with Ira Cohen, co-founder and chief data scientist at Anodot (full disclosure: I’m an advisor to Anodot). Since my days in quantitative finance, I’ve had a longstanding interest in time-series analysis. Back then, I used statistical (and data mining) techniques on relatively small volumes of financial time series. Today’s applications and use cases involve data volumes and speeds that require a new set of tools for data management, collection, and simple analysis.

On the analytics side, applications are also beginning to require online machine learning algorithms that are able to scale, are adaptive, and free of a rigid dependence on labeled data. I talked with Cohen about the challenges in building an advanced analytics system for intelligent applications at extremely large scale.

Semi-Supervised, Unsupervised, and Adaptive Algorithms for Large-Scale Time Series

In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.

Building and Deploying Large-Scale Machine Learning Applications

In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.

Deep Learning for Apache Spark

In this episode of the O’Reilly Programming Podcast, I talk about microservices with Sam Newman, presenter of the O’Reilly video course "The Principles of Microservices" and the online training course "From Monolith to Microservices." He is also the author of the book "Building Microservices: Designing Fine-Grained Systems."

Sam Newman on Moving from Monolith Systems to Microservices

In this episode of the O’Reilly Programming Podcast, I talk serverless architecture with Mike Roberts, engineering leader and co-founder of Symphonia, a serverless and cloud architecture consultancy. Roberts will give two presentations—Serverless Architectures: What, Why, Why Not, and Where Next? and Designing Serverless AWS Applications—at the O’Reilly Software Architecture Conference, October 16-19, 2017, in London.

Mike Roberts on Serverless Architectures

In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary algorithms. In a recent survey article, Stanley went through the history of neuroevolution and listed recent developments, including its applications to reinforcement learning problems.

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