O'Reilly Radar

This will be a breakout year for JavaScript on embedded systems. Our guest on the Hardware Podcast this week is Peter Hoddie, who founded Kinoma, which makes software and hardware for building JavaScript-powered prototypes. Previously he was one of the original developers of QuickTime at Apple.

Peter Hoddie on JavaScript for Embedded Systems

This week, I talk with Alyona Medelyan, co-founder and CEO at Thematic and founder and CEO at Entopix. We talk about natural language understanding, the challenges of analyzing unstructured text, and her open source indexing tool Maui that she's been working on for the past 10 years.

Alyona Medelyan on Applications of NLU

In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).

We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.

How to Train and Deploy Deep Learning at Scale

In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some disagreement on whether such a specialized job role/title was needed in the first place. As Eric Colson pointed out in his beautiful keynote at Strata Data San Jose, when done too soon, creating specialized roles can slow down your data team.

We recorded this conversation at Strata San Jose, while Anderson was in the middle of teaching his very popular two-day training course on real-time systems. We closed the conversation with Anderson’s take on Apache Pulsar, a very impressive new messaging system that is starting to gain fans among data engineers.

What Machine Learning Engineers Need to Know

In this episode of the O’Reilly Programming Podcast, I talk with two of the program chairs for the upcoming O’Reilly Fluent Conference (July 11-14 in San Jose), Kyle Simpson and Tammy Everts. Simpson is co-author of the "HTML 5 Cookbook," and the author of the "You Don’t Know JS" series of books. Everts is the chief experience officer at SpeedCurve and the author of "Time is Money: The Business Value of Web Performance."

Kyle Simpson and Tammy Everts on the Challenges of the Modern Web

This episode of the Data Show marks our 100th episode. This podcast stemmed out of video interviews conducted at O’Reilly’s 2014 Foo Camp. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. We originally conceived of using those initial conversations to be the basis of a regular series of video interviews. The logistics of studio interviews proved too complicated, but those Foo Camp conversations got us thinking about starting a podcast, and the Data Show was born.

To mark this milestone, my colleague Paco Nathan, co-chair of Jupytercon, turned the tables on me and asked me questions about previous Data Show episodes. In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. I’m proud of how this show has reached so many people across the world, and I’m looking forward to sharing more conversations in the future.

The Evolution of Data Science, Data Engineering, and AI

In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.

As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”

Simplifying Machine Learning Lifecycle Management

In this episode of the Data Show, I spoke with Eric Jonas, a postdoc in the new Berkeley Center for Computational Imaging. Jonas is also affiliated with UC Berkeley’s RISE Lab. It was at a RISE Lab event that he first announced Pywren, a framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services. Jonas and his collaborators are working on a related project, NumPyWren, a system for linear algebra built on a serverless architecture. Their hope is that by lowering the barrier to large-scale (scientific) computation, we will see many more experiments and research projects from communities that have been unable to easily marshal massive compute resources. We talked about Bayesian machine learning, scientific computation, reinforcement learning, and his stint as an entrepreneur in the enterprise software space.

Building Accessible Tools for Large-Scale Computation and Machine Learning

In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future.

Using Machine Learning to Improve Dialog Flow in Conversational Applications

This episode of the O’Reilly Podcast, features a conversation on serverless and Kubernetes, with Kelsey Hightower, developer advocate for Google Cloud Platform at Google (and co-author of "Kubernetes: Up and Running"), and Chris Gaun, Kubernetes product marketing manager at Mesosphere.

Kelsey Hightower and Chris Gaun on Serverless and Kubernetes

In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.

Why It's Hard to Design Fair Machine Learning Models

In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become interested in the interplay between research in psychology, decision-making, and AI systems. He’s in the process of writing a book on these topics, and was gracious enough to give an informal preview by way of this podcast conversation.

How Social Science Research Can Inform the Design of AI Systems

In this episode of the Data Show, I spoke with Alon Kaufman, CEO and co-founder of Duality Technologies, a startup building tools that will allow companies to apply analytics and machine learning to encrypted data. In a recent talk, I described the importance of data, various methods for estimating the value of data, and emerging tools for incentivizing data sharing across organizations. As I noted, the main motivation for improving data liquidity is the growing importance of machine learning. We’re all familiar with the importance of data security and privacy, but probably not as many people are aware of the emerging set of tools at the intersection of machine learning and security. Kaufman and his stellar roster of co-founders are doing some of the most interesting work in this area.

Machine Learning on Encrypted Data

In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

Lessons Learned While Helping Enterprises Adopt Machine Learning

In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection and hyperparameter tuning, as well as monitoring.

I wanted the perspective of someone who is already faced with having to support many models in production. The proliferation of models is still a theoretical consideration for many data science teams, but Gordon and his colleagues at Salesforce already support hundreds of thousands of customers who need custom models built on custom data. They recently took their learnings public and open sourced TransmogrifAI, a library for automated machine learning for structured data, which sits on top of Apache Spark.

Building Tools for Enterprise Data Science

In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.

Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.

Tools for Generating Deep Neural Networks With Efficient Network Architectures

For the end-of-year holiday episode of the Data Show, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming Strata Data and Artificial Intelligence conferences.

Trends in Data, Machine Learning, and AI

In this episode of the Data Show, I spoke with Haoyuan Li, CEO and founder of Alluxio, a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab), specifically emerging use cases here and in China. Given the large-scale use in China, I also wanted to get Li’s take on the state of data and AI technologies in Beijing and other parts of China.

In the Age of AI, Fundamental Value Resides in Data

In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer and legal engineer at Immuta, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI. They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI.

We discussed their new white paper, cybersecurity (Burt was previously a special advisor at the FBI), and an exciting new Strata Data tutorial that Burt will be co-teaching in March.

How Machine Learning Impacts Information Security

In this episode of the Data Show, I spoke with Maryam Jahanshahi, research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively. In an upcoming survey, we found that a “skills gap” or “lack of skilled people” was one of the main bottlenecks holding back adoption of AI technologies. Many companies are exploring a variety of internal and external programs to train staff on new tools and processes. The other route is to hire new talent. But recent reports suggest that demand for data professionals is strong and competition for experienced talent is fierce. Jahanshahi and her team are building natural language and statistical tools that can help companies improve their ability to attract and retain talent across many key areas.

O'Reilly Radar

Alex Brajkovic