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

The O'Reilly Data Show Podcast

Podcasts

Top 50: Technology

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The full version of "We All Become" from the Transistor Original Soundtrack. Get it here: http://bit.ly/TransistorOST

Transistor - We All Become

For promotional use only. (c) & (P) 2015 Sony Computer Entertainment Inc.

Buy Bloodborne

Bloodborne: Cleric Beast

Music for the 101 Trailer of Deus Ex: Mankind Divided, composed by @sonicmayhem - https://www.youtube.com/watch?v=ZGGaVCCMgfw
Deus Ex: Mankind Divided is coming August 23rd, 2016 on PC, PS4 and Xbox One.

Deus Ex: Mankind Divided - 101 Trailer Music by Sonic Mayhem

In this podcast, Deam Wampler discusses fast data, streaming, microservices, and the paradox of choice when it comes to the options available today building data pipelines.

Why listen to this podcast:

* Apache Beam is fast becoming the de-facto standard API for stream processing
* Spark is great for batch processing, but Flink is tackling the low-latency streaming processing market
* Avoid running blocking REST calls from within a stream processing system - have them asynchronously launched and communicate over Kafka queues
* Visibility into telemetry of streaming processing systems is still a new field and under active development
* Running the fast data platform is easily launched on an existing or new Mesosphere DC/OS runtime

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Show Notes

Fast Data with Dean Wampler

Three themes from Detroit: Become Human, each focusing on one of the title's main characters, and each scored by a different composer.

Detroit: Become Human launches May 25, 2018 on PS4.

Detroit: Become Human — Markus Main Theme By John Paesano

Detroit: Become Human — Kara Main Theme By Philip Sheppard

Detroit: Become Human — Connor Main Theme By Nima Fakhrara

In this episode of the Data Show, I spoke with Ashok Srivastava, senior vice president and chief data officer at Intuit. He has a strong science and engineering background, combined with years of applying machine learning and data science in industry. Prior to joining Intuit, he led the teams responsible for data and artificial intelligence products at Verizon. I wanted his perspective on a range of issues, including the role of the chief data officer, ethics in machine learning, and the emergence of AI technologies for enterprise products and applications.

The Real Value of Data Requires a Holistic View of the End- to-End Data Pipeline

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 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

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Genna, Dodger & Jesse are joined by Crendor in this episode of the Co-Optional Podcast. Enjoy!


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#CoOptionalPodcast #Crendor #GennaBain #JesseCox #DexBonus

The Co-Optional Podcast Ep. 247 ft. Crendor

In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play. This shouldn’t come as a shock, as current machine learning and AI technologies require large amounts of data—specifically, labeled data for training models. There are also many other considerations—including security, privacy, reliability/safety—that are encouraging companies to invest in a suite of data technologies. In conversations with data engineers, data scientists, and AI researchers, the need for solutions that can help track data lineage and provenance keeps popping up.

There are several San Francisco Bay Area companies that have embarked on building data lineage systems—including Salian and his colleagues at Stitch Fix. I wanted to find out how they arrived at the decision to build such a system and what capabilities they are building into it.

Why Companies are in Need of Data Lineage Solutions

with Jorge Conde (@JorgeCondeBio), Julie Yoo (@julesyoo), and Hanne Tidnam (@omnivorousread)

Building a software company in healthcare is hard -- and comes along with unique challenges no other entrepreneurs face. In this conversation, a16z bio general partner -- and previous founder of genomics company Knome -- Jorge Conde; and a16z bio partner and former founder Julie Yoo (of patient provider matching system, Kyruus) share their mistakes and hard earned lessons learned with a16z partner Hanne Tidnam. 

Why is this so damn hard? How should founders think about this space differently? What are the specific things that healthcare founders can do -- when, where, and why? You'll wish you only knew this when you started your own company!

a16z Podcast: So You Wanna Build a Software Company in Healthcare?

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 to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

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 a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

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 project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

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.

Becoming a Machine Learning Practitioner

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.

Understanding Deep Neural Networks

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Why Companies are in Need of Data Lineage Solutions

O'Reilly Radar