Remember me

Register  |   Lost password?

 

Our popular course Introduction to QuantLib Development will be taking place June 18-20th, 2018.

 

The Practical Quant's Blog

The Practical Quant Blog Header

The evolution of data science, data engineering, and AI

May 24, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: A special episode to mark the 100th episode.This episode of the Data Showmarks 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...

Companies in China are moving quickly to embrace AI technologies

May 10, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Jason Dai on the first year of BigDL and AI in China.In this episode of the Data Show, I spoke with Jason Dai, CTO of Big Data Technologies at Intel, and one of my co-chairs for the AI Conference in Beijing. I wanted to check in on the status of BigDL, specifically how companies have been using this deep learning library on top of Apache Spark, and discuss some newly added features. It turns out there are...

How to build analytic products in an age when data privacy has become critical

May 3, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.In this post, I share slides and notes from a talk I gave in March 2018 at the Strata Data Conference in California, offering suggestions for how companies may want to build analytic products in an age when data privacy has become critical. A lot has changed since I gave this...

Teaching and implementing data science and AI in the enterprise

April 26, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jerry Overton on organizing data teams, agile experimentation, and the importance of ethics in data science.In this episode of the Data Show, I spoke with Jerry Overton, senior principal and distinguished technologist at DXC Technology. I wanted the perspective of someone who works across industries and with a variety of companies. I specifically wanted to explore the current state of data science and AI...

Building tools for the AI applications of tomorrow

April 26, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.By Ben Lorica and Mike LoukidesFor the last few years, AI has been almost synonymous with deep learning (DL). We’ve seen AlphaGo touted as an example of deep learning. We’ve seen deep learning used for naming paint colors (not very successfully), imitating Rembrandt and other great painters, and many other applications. Deep learning has...

The importance of transparency and user control in machine learning

April 12, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time...

What machine learning engineers need to know

March 29, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.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...

How to train and deploy deep learning at scale

March 15, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.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...

Using machine learning to monitor and optimize chatbots

March 6, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer...

Unleashing the potential of reinforcement learning

March 1, 2018 Comments (0)

[A version of they post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.In this episode of the Data Show, I spoke with Danny Lange, VP of AI and machine learning at Unity Technologies. Lange previously led data and machine learning teams at Microsoft, Amazon, and Uber, where his teams were responsible for building data science tools used by other developers and analysts...