Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133

Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133

In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his insights into how differential privacy can be deployed in the real world and some of the technical and cultural challenges to doing so. We discuss the Bluecore use case in depth, including why and for whom they build differentially private machine learning models. The notes for this show can be found at twimlai.com/talk/133

Episoder(774)

Deep Learning for Earthquake Aftershock Patterns with Phoebe DeVries & Brendan Meade - #311

Deep Learning for Earthquake Aftershock Patterns with Phoebe DeVries & Brendan Meade - #311

Today we are joined by Phoebe DeVries, Postdoctoral Fellow in the Department of Earth and Planetary Sciences at Harvard and Brendan Meade, Professor of Earth and Planetary Sciences at Harvard. Phoebe and Brendan’s work is focused on discovering as much as possible about earthquakes before they happen, and by measuring how the earth’s surface moves, predicting future movement location, as seen in their paper: ‘Deep learning of aftershock patterns following large earthquakes'.

25 Okt 201936min

Live from TWIMLcon! Operationalizing Responsible AI - #310

Live from TWIMLcon! Operationalizing Responsible AI - #310

An often forgotten about topic garnered high praise at TWIMLcon this month: operationalizing responsible and ethical AI. This important topic was combined with an impressive panel of speakers, including: Rachel Thomas, Director, Center for Applied Data Ethics at the USF Data Institute, Guillaume Saint-Jacques, Head of Computational Science at LinkedIn, and Parinaz Sobahni, Director of Machine Learning at Georgian Partners, moderated by Khari Johnson, Senior AI Staff Writer at VentureBeat.

22 Okt 201930min

Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309

Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309

Machine learning and AI is finding a place in the traditional enterprise - although the path to get there is different. In this episode, our panel analyzes the state and future of larger, more established brands. Hear from Amr Awadallah, Founder and Global CTO of Cloudera, Pallav Agrawal, Director of Data Science at Levi Strauss & Co., and Jürgen Weichenberger, Data Science Senior Principal & Global AI Lead at Accenture, moderated by Josh Bloom, Professor at UC Berkeley.

18 Okt 201933min

Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308

Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308

TWIMLcon brought together so many in the ML/AI community to discuss the unique challenges to building and scaling machine learning platforms. In this episode, hear about changing the way companies think about machine learning from a diverse set of panelists including Pardis Noorzad, Data Science Manager at Twitter, Eric Colson, Chief Algorithms Officer Emeritus at Stitch Fix, and Jennifer Prendki, Founder & CEO at Alectio, moderated by Maribel Lopez, Founder & Principal Analyst at Lopez Research.

15 Okt 201927min

Live from TWIMLcon! Use-Case Driven ML Platforms with Franziska Bell - #307

Live from TWIMLcon! Use-Case Driven ML Platforms with Franziska Bell - #307

Today we're Franziska Bell, Ph.D., the Director of Data Science Platforms at Uber, who joined Sam on stage at TWIMLcon last week. Fran provided a look into the cutting edge data science available company-wide at the push of a button. Since joining Uber, Fran has developed a portfolio of platforms, ranging from forecasting to conversational AI. Hear how use cases can strategically guide platform development, the evolving relationship between her team and Michelangelo (Uber’s ML Platform) and much more!

10 Okt 201932min

Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306

Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306

The live interviews from TWIMLcon continue with Hussein Mehanna, Head of ML and AI at Cruise. From his start at Facebook to his current work at Cruise, Hussein has seen first hand what it takes to scale and sustain machine learning programs. Hear him discuss the challenges (and joys) of working in the industry, his insight into analyzing scale when innovation is happening in parallel with development, his experiences at Facebook, Google, and Cruise, and his predictions for the future of ML platforms!

8 Okt 201933min

Live from TWIMLcon! Encoding Company Culture in Applied AI Systems - #305

Live from TWIMLcon! Encoding Company Culture in Applied AI Systems - #305

In this episode, Sam is joined by Deepak Agarwal, VP of Engineering at LinkedIn, who graced the stage at TWIMLcon: AI Platforms for a keynote interview. Deepak shares the impact that standardizing processes and tools have on a company’s culture and productivity levels, and best practices to increasing ML ROI. He also details the Pro-ML initiative for delivering machine learning systems at scale, specifically looking at aligning improvement of tooling and infrastructure with the pace of innovation and more

4 Okt 201932min

Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production with Andrew Ng - #304

Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production with Andrew Ng - #304

Earlier today, Andrew Ng joined us onstage at TWIMLcon - as the Founder and CEO of Landing AI and founding lead of Google Brain, Andrew is no stranger to knowing what it takes for AI and machine learning to be successful. Hear about the work that Landing AI is doing to help organizations adopt modern AI, his experience in overcoming challenges for large companies, how enterprises can get the most value for their ML investment as well as addressing the ‘essential complexity’ of software engineering.

1 Okt 201934min

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