At the end of January, I attended REWORK’s Deep Learning 2.0 Virtual Summit, which brings together the latest technological advancements and practical examples to apply deep learning to solve challenges in business and society. As a previous attendee of their in-person summit, I enjoyed the unique mix of academia and industry, with sessions from deep learning pioneers at the research forefront and deep learning practitioners building real-world applications.
In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the summit. …
The 56th episode of Datacast is my interview with Alba Cervera-Lierta— a Postdoctoral fellow at the Matterlab group at the University of Toronto.
We had a wide-ranging conversation that covers her foray into studying physics; her research in Bell Inequalities, multipartite entanglement, and entanglement in fundamental interactions; her current work that intersects quantum computation and machine learning; her education and public outreach activities to spread awareness about quantum technologies, and much more.
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Breaker, and (7) RadioPublic
Key Takeaways
Here are highlights from…
The 55th episode of Datacast is my conversation with Jean-Yves Stephan — the CEO & Co-Founder of Data Mechanics, a Y-Combinator-backed startup building a data engineering platform that makes Apache Spark more developer-friendly and more cost-effective. Give it a listen to hear about his M.S. degree at Stanford, his experience leading Spark infrastructure at Databricks, his motivation to build Data Mechanics and go through YC, the tech community in France, and much more.
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Breaker, and (7) TuneIn
Key Takeaways
Here are highlights…
In January, I attended TWIMLcon, a leading MLOps and enterprise ML virtual conference. It focuses on MLOps and how enterprises can overcome the barriers to building ML models and getting them into production. There was a wide range of both technical and case-study sessions curated for ML/AI practitioners. In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the conference.
The post consists of 14 talks that are divided into 3 sections: (1) Case Study, (2) Technology, and (3) Perspectives.
1 — Case Study
1.1 — How Spotify Does ML At Scale
Over 320 million Spotify users in 92 different markets worldwide rely…
The 54th episode of Datacast is my conversation with Chris Mattmann— the Chief Technology and Innovation Officer at NASA JPL. Give it a listen to hear about his wide-ranging career: doing a Ph.D. in Software Architectures, being an Assistant Professor, and leading Information Retrieval & Data Science research at USC; developing Apache Tika and sitting on the board of the Apache Software Foundation; developing next-generation data systems that support space and earth science missions at NASA JPL; writing a book about TensorFlow, and growing up in Los Angeles.
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3)…
The 53rd episode of Datacast is my conversation with Marcello LaRocca — a research engineer with experience creating large-scale web applications and machine learning infrastructure. Give it a listen to hear about his education in Italy; his career as a full-stack developer; his time working at Twitter, Microsoft, and Apple; his book “Algorithms and Data Structures in Action”; his JavaScript library that implements various graph algorithms; his interest in quantum computing; his thoughts on the tech communities in Dublin, Zurich, and Rome; and more.
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher…
The 52nd episode of Datacast is my conversation with Dave Bechberger — a Graph Database Subject Matter Expert currently working for Amazon Neptune. Give it a listen to hear about his 20+ years developing, managing, and consulting on software projects; his pragmatic approach for implementing large-scale distributed data architectures for big data analysis and data science workflows; his book “Graph Database in Action”; and more.
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Radio Public, (7) Breaker, and (8) TuneIn
Key Takeaways
Here are the highlights from my conversation with…
How many different ways can we evaluate RecSys?
Update: This article is part of a series where I explore recommendation systems in academia and industry. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, and Part 7.
In information retrieval, evaluation metrics are used to judge and compare the performance of recommendation models on benchmark datasets. Good quantitative assessments of their accuracy are crucial to building successful recommendation systems.
- For a typical offline recommendation problem, we randomly select the training and test samples from the dataset. …
The 51st episode of Datacast is my conversation with Professor Jason Corso — the new director of the Stevens Institute for Artificial Intelligence and the co-founder/CEO of Voxel51. Give it a listen to hear about his wide-ranging computer vision research in image registration, medical imaging, visual segmentation, video understanding, and robotics; his courses at SUNY Buffalo and the University of Michigan; his startup Voxel51 that builds dataset analysis tools; his opinion of doing good research; common threads between professorship and entrepreneurship; and much more.
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher…
Every December since 2015, I have taken some time to write my annual review, a practice that was originally inspired by James Clear (see my 2019, 2018, 2017, 2016, 2015, and 2014 versions). The review's goal is to reflect on the previous year and identify what went well, what could have gone better, and what I’m working toward. This practice enables me to celebrate the efforts and milestones I have made over the past 12 months while examining the bottlenecks and limitations I could have improved upon. The review is a deeply personal report, letting me see myself for who…
