Last month, I attended REWORK’s AI Applications Virtual Summit, which discovers machine learning tools and techniques to improve the financial, retail, and insurance experience. As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.
In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the summit. …
The 63rd episode of Datacast is my conversation with azin asgarian — an applied research scientist on Georgian’s R&D team, where she works with companies to help adopt applied research techniques to overcome business challenges.
Our wide-ranging conversation touches on her foray into studying math and computer science in Iran, her academic research on facial detection analysis at the University of Toronto, the benefits of being a teaching assistant, her interesting projects with Georgian Partners, real-world applications of transfer learning, and much more.
Last week, I attended apply(), Tecton’s first-ever conference that brought together industry thought leaders and practitioners from over 30 organizations to share and discuss ML data engineering’s current and future state. The complexity of ML data engineering is the most significant barrier between most data teams and transforming their applications and user experiences with operational ML.
In this long-form blog recap, I will dissect content from 23 session and lightning talks that I found most useful from attending apply(). These talks cover everything from the rise of feature stores and the evolution of MLOps, to novel techniques and scalable platform…
Our wide-ranging conversation touches on his foray into the database world, his interest in consulting, the evolution of data warehousing and business intelligence platforms, how to choose data tooling vendors, what it means to be data-driven, effective collaboration for data teams, data “hierarchy of needs”, data for social impact, and much more.
We had a wide-ranging conversation covering his interest in programming growing up, his foray into AI research, the intersection of meta-learning and reinforcement-learning, contemporary challenges in AI, working with professor Schmidhuber, and much more.
Here are highlights from my conversation with Louis:
I started programming when I was about 10. I…
A few weeks ago, I attended Transform, Scale AI’s first-ever conference that brought together an all-star line-up of the leading AI researchers and practitioners. The conference featured 19 sessions discussing the latest research breakthroughs and real-world impact across industries.
In this long-form blog recap, I will dissect content from the session talks that I found most useful from attending the conference. These talks cover everything from the future of ML frameworks and the importance of a data-centric mindset to AI applications at companies like Facebook and DoorDash. …
The 60th episode of Datacast is my interview with Dzejla Medjedovic— the Assistant Professor of Computer Science at the International University of Sarajevo and the author of “Algorithms and Data Structures for Massive Datasets.”
We had a wide-ranging conversation covering her foray into studying Computer Science, her Ph.D. in Applied Algorithms at Stony Brook University, her love for teaching, her industry internships at Microsoft, her Manning book, the tech scene in Sarajevo, and much more.
Last week, I attended DataOps Unleashed, a great event that examines the emergence of DataOps, CloudOps, AIOps, and other professionals coming together to aggregate conversations around the latest trends and best practices for running, managing, and monitoring data pipelines.
In this long-form blog recap, I will dissect content from the session talks that I found most useful from attending the summit. These talks are from DataOps professionals at leading organizations detailing how they establish data predictability, increase reliability, and create economic efficiencies with their data pipelines.
As the world’s leading tool for data quality, Great Expectations occupies a…
We had a wide-ranging conversation that covers his entrepreneurial journey founding and selling a networking startup in college, his time building industrial data systems, his work on designing and scaling Gojek’s Machine Learning platform, his well-known open-source feature store Feast, his move to Tecton to build an enterprise-grade feature store, and much more.
The 58th episode of Datacast is my interview with Jim Dowling — the CEO of Logical Clocks AB, an Associate Professor at KTH Royal Institute of Technology, and a Senior Researcher at SICS RISE in Stockholm.
We had a wide-ranging conversation that covers his Ph.D. in distributed systems, his applied research work at RISE, his teachings at KTH, his explanation of distributed deep learning, his contribution to HopsFS, the development of Logical Clocks’ Hopsworks platform, the rise of feature stores in ML pipelines, public-research vs. VC-funded money, worrying trends in the European tech ecosystem, and much more.