The 68th episode of Datacast is my conversation with Sarah Catanzaro — a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems.
Our wide-ranging conversation touches on her interest in terrorism analysis growing up; her early career in threat intelligence for the public sector; her transition to startups as the Head of Data at Mattermark; her move to venture capital and the criticality of having high stamina; her investments at Amplify; her predictions for emerging trends in the data tooling ecosystem, and much more.
Our wide-ranging conversation touches on her educational background in Electrical Engineering and Computer Science at UC Berkeley; her work on the model store for Uber’s Marketplace; the difference between academic ML and production ML; the case for researching AI Bias; her current journey with Arize to tackle real-time ML observability; her predictions for the ML infrastructure ecosystem, and much more.
Please enjoy my conversation with Aparna!
Listen to the show on (1) Spotify, (2)…
Earlier of June 2021, I attended two great summits organized by REWORK: The MLOps summit that discovers how to optimize the ML lifecycle & streamline ML pipeline for better production and the ML Fairness summit that discovers strategies to ensure ML models are accountable & fair to build secure & responsible AI.
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…
Our wide-ranging conversation touches on her educational background in Applied Mathematics and Computer Science; her work on recommendation systems and applied ML at Yandex; her popular teaching materials online and in-person; her startup on Industrial AI; her current journey with Evidently to tackle the model monitoring space, and much more.
Please enjoy my conversation with Emeli!
The 65th episode of Datacast is my conversation with David Sweet— an experienced quantitative trader and machine learning engineer who has used experimental methods to tune large-scale trading and recommendation systems.
Our wide-ranging conversation touches on his educational background in Physics and Ph.D. work on chaos theory; his work on open-source software and open content in the early 2000s; his Wall Street quant career dabbling across hedge-fund management, investment banking, and cryptocurrency trading; his work on recommendation systems at Instagram; his book “Tuning Up” that explores experimental optimization methods, and much more.
Please enjoy my conversation with David!
The 64th episode of Datacast is my conversation with Fabiana Clemente — the Co-Founder and Chief Data Officer of YData, whose mission is to help companies and individuals to become the industry leaders by solving the true AI hidden secret — access to high-quality data.
Our wide-ranging conversation touches on her educational background in applied mathematics and data management, her time working as a developer building big data solutions, her foray into the data science universe, the genesis behind YData, synthetic data generation, differential privacy, model explainability, open-source as a strategy, and much more.
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.