Datacast Episode 63: Real-World Transfer Learning with Azin Asgarian

Datacast Episode 63: Real-World Transfer Learning with Azin Asgarian

Azin Asgarian is currently 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. Azin holds a Master of Science in Computer Science from the University of Toronto and a Bachelor of Computer Science from the University of Tehran. Before joining Georgian, Azin was a research assistant at the University of Toronto and part of the Computer Vision Group, where she worked on the intersection of Machine Learning, Transfer Learning, and Computer Vision. In addition, due to her interest in HealthCare, she has worked on various healthcare projects as a research assistant at University Health Network.

What I Learned From Attending Tecton's apply() Conference

What I Learned From Attending Tecton's apply() Conference

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 sessions 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 design. Let’s dive in!

Datacast Episode 62: Leading Organizations Through Analytics Transformations with Gordon Wong

Datacast Episode 62: Leading Organizations Through Analytics Transformations with Gordon Wong

As a data modeling fanatic, data warehouse architect, multi-hypergrowth startup veteran, and team builder, Gordon has built his career on helping people get their business questions. Over time, he's switched his focus from pure technology to complete solutions where people, process, and technology all play a role. At Fitbit, he established the data warehousing team and, as an early customer of Snowflake, used it to fuel petabyte-scale analytics. Later on, at both ezCater and Hubspot, he rebuilt the data warehousing teams to focus on enabling analysts, not loading more data. A constant focus on the customer and their problems has led him to realize that empathy is the most important trait a leader can have.

Datacast Episode 61: Meta Reinforcement Learning with Louis Kirsch

Datacast Episode 61: Meta Reinforcement Learning with Louis Kirsch

Louis Kirsch is a third-year Ph.D. student at the Swiss AI Lab IDSIA, advised by Prof. Jürgen Schmidhuber. He received his B.Sc. in IT-Systems-Engineering from Hasso-Plattner-Institute (1st rank) and his Master of Research in Computational Statistics and Machine Learning from University College London (1st rank). His research focuses on meta-learning algorithms for reinforcement learning, specifically meta-learning algorithms that are general-purpose, introduced by his work on MetaGenRL. Louis has organized the BeTR-RL workshop at ICLR 2020, was an invited speaker at Meta Learn NeurIPS 2020, and won several GPU compute awards for the Swiss national supercomputer Piz Daint.

What I Learned From Attending Scale Transform 2021

What I Learned From Attending Scale Transform 2021

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. To be honest, the conference's quality was so amazing, and it’s hard to choose the talks to recap.

Datacast Episode 60: Algorithms and Data Structures For Massive Datasets with Dzejla Medjedovic

Datacast Episode 60: Algorithms and Data Structures For Massive Datasets with Dzejla Medjedovic

Dzejla Medjedovic earned her Ph.D. in the Applied Algorithms Lab of the Computer Science department at Stony Brook University in 2014. Dzejla has worked on a number of projects in algorithms for massive data, taught algorithms at various levels, and spent some time at Microsoft. Dzejla is passionate about teaching, promoting computer science education and technology transfer. She now works as an assistant professor of Computer Science at the International University of Sarajevo.

What I Learned From Attending DataOps Unleashed 2021

What I Learned From Attending DataOps Unleashed 2021

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.

This long-form blog recap dissects 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.

Datacast Episode 59: Bridging The Gap Between Data and Models with Willem Pienaar

Datacast Episode 59: Bridging The Gap Between Data and Models with Willem Pienaar

Willem Pienaar is the Engineering Lead at Tecton and the creator of Feast, a feature store for machine learning. Feast is an open-source project that Willem developed while leading the machine learning platform team at Gojek, the Indonesian ride-hailing startup. In a previous life, Willem founded and sold a networking startup and worked on industrial data systems.

Datacast Episode 58: Deep Learning Meets Distributed Systems with Jim Dowling

Datacast Episode 58: Deep Learning Meets Distributed Systems with Jim Dowling

Jim Dowling is the CEO of Logical Clocks AB, an Associate Professor at KTH Royal Institute of Technology, and a Senior Researcher at SICS RISE in Stockholm. His research concentrates on building systems support for machine learning at scale. He is the lead architect of Hops Hadoop, the world's fastest and most scalable Hadoop distribution and only Hadoop platform with support for GPUs as a resource. He is also a regular speaker at Big Data and AI industry conferences.

Datacast Episode 57: Building Data Science Projects with Pier Paolo-Ippolito

Datacast Episode 57: Building Data Science Projects with Pier Paolo-Ippolito

Pier Paolo Ippolito is a SAS Data Scientist and MSc in Artificial Intelligence graduate from the University of Southampton. He has a strong interest in AI advancements and machine learning applications (such as finance and medicine). Outside of his work activities, he is a writer for Towards Data Science and Freelancer.