Datacast Episode 72: Folding Data with Gleb Mezhanskiy

Datacast Episode 72: Folding Data with Gleb Mezhanskiy

Gleb Mezhanskiy is the CEO & Co-founder of Datafold -  a data observability platform that helps companies unlock growth through more effective and reliable use of their analytical data. As a founding member of Data teams at Autodesk and Lyft and the Head of Product at Phantom Auto, Gleb has built some of the world's largest and most sophisticated data platforms and has developed tooling to improve productivity and data quality in organizations with hundreds of data users.

Datacast Episode 71: Trusted AI with Saishruthi Swaminathan

Datacast Episode 71: Trusted AI with Saishruthi Swaminathan

Saishruthi Swaminathan is an Advisory Data Scientist at IBM's AI Strategy and Innovation division. Previously, she was a technical lead and data scientist in the IBM Center for Open-Source Data and AI Technologies team, whose main focus is to democratize data and AI through open source technologies. She has a Master’s in Electrical Engineering that specializes in Data Science and a Bachelor's degree in Electronics and Instrumentation. Her passion is to dive deep into the ocean of data, extract insights, and use AI for social good.

Previously, she worked as a Software Developer on a mission to spread the knowledge and experience she acquired in her learning process. She also leads an initiative to bring education to rural children and organizes meetups that focus on women's empowerment.

Datacast Episode 70: Machine Learning Testing with Mohamed Elgendy

Datacast Episode 70: Machine Learning Testing with Mohamed Elgendy

Mohamed Elgendy is a seasoned AI expert, who has previously built and managed AI organizations at Amazon, Rakuten, Twilio, and Synapse. In particular, he founded and managed Amazon's computer vision think tank. He is the author of the "Deep Learning for Vision Systems" book published by Manning in November 2020. Mohamed regularly speaks at many AI conferences like Amazon's DevCon, O'Reilly's AI, and Google's I/O.

What I Learned From Attending MLOps World 2021

What I Learned From Attending MLOps World 2021

Two months ago, I attended the second edition of MLOps: Production and Engineering World, which is a multi-day virtual conference organized by the Toronto Machine Learning Society that explores the best practices, methodologies, and principles of effective MLOps. In this post, I would like to share content from the talks that I found most useful during this conference, broken down into Operational and Technical talks.

Datacast Episode 69: DataPrepOps, Active Learning, and Team Management with Jennifer Prendki

Datacast Episode 69: DataPrepOps, Active Learning, and Team Management with Jennifer Prendki

Dr. Jennifer Prendki is the founder and CEO of Alectio, the first startup fully focused on DataPrepOps. She and her team are on a fundamental mission to help ML teams build models with less data. Before Alectio, Jennifer was the Vice President of ML at Figure Eight. She also built an entire ML function from scratch at Atlassian and led multiple Data Science projects on the Search team at Walmart Labs. She is recognized as one of the top industry experts on Active Learning and ML lifecycle management. She is an accomplished speaker who enjoys addressing both technical and non-technical audiences.

Fugue - Reducing Spark Developer Friction

Fugue - Reducing Spark Developer Friction

This is a guest article written by Han Wang and Kevin Kho, in collaboration with James Le. Han is a Staff Machine Learning Engineer at Lyft, where he serves as a Tech Lead of the ML Platform. He is also the founder of the Fugue Project. Kevin is an Open Source Engineer at Prefect, a workflow orchestration framework, and a contributor to Fugue. Opinions presented are their own and not the views of their employers.

Datacast Episode 68: Threat Intelligence, Venture Stamina, and Data Investing with Sarah Catanzaro

Datacast Episode 68: Threat Intelligence, Venture Stamina, and Data Investing with Sarah Catanzaro

Sarah Catanzaro is a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems. Her investments at Amplify include startups like RunwayML, Maze Design, OctoML, and Metaphor Data, among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector, including roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies.

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Aparna Dhinakaran is the Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michelangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.