Data Labeling

Datacast Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Datacast Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Hyun Kim is the co-founder and CEO of Superb AI, an ML DataOps platform that helps computer vision teams automate and manage the entire data pipeline: from ingestion and labeling to data quality assessment and delivery. He initially studied Biomedical Engineering and Electrical Engineering at Duke but shifted from genetic engineering to robotics and deep learning. He then pursued a Ph.D. in computer science at Duke with a focus on Robotics and Deep Learning but ended up taking leave to further immerse himself in the world of AI R&D at a corporate research lab. During this time, he started to experience the bottlenecks and obstacles that many companies still face to this day: data labeling and management were very manual, and the available solutions were nowhere near sufficient.

Datacast Episode 80: Creating The Sense of Sight with Alberto Rizzoli

Datacast Episode 80: Creating The Sense of Sight with Alberto Rizzoli

Alberto Rizzoli is co-Founder of V7, a platform for deep learning teams to manage training data workflows and create image recognition AI. V7 is used by AI-first companies and enterprises, including Honeywell, Merck, General Electrics, and MIT.

Alberto founded his first startup at age 19 and made the MakerFaire’s 20 under 20 list. In 2015, he began working on AI with Simon Edwardson while studying under Ray Kurzweil, leading to the creation of the first engine capable of running large deep neural networks on smartphone CPUs. Later, this project became Aipoly, a startup that helped the blind identify over 3 billion objects to date using their phones.

Alberto's work on AI granted him an award and personal audience by Italian President Sergio Mattarella and Italy’s Premio Gentile for Science and Innovation. V7's underlying technology won the CES Best of Innovation in 2017 and 2018.

What I Learned From The Future of Data-Centric AI 2021

What I Learned From The Future of Data-Centric AI 2021

Last September, I attended Snorkel AI’s The Future of Data-Centric AI. This summit connects experts on data-centric AI from academia, research, and industry to explore the shift from a model-centric practice to a data-centric approach to building AI. There were talks discussing the challenges, solutions, and ideas to make AI practical, both now and in the future.

In this blog recap, I will dissect content from the conference’s session talks, covering a wide range of topics from weak supervision and fine-grained error analysis to MLOps design principles and data-centric AI case studies.

Datacast Episode 73: Datasets for Software 2.0 with Taivo Pungas

Datacast Episode 73: Datasets for Software 2.0 with Taivo Pungas

Taivo Pungas is a tech entrepreneur working on a stealth-mode startup. Previously, he built the AI team at Veriff from scratch to 20+ people and contributed to various ML/data roles at Starship and other Estonian startups. On the side, he advises startups and writes a blog at taivo.ai.

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.

What I Learned From Attending REWORK MLOps and ML Fairness Summits

What I Learned From Attending REWORK MLOps and ML Fairness Summits

Last month, 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, I will dissect content from the talks that I found most useful from attending the summit. The post consists of 10 talks that range from automated data labeling and pipeline optimization, to model fairness and responsible AI at scale.

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.