Datacast Episode 87: Product Experimentation, ML Platforms, and Metrics Store with Nick Handel

Datacast Episode 87: Product Experimentation, ML Platforms, and Metrics Store with Nick Handel

Nick Handel is Transform's CEO and Co-Founder. Before Transform, Nick was Head of Data at Branch International in the micro-lending space.

Before Branch, Nick held a variety of roles at Airbnb, both as a Data Scientist & Product Manager. He was on the Growth team that founded the Experiences product and the Data Platform team. His work includes launching Airbnb's ML platform, Zipline, building the company's data science team, helping with the company's initial international expansion, and leading the data science team that launched Airbnb's Trips product.

Before joining Airbnb, Nick was a research economist at BlackRock. He is an avid trail runner, climber, skier, and adventurer, much of the time with his dog Huckleberry.

Datacast Episode 86: Risk Management, Open-Source Governance, and Negative Engineering with Jeremiah Lowin

Datacast Episode 86: Risk Management, Open-Source Governance, and Negative Engineering with Jeremiah Lowin

Jeremiah Lowin is the Founder & CEO of Prefect, a dataflow automation company. Before starting Prefect, Jeremiah gained extensive experience in all aspects of the modern data stack as a director of risk management, machine learning researcher, and data scientist at a number of institutional investment firms. Today, he lives with his wife and two sons in Washington, DC.

What I Learned From Convergence 2022

What I Learned From Convergence 2022

Last week, I attended Comet ML’s Convergence virtual event. The event features presentations from data science and machine learning experts, who shared their best practices and insights on developing and implementing enterprise ML strategies. There were talks discussing emerging tools, approaches, and workflows that can help you effectively manage an ML project from start to finish.

In this blog recap, I will dissect content from the event’s technical talks, covering a wide range of topics from testing models in production and data quality assessment to operational ML and minimum viable model.

What I Learned From Attending Tecton apply(meetup) 2022

What I Learned From Attending Tecton apply(meetup) 2022

Last month, I attended another apply(meetup), Tecton’s follow-up virtual event of their ML data engineering conference series. For context, I have written recaps for both of their 2021 events, including the inaugural conference and the follow-up meetup. The content below covers my learnings, ranging from model calibration and ranking systems to real-time analytics and online feature stores.

Datacast Episode 85: Ad Exchange, Stream Processing, and Data Discovery with Shinji Kim

Datacast Episode 85: Ad Exchange, Stream Processing, and Data Discovery with Shinji Kim

Shinji Kim is the Founder & CEO of Select Star, an intelligent data discovery platform that helps you understand your data. Previously, she was the CEO of Concord Systems, an NYC-based data infrastructure startup acquired by Akamai Technologies in 2016. She led the development of Akamai’s Internet-of-Things data platform for real-time messaging, log processing, and edge computing.

Prior to Concord, Shinji was the first Product Manager hired at Yieldmo, where she led the Ad Format Lab, A/B testing, and yield optimization. Before Yieldmo, she analyzed data and built enterprise applications at Deloitte Consulting, Facebook, Sun Microsystems, and Barclays Capital.

Shinji studied Software Engineering at the University of Waterloo and General Management at Stanford GSB. She also advises early-stage startups on product strategy, customer development, and company building.

What I Learned From DataOps Unleashed 2022

What I Learned From DataOps Unleashed 2022

Earlier this month, I attended the second iteration of DataOps Unleashed, a great event that examines the emergence of DataOps, CloudOps, AIOps, and other professionals coming together to share the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

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 reduce costs with their data pipelines. If interested, you should also check out my recap of DataOps Unleashed 2021 last year.

Datacast Episode 84: Business Development and Customer Success for Emerging Technologies with Taimur Rashid

Datacast Episode 84: Business Development and Customer Success for Emerging Technologies with Taimur Rashid

As Chief Business Development Officer, Taimur is responsible for developing emerging businesses at Redis and leading strategic business & corporate development. He is currently leading initiatives related to AI/ML.

Prior to Redis, Taimur led Worldwide Customer Success for Microsoft's Azure Data & AI. He jointly led the design, implementation, and landing of one of Microsoft's largest field transformations, which combined customer success, support engineering, and technical account management.

Before Microsoft, Taimur was the Managing Director for Amazon Web Services (AWS) Platform Technology and Applications - where he led business development from 2008 (near its inception) to 2018 when the business reached $25B in ARR. Taimur helped forge key partnerships and customers, including Airbnb, CapitalOne, Dropbox, Liberty Mutual, NASA JPL, Nasdaq, Netflix, Nintendo, Intuit, SAP, and Samsung.

Taimur grew up in three countries and lived in five states. Bellevue, WA is home for him, where he lives with his wife and three boys. Taimur enjoys cross-training, hiking, and biking. He is an avid reader of technology, business, and history. He enjoys art, music, coffee, and cooking on the weekends for his family.

Datacast Episode 83: Startup Scrappiness, Venture Matchmaking, and Thinking In Bets with Leigh-Marie Braswell

Datacast Episode 83: Startup Scrappiness, Venture Matchmaking, and Thinking In Bets with Leigh-Marie Braswell

Leigh-Marie Braswell is an investor at Founders Fund.

Before joining Founders Fund, she was an early engineer & the first product manager at Scale AI, where she originally built & later led product development for the LiDAR/3D annotation products, used by many autonomous vehicles, robots, and AR/VR companies as a core step in their machine learning lifecycles. She also has done software development at Blend, machine learning at Google, and quantitative trading at Jane Street.

She is originally from Alabama, graduated from MIT, and loves to play poker, run long distances, and scuba dive.

Datacast Episode 82: Enabling AI-Powered AR Navigation For Driving with Chen-Ping Yu

Datacast Episode 82: Enabling AI-Powered AR Navigation For Driving with Chen-Ping Yu

Dr. Chen-Ping Yu is the co-founder and CEO of Phiar, a company that is bringing human-like perception to every vehicle with its advanced lightweight spatial AI. Prior to founding Phiar, he was a postdoctoral fellow at Harvard University, researching neuro-inspired deep learning.

Chen-Ping received his Ph.D. from Stony Brook University in Computer Vision and Machine Learning and his MS from Penn State University. He was the recipient of numerous honors and awards, including from the NSF, and has published more than 15 scientific publications at top computer vision, AI, and cognitive science conferences and journals.

Datacast Episode 81: Research, Engineering, and Product in Machine Learning with Aarti Bagul

Datacast Episode 81: Research, Engineering, and Product in Machine Learning with Aarti Bagul

Aarti Bagul is a machine learning engineer at Snorkel AI. Before Snorkel, she worked closely with Andrew Ng in various capacities: (1) at AI Fund helping build ML companies from scratch internally and investing in ML companies, (2) as an ML engineer at his startup Landing AI, (3) as head TA for his deep learning class CS230, and (4) as an assistant in his research lab at Stanford.

Aarti graduated with a master’s in Computer Science from Stanford, where she participated in the Threshold Venture and Greylock X fellowships. Before Stanford, she got her bachelor’s in Computer Science and Computer Engineering from NYU with the highest honors. During her time at NYU, she worked in David Sontag’s lab on machine learning applications to clinical medicine and at Microsoft Research as a research intern for John Langford (where she contributed to Vowpal Wabbit, an open-source project).