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

Show Notes(1:45) Willem discussed his undergraduate degree in Mechatronic Engineering at Stellenbosch University in the early 2010s.(2:34) Willem recalled his entrepreneurial journey founding and selling a networking startup that provides internet access to private residents on campus.(5:37) Willem worked for two years as a Software Engineer focusing on data systems at Systems Anywhere in Capetown after college.(6:49) Willem talked about his move to Bangkok working as a Senior Software Engineer at INDEFF, a company in industrial control systems.(9:52) Willem went over his decision to join Gojek, a leading Indonesian on-demand multi-service platform and digital payment technology group.(12:16) Willem mentioned the engineering challenges associated with building complex data systems for super-apps.(14:50) Willem dissected Gojek’s ML platform, including these four solutions for various stages of the ML life cycle: Clockwork, Merlin, Feast, and Turing.(19:24) Willem recapped the lessons from designing the ML platform to meet Gojek’s scaling requirements — as delivered at Cloud Next 2018.(23:09) Willem briefly went through the key design components to incorporate Kubeflow pipelines into Gojek’s existing ML platform — as delivered at KubeCon 2019.(26:21) Willem explained the inception of Feast, an open-source feature store that bridges the gap between data and models.(32:20) Willem talked about prioritizing the product roadmap and engaging the community for an open-source project.(35:07) Willem recapped the key lessons learned and envisioned Feast's future to be a lightweight modular feature store.(37:29) Willem explained the differences between commercial and open-source feature stores (given Tecton’s recent backing of Feast).(41:36) Willem reflected on his experience living and working in Southeast Asia.(44:33) Closing segment.Willem’s Contact InfoTwitterLinkedInGitHubMentioned Content

Feast

Feast Project website: feast.devFeast Slack community: #FeastFeast Documentation: docs.feast.devFeast GitHub repository: feast-dev/feastFeast on StackOverflow: stackoverflow.com/questions/tagged/feastFeast Wiki: wiki.lfaidata.foundation/display/FEAST/Feast+HomeFeast Twitter: @feast_dev

Article

An Introduction to Gojek’s Machine Learning Platform (2019)Introducing Feast: An Open-Source Feature Store For Machine Learning (2019)A State of Feast (2020)Why Tecton is Backing The Feast Open-Source Feature Store (2020)

Talks

Lessons Learned Scaling Machine Learning at GoJek on Google Cloud (Cloud Next 2018)Accelerating Machine Learning App Development with Kubeflow Pipelines (Cloud Next 2019)Moving People and Products with Machine Learning on Kubeflow (KubeCon 2019)

People

David Aronchick (Open-Source ML Strategy at Azure, Ex-PM for Kubernetes at Google, Co-Founder of Kubeflow, Advisor to Tecton)Jeremy Lewi (Principal Engineer at Primer.ai, Co-Founder of Kubeflow)Felipe Hoffa (Developer Advocate for BigQuery, Data Cloud Advocate for Snowflake)

Book

Cal Newport’s “Deep Work”

Willem will be a speaker at Tecton’s apply() virtual conference (April 21-22, 2021) for data and ML teams to discuss the practical data engineering challenges faced when building ML for the real world. Participants will share best practice development patterns, tools of choice, and emerging architectures they use to successfully build and manage production ML applications. Everything is on the table from managing labeling pipelines, to transforming features in real-time, and serving at scale. Register for free now: https://www.applyconf.com/!

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

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