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cover of episode Hard Learned Lessons from Over a Decade in AI

Hard Learned Lessons from Over a Decade in AI

2025/6/6
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Mike Del Balso
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Mike Del Balso: 我认为目前机器决策带来的大部分价值并非来自大型语言模型，而是来自传统的预测性机器学习。我在 Uber 领导 Michelangelo 团队时，深刻体会到数据管道是机器学习项目中的主要瓶颈。因此，我们构建了特征商店，实现了数据管道的集中化和自动化，极大地促进了 Uber 人工智能应用的普及。现在，企业需要能够可靠、快速、准确地做出高质量的决策。欺诈检测、风险评估和推荐是机器学习可以带来显著业务影响的领域。公司需要根据用例的成熟度和对业务的贡献程度来提供不同级别的支持。Tecton 的目标是帮助每个机器学习工程师和数据科学家构建最佳模型，并使主题专家能够直接影响生产系统。

Deep Dive

Chapters
This chapter discusses the challenges of deploying machine learning models at scale, focusing on data pipelines and the creation of a feature store to address these challenges. It highlights the transition from descriptive and diagnostic analytics to predictive and prescriptive analytics.
  • Data pipelines were a major blocker in getting ML models to production.
  • The feature store was created to centralize and automate data pipelines.
  • The feature store became an inflection point in AI adoption, enabling self-service for data scientists.

Shownotes Transcript

Tecton⁠) Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.

// Bio

Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.

// Related Links

Website: www.tecton.ai




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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Mike on LinkedIn: /michaeldelbalso



Timestamps:



[00:00] Smarter decisions, less manual work

[03:52] Data pipelines: pain and fixes

[08:45] Why Tecton was born

[11:30] ML use cases shift

[14:14] Models for big bets

[18:39] Build or buy drama

[20:20] Fintech's data playbook

[23:52] What really needs real-time

[28:07] Speeding up ML delivery

[32:09] Valuing ML is tricky

[35:29] Simplifying ML toolkits

[37:18] AI copilots in action

[42:13] AI that fights fraud

[45:07] Teaming up across coasts

[46:43] Tecton + Generative AI?