Nathan Frank
Director, MLOps Platform @ Grainger
Chicago, Illinois, United States
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Nathan Frank is currently the Director of Machine Learning Platform and Operations at Grainger where he is building a team to support the Technology Group's expanding machine learning efforts. Prior to joining Grainger, Nathan led machine learning engineering efforts at Strong Analytics, a boutique data science and machine learning consulting firm, as well as machine learning platform and development teams at Stats Perform, a leader in sports data and technology.
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The Open Source Edge: Tools, Tradeoffs, and Future Directions
Open source software is central to how our platform team builds, experiments, and scales. In this session, we’ll share how we strategically adopt tools like Kubernetes, ArgoCD, Milvus, Triton, and Onyx to move fast, maintain flexibility, and own our future architecture. Through real-world case studies, we’ll highlight the benefits—and challenges—of using and potentially contributing to open source as a Fortune 500 company.
We’ll also explore the process of open sourcing Grainger software and reflect on how companies like Google and Meta have benefited from an open source strategy.
Learning Outcomes:
- Learn how to evaluate open source tools for enterprise use, balancing flexibility and agility against risk and maintenance.
- See how we piloted and scaled tools like Kubernetes, Milvus, Triton, and ArgoCD.
- Get ideas for building internal momentum and contributing upstream.
Testing Patterns for Data and Machine Learning
Are you a data / machine learning scientist or data engineer who’s wondered how you can apply sensible defaults like TDD to your work? Are you someone who’s ever said “I’ll write tests when this goes to production…it’ll only slow me down” (be honest)? Are you a Python data stack (Pandas, NumPy, etc) or PySpark user curious about testing but don’t know where to start?
If you answered yes to any of those questions then this workshop is for you! Join us as we walkthrough testing patterns for data and machine learning developers (in Python), covering topics such as project setup, testing frameworks / methods, and best practices.
Ctrl+F for the Enterprise: Because “Where Was That Again?” Shouldn’t Be a Daily Question
In today’s fast-paced, information-rich enterprise environments, the ability to find the right document, decision, or data point when needed isn’t just a convenience, it’s a competitive advantage. This session explores the case for a centralized enterprise search platform and introduces Onyx, an open-source, AI-powered search and assistant tool currently being piloted at Grainger.
We’ll walk through the motivation behind the initiative, how Onyx is deployed internally, and what we’ve learned from our pilot including comparisons with similar tools like M365 CoPilot and ChatGPT. You’ll see how Onyx connects to internal tools, documents, and conversations to deliver fast, context-aware answers, and how its open-source foundation enables deep customization and extensibility.
Whether you’re a team lead, product manager, or technical stakeholder, this session will help you rethink how your teams access and act on information.
Learning Outcomes:
- Why centralized enterprise search is critical for productivity, especially in technical domains.
- How Onyx works: architecture, integrations, and how it fits into Grainger’s ecosystem.
- What we learned from the Onyx pilot, including performance, adoption, and comparison with similar tools.
- The benefits of open-source: flexibility, transparency, and the ability to tailor Onyx to your team’s needs.
- What’s next: roadmap for broader adoption and how you can get involved.
GTG Tech Conference 2024 Sessionize Event
GTG Tech Conference Sessionize Event
Nathan Frank
Director, MLOps Platform @ Grainger
Chicago, Illinois, United States
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