Podcast
Building and Scaling Data Science Practice in Industrial Enterprises: AI Adoption, MLOps Maturity & Career Growth
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Building and Scaling Data Science Practice in Industrial Enterprises: AI Adoption, MLOps Maturity & Career Growth
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Episode Overview
How do industrial enterprises move from pilots to production-ready AI—and what team structures, MLOps practices, and career moves make that possible? In this episode Andrey Shtylenko, Director of Engineering at Honeywell and leader of its Advanced Technology Group and AI practice, walks through practical approaches for building and scaling data science teams in industrial enterprises. Drawing on Honeywell use cases—smart sensors, computer vision, and robotics—Andrey explains the data and machine learning practices.
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Chapter Summary
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- 0:00 - Introduction & Live Chat Poll Results
- 2:29 - Guest Introduction: Andrey Shtylenko, Honeywell
- 3:16 - Career Journey: Startups, Organizational Development, and Honeywell
- 8:54 - Honeywell Use Cases: Smart Sensors, Computer Vision, and Robotics
- 11:22 - Defining Organizational Data and Machine Learning Practices
- 13:46 - Challenges of AI Adoption in Traditional Industrial Companies
- 15:42 - Sensorization and Cloud Processing to Enable Advanced Models
- 19:06 - Reporting Line Impact: CTO vs CIO vs CMO vs CEO
- 24:26 - Data Practice Maturity Model: Crawl → Walk → Run
- 32:00 - POC Strategy: Single End-to-End Project to Prove Value
- 38:26 - Centralized Team: Roles, Tooling, and MLOps Standardization
- 43:39 - Transition Risks: Centralized vs Decentralized Approaches
- 46:04 - Embedded Teams: Reporting Structure, Ownership, and Trust
- 48:13 - Hybrid Hub-and-Spoke Model: Balancing Autonomy and Standards
- 50:14 - Shared Services: Experiment Tracking, Annotation, and Procurement
- 51:47 - Recommended Reading and Resources for Building Data Teams
- 52:39 - Career Pivot: From Software Engineer to Data Scientist Internally
- 55:07 - Timing and Strategies for Internal Role Transitions
- 56:44 - Research vs Production: ML Engineers and Productionizing Models
- 59:44 - Career Advice: Expanding Scope to Increase Organizational Impact