MLOps: A leader's perspective // Stephen Galsworthy // MLOps Coffee Sessions #39

Coffee Sessions #39 with Stephen Galsworthy of Quby, MLOps: A leader's perspective.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠// Abstract  Demetrios and Vishnu sit down with Stephen Goldsworthy, former Chief Data and Product Officer at Quby, to explore the evolving intersection of machine learning, organizational culture, and leadership. The discussion traces Stephen’s journey from data scientist to executive board member, highlighting how the toughest challenges in scaling ML aren’t technical—they’re organizational. He shares lessons from embedding data science into core product teams, aligning executives around AI literacy, and navigating the cultural transformation of merging a fast-moving tech company with a traditional utility. This episode unpacks how true MLOps maturity depends less on tools and more on communication, structure, and shared understanding across every level of the business.//Bio Dr. Stephen Galsworthy is a data leader skilled at building high-performing teams and passionate about developing data-powered products with lasting impact on users, businesses, and society.  Most recently, he was the Chief Data and Product Officer at Quby, an Amsterdam-based tech company offering data-driven energy services. He oversaw its transformation from a hardware-based business to a digital organization with data and AI at its core. He put in place a central cloud-based data infrastructure and unified analytics platform to collect and take advantage of petabytes of IoT data. His team deployed real-time monitoring and energy insight services for 500k homes across Europe.   Stephen has a Master’s degree and Ph.D. in Mathematics from Oxford University and has been leading data science teams since 2011.//Takeaways MLOps as a process, people, and technological problem.  Experiences from a team working at the forefront of data and AI.--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/Connect with Stephen on LinkedIn: https://www.linkedin.com/in/galsworthy/Timestamps:[00:00] Introduction to Stephen Galsworthy[01:28] Stephen’s Background in Tech[03:53] ML at Scale and Production[05:28] Production Is Not Final[06:15] From Zero to One[07:35] Non-Technical Challenges in ML[09:13] Technology No Longer Stumbling Block[09:37] Maximizing Value from Teams[10:20] Focusing on Business Impact[10:30] Organizational View of MLOps[18:00] Importance of Labeled Data[20:43] Aligning with Stakeholders Effectively[21:05] Different Approaches for Stakeholders[25:34] Filtering Noise for Strategy[26:54] Stephen’s Role and Mandate[28:30] Beyond Traditional Data Leadership[31:37] MLOps Organizational Challenge Project[32:15] Lessons from First Projects[35:37] Speed Through Team Discipline[37:00] Processes Enable Smooth Operations[38:07] Communicating Effectively with Stakeholders[41:34] Transparency with Leadership Peers[43:25] Ensuring End-User Benefits[43:44] Sharing Success Inside and Out[46:06] Prioritizing High-Impact Problems[47:05] Simple Solutions Over Machine Learning