Organisational Challenges of MLOps // Adam Sroka // MLOps Coffee Sessions #38

Coffee Sessions #38 with Adam Sroka of Origami Energy, Organisational Challenges of MLOps.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter// AbstractDeploying data science solutions into production is challenging for both small and large organizations. From platform and tooling wars to architecture and design pattern trade-offs, it can get overwhelming for inexperienced teams. Furthermore, many organizations will only go through the painful discovery process once. Adam will share some of his experiences from consulting and leading data teams to successfully deploying machine learning solutions, highlighting some of the more difficult challenges to overcome. You might not be surprised to hear it’s not all down to the tech.// BioDr. Adam Sroka, Head of Machine Learning Engineering at Origami Energy, is an experienced data and AI leader helping organizations unlock value from data by delivering enterprise-scale solutions and building high-performing data and analytics teams from the ground up. Adam shares his thoughts and ideas through public speaking, tech community events, on his blog, and in his podcast.--------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign 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 Adam on LinkedIn: https://www.linkedin.com/in/aesroka/Timestamps: [00:00] Introduction to Adam Sroka [01:53] Adam's background in tech [08:06] 2 blog posts of Adam: Why So Many Data Scientists Quit Good Jobs at Great Companies and Why You Shouldn’t Hire More Data Scientists [08:31] High turn rate Adam has in the data science role[13:50] Avoiding hiring talents with deficits and coaching people[16:05] "I can't teach you to care about the standard of your core of what you're doing. It's quite hard to teach people charisma. Everything else, you pick up." [16:45] Resume-driven development, the idea of not playing the game, and politics in the workplace. [17:57] "You have to realize, other people don't have the same experience in the context that you do."  [19:59] Exit, Voice, Loyalty, and Neglect Model[22:35] You probably don't need a data scientist [23:40] "Data scientists can do everything slower and more expensively than everyone else, but they can do everything, and that's the important bit." [27:54] "My success is just driven by who I am as much as what I can do." Vishnu [28:24] Being Candor[30:37] Disconnect between the senior stakeholders and data scientists [32:30] "Before you come out to bring someone in an expensive talent search, engage with the consultancy. Do a four-week PRC, get them to tell you like." [34:18] Educational experiences as a consultant[37:35] Adam's journey into MLOps, productionize ML models when you are a data scientist, and tips[43:16] "Beginners can help beginners. Your perspective is really important. The value is not in the content. The value is in your perspective of the content." [45:21] Educating clients on uncertainty [48:34] Decision-making process [52:32] Organizational problems [53:43] "All models are wrong, but some are useful." George Box