Challenges of Deploying Traditional ML Models

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MLOps Coffee Sessions Special episode with Tecton, Get your ML Application Into Production, sponsored by Tecton. // Abstract Getting an ML application into production is more difficult than most teams expect—but with the right preparation, it can be done efficiently! Join us for this exclusive roundtable, where 4 machine learning experts from Tecton will discuss some of the most common challenges and best practices to avoid them. With over 35 years of combined experience in MLOps at companies like AWS, Google, Lyft, and Uber, and 15 years of experience at Tecton spent helping customers like FanDuel, Plaid, and HelloFresh getting ML models into production, the presenters will share how factors like organizational structure, use cases, tech stack, and more, can create different types of bottlenecks. They’ll also share best practices and lessons learned throughout their careers on how to overcome these challenges. // Bio Kevin Stumpf Kevin co-founded Tecton where he leads a world-class engineering team that is building a next-generation feature store for operational Machine Learning. Kevin and his co-founders built deep expertise in operational ML platforms while at Uber, where they created the Michelangelo platform that enabled Uber to scale from 0 to 1000's of ML-driven applications in just a few years. Prior to Uber, Kevin founded Dispatcher, with the vision to build the Uber for long-haul trucking. Kevin holds an MBA from Stanford University and a Bachelor's Degree in Computer and Management Sciences from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete. Derek Salama Derek is currently a Senior Product Manager at Tecton, where he is responsible for security, collaboration experience, and Feature Platform infrastructure. Prior to Tecton, Derek worked at Google and Lyft across both ML infrastructure and ML applications. Eddie Esquivel Eddie Esquivel is a Solutions Architect at Tecton, where he helps customers implement feature stores as part of their stack for operational ML. Prior to Tecton, Eddie was a Solutions Architect at AWS. He holds a Bachelor’s Degree in Computer Science & Engineering from the University of California, Los Angeles. Isaac Cameron Isaac Cameron is a Consulting Architect at Tecton. Prior to Tecton, he was a Principal Solutions Architect at Slalom Build, focusing on data and machine learning, where he built his own feature platform for a large U.S. airline and has enabled many organizations to build intelligent products leveraging operational ML. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Kevin on LinkedIn: https://www.linkedin.com/in/kevinstumpf/ Connect with Derek on LinkedIn: https://www.linkedin.com/in/dereksalama/ Connect with Eddie on LinkedIn: https://www.linkedin.com/in/eddie-esquivel-2016/ Connect with Isaac on LinkedIn: https://www.linkedin.com/in/isaaccameron/ Timestamps: [00:00] Introduction to Kevin Stumpf, Derek Salama, Eddie Esquivel, and Isaac Cameron [02:48] Challenges of traditional classical ML into production [10:21] Infrastructure cost [16:50] Bridging Business and Tech [19:23] ML Infrastructure Essentials [29:38] Integrated Batch and Stream [35:12] Scaling AI from Zero [36:23] Stacks red flags [45:53] Tecton: Features Quality Monitoring [49:06] Building Recommender System Tools [53:19] Quantify business value in ML [54:40] Wrap up