Machine Learning Operations — What is it and Why Do We Need It? // Niklas Kühl // MLOps Podcast #137

MLOps Coffee Sessions #137 with Niklas Kühl, Machine Learning Operations — What is it and Why Do We Need It? co-hosted by Abi Aryan.// Abstract The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products, and thus, many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue.// Bio NIKLAS KÜHL studied Industrial Engineering & Management at the Karlsruhe Institute of Technology (KIT) (Bachelor's and Master's). During his studies, he gained practical experience in IT by working at Porsche in both national and international roles. Niklas has been working on machine learning (ML) and artificial intelligence (AI) in different domains since 2014. In 2017, he gained his PhD (summa cum laude) in Information Systems with a focus on applied machine learning from KIT. In 2020, he joined IBM.As of today, Niklas engages in two complementary roles: He is head of the Applied AI in Services Lab at the Karlsruhe Institute of Technology (KIT), and, furthermore, he works as a Managing Consultant for Data Science at IBM. In his academic and practical projects, he is working on conceptualizing, designing, and implementing AI in Systems with a focus on robust and fair AI as well as the effective collaboration between users and intelligent agents. Currently, he and his team are actively working on different ML & AI solutions within industrial services, sales forecasting, production lines or even creativity. Niklas is internationally collaborating with multiple institutions, like the University of Texas and the MIT-IBM Watson AI Lab.// MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/// Related Links Website: niklas.xyz MLOps Newsletters: https://airtable.com/shrx9X19pGTWa7U3YMachine Learning Operations (MLOps): Overview, Definition, and Architecture paper: https://arxiv.org/abs/2205.02302--------------- ✌️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/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/Connect with Niklas on LinkedIn: https://www.linkedin.com/in/niklaskuehl/Timestamps: [00:00] Niklas' preferred coffee [00:43] Introduction to Niklas Kühl [01:16] Takeaways [02:05] Subscribe to our newsletters and give us a rating here! [02:54] Niklas background [05:09] Scraping Twitter data [06:58] EV's conclusions [08:24] NLP usage on Twitter [10:26] Consumer behavior production [12:03] Management and Machine Learning Systems Communication [14:00] Current hype around Machine Learning [15:10] Budgeting ML Productions [18:15] Machine Learning Operations (MLOps): Overview, Definition, and Architecture paper [22:56] Niklas' MLOps definiton [25:55] Navigating the idea of MLOps [30:34] Return on Investment endeavor [33:58] Full-stack data scientist [37:39] Defining success for different kinds of data science projects [41:06] Fun fact about Niklas [44:35] Other things Niklas does [47:02] The world is your oyster [50:57] Niklas' day-to-day life [52:48] One lecture Niklas can drop in [53:57] Foundational models [58:20] Wrap up