Bringing Artificial Intelligence Projects From Idea To Production

Summary Artificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen. Announcements Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show. Equalum’s end to end data ingestion platform is relied upon by enterprises across industries to seamlessly stream data to operational, real-time analytics and machine learning environments. Equalum combines streaming Change Data Capture, replication, complex transformations, batch processing and full data management using a no-code UI. Equalum also leverages open source data frameworks by orchestrating Apache Spark, Kafka and others under the hood. Tool consolidation and linear scalability without the legacy platform price tag. Go to pythonpodcast.com/equalum today to start a free 2 week test run of their platform, and don’t forget to tell them that we sent you. Your host as usual is Tobias Macey and today I’m interviewing Henrik Landgren about his experiences building AI platforms to transform business capabilities. Interview Introductions How did you get introduced to Python? Can you start by sharing your thoughts on when, where, and how AI/ML are useful tools for a business? What has been your experience in building AI platforms? For organizations who are considering investing in AI capabilities, what are some alternative strategies that they might consider first? What are the cases where AI is likely to be a wasted effort, or will fail to create a return on investment? In order to be succesful in bringing AI products to production, what are the foundational capabilities that are necessary? What have you found to be a useful composition of roles and skills for building AI products? There are various statistics that all point to a remarkably low success rate for bringing AI into production. What are some of the pitfalls that organizations and engineers should be aware of when undertaking such a project? What is your strategy for identifying opportunities for a successful AI product? Once you have determined the possible utility for such a project, how do you approach the work of making it a reality? What are the common factors in what you built at Spotify and EQT ventures? Where do the two efforts diverge? Your work on Motherbrain is interesting because of the fact that it is dealing in what seems to be intangible or unpredictable forces. What kinds of input are you relying on to generate useful predictions? What are some of the most interesting, innovative, or unexpected uses of AI that you have seen? What are some of the biggest failures of AI that you are aware of? In your work at Spotify and EQT ventures, what are the most interesting, unexpected, or challenging lessons that you have learned? What advice or recommendations do you have for anyone who wants to learn more about the potential for AI and the work involved in bringing it to production? Keep In Touch LinkedIn @hlandgren on Twitter Picks Tobias Whale bat Henrik Observable Dataform Data Engineering Podcast Episode Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at pythonpodcast.com/chat Links EQT Ventures Stockholm Sweden Motherbrain Accenture Spotify Basic C# ASP.NET Javascript Hadoop McKinsey Deep Learning Data Engineer Data Scientist Machine Learning Engineer Discover Weekly Spotify Playlist GPT-3 Deep Fakes DBT Data Engineering Podcast Episode The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA