There are a large and growing number of businesses built by and for data science and machine learning teams that rely on Python. Tony Liu is a venture investor who is following that market closely and betting on its continued success. In this episode he shares his own journey into the role of an investor and discusses what he is most excited about in the industry. He also explains what he looks at when investing in a business and gives advice on what potential founders and early employees of startups should be thinking about when starting on that journey.
- 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!
- Your host as usual is Tobias Macey and today I’m interviewing Tony Liu about his perspectives on the landscape of Python in the data ecosystem from his role as an investor
- How did you get introduced to Python?
- Can you start by sharing your background in the data ecosystem?
- What led you to your current role as a venture investor?
- What is your current area of focus in your investments?
- What do you see as the major strengths of Python in the current landscape for data and analytics?
- What are the areas where the ecosystem is still lacking?
- Where are you seeing growth in the space and what do you see as the motivating factors?
- As an investor, what are the qualities that you look for in a startup that is trying to compete in the data ecosystem?
- What is your process for learning about and identifying companies that demonstrate the potential to succeed?
- Do you focus on a particular problem domain and research a grouping of companies that are focused on that problem, or do you start from a given company to determine where to place your bets?
- How has COVID changed the competitive landscape?
- Can you share some of the companies that you have invested in?
- What was noteable about their respective businesses that provided you with the confidence that they were worth investing in?
- What are some of the most interesting, unexpected, or challenging lessons that you have learned from your experience as a venture investor?
- What are some of the companies that you are keeping a close eye on, whether as potential investments or as competitors to your existing portfolio?
- What are some of the problem spaces that you would like to see companies try to tackle?
- What advice do you have for engineers who might be considering building a new business?
- Do you have any advice for engineers who are working at a startup as to how best to compete in the current market?
Keep In Touch
- 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 email@example.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
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA