Episode description

Diego Oppenheimer is an entrepreneur, product developer, and investor with an extensive background in all things data. He’s currently a Partner at Factory HQ and was previously an EVP at DataRobot as well as the founder & CEO of Algorithmia. Diego recently joined Arthur’s CEO, Adam Wenchel, for a fireside chat where they talked about:

• His founder journey at Algorithmia, which paved the way as one of the first MLOps companies
• His thoughts on the ChatGPT and LLM hype, and how he sees that playing out in the next few years
• The tooling needed to support those adopting foundation/language models
• His opinions on the term “LLMOps”
• What is currently missing from MLOps architecture that would allow people to scale their model production
• The safety risks and pros/cons of government involvement with AI

Watch the video of the Diego's fireside chat on YouTube: https://bit.ly/40oDOSe

Learn more about Arthur: https://bit.ly/40JXiC5

[1:22] As someone who has been deeply involved in the AI world for a long time, what’s your take on the current foundation model/LLM explosion? How big of a deal is it? 

[2:12] Do you think the set of people who are going to be interacting with and using this technology will grow beyond the traditional ML engineers and data scientists?

[4:01] Tell us a little bit about Factory and what you all are doing. 

[6:42] Before Factory, tell us a little bit more about your founder’s journey at Algorithmia.

[8:36] What were customer conversations like back in 2014 when no one was talking about MLOps?

[9:48] A lot of investment is now going toward companies that are automating certain current human-based workflows through LLMs, and some are a very tiny layer on top of OpenAI or another off-the-shelf foundation model. How do you see this playing out over the next 6-12 months and where will the value be created? Which companies will become the pillars?

[16:11] How do you see the “LLMOps” ecosystem growing and extending beyond MLOps?

[19:44] In Arthur’s 4-year journey, we’ve seen customers go from having just a handful of models in production to, in some cases, hundreds—and soon enough, they’ll have tens of thousands. What, in your opinion, breaks when it comes to MLOps at this kind of scale?

[21:36] What still frustrates you about the space and what can the community do to address some of that?

[23:19] Do you think what we’re seeing with foundation models will stay within the realm of unstructured data, or will we see it with structured data at some point as well?

[24:43] What do you think about clearing houses of shared models like HuggingFace? At what point can we stop creating so many similar models and just reuse existing ones that are close enough? 

[26:19] What do you make of the head training wrappers for LLMs as a business model—basically creating domain experts for customers. Is this a short-lived trend?

[27:38] A letter came out recently from Elon Musk and some other AI folks, saying we should pause training on “giant models” for the next 6 months. What are your thoughts on that and how we should be thinking about AI safety with these models in general?

[30:31] If you could conjure up a startup to automate any part of your life using LLMs, what would you like to see?