ZenML Recap with Adam and Hamza

This week I spoke with Daniel van Strien, a digital curator working at the British Library. Daniel has worked on a number of projects at the intersection of archives, libraries and machine learning and I was really happy to have the chance to get to unpack some of the ways he's finding to apply these techniques and tools. In particular, I found it interesting how important the annotation process is as part of many overall workflows, as well as how simple out-of-the-box techniques like image classification using a fine-tuned model could satisfy many low-hanging fruit-type use cases. Special Guest: Daniel van Strien.

This week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously. Lak brings a ton of experience to the table and it was interesting to hear his suggestions around when it is and isn't appropriate to bring the full set of MLOps tools to the table, for example. We also discussed the fundamentals of doing ML-backed projects as well as the teams needed to make those projects succeed. Special Guest: Lak Lakshmanan.

This week I spoke with Charles Frye. Not only has Charles volunteered to be a judge on our Month of MLOps competition happening right now, he's part of the core team working on the Full Stack Deep Learning course. Naturally, we get into education for practitioners as well as the things that Charles has seen in his own prior background working on production use cases. We also discuss the ways that tooling to support education as well as productive machine learning can and is being improved. Special Guest: Charles Frye.

In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML (https://madewithml.com/). Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more. In this conversation we continued to unpack the theme of education in ML, the challenges when it comes to working across the full stack of ML applications, and what he's seen work in his experience working on MadeWithML (https://madewithml.com/). We also discuss some of the patterns he's seen in the production stacks he's seen in his experience consulting with various ML teams as well as where he sees room for improvement in the abstractions that we all rely on to do our work. Goku has generously agreed to be an external judge for our Month of MLOps competition that starts on October 10. If you haven't signed up yet, or want to learn more, please visit zenml.io/competition (https://zenml.io/competition). Special Guest: Goku Mohandas.

So excited to be able to announce our 🔥 AMAZING 🔥 external judges for the ZenML Month of MLOps competition! We have a stellar panel of ✨ ML and MLOps heroes ✨ to help select the best pipelines from all of your submissions! 💥 Charles Frye, core instructor at the amazing Full Stack Deep Learning course 💥 Anthony Goldbloom, co-founder and former CEO of Kaggle 💥 Chip Huyen, author of 'Designing Machine Learning Systems' and co-founder of Claypot AI 💥 Goku Mohandas, founder of MadeWithML, another essential course in production ML We're honoured to have them on board for the ride, and we can't wait to see all the amazing ML use cases and problems our competitors solve along the way! To learn more about the competition and to sign up, visit https://zenml.io/competition

This week I spoke with Eric Landau, co-founder of Encord, a platform for data-centric computer vision. This podcast contains a lot of geekery about annotation, and even though Encord aren't an annotation tool per se, Eric and his team have tackled a bunch of quite complicated problems relating to that domain. We also discuss the much-used term 'data-centric AI' and consider where it's useful and where perhaps there's a little bit of hype. We also get into some of the technical tradeoffs and decisions that come when building a platform. I'm really excited to get to present this episode to you today as I really enjoyed the discussion. Special Guest: Eric Landau.

This week we dive into the abstractions that we're all trying to layer on top of the core ML processes and workflows. I spoke with Phil Howes, co-founder and chief scientist at BaseTen. BaseTen is a platform that allows data scientists to go from an initial model to an MVP web app quickly. We got into some of the big challenges he had working to build out the platform, as well as the core issue of iteration speed that motivates why they're building BaseTen. Phil has experienced quite a few of the industry's end-to-end patterns in the years that he's been working on machine learning and it was great to have that context inform the conversation, too. Special Guest: Phil Howes.

This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps. Above all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it's great that there are lots of smart people thinking about this really hard problem, and even though it is by no means 'solved' conversations like this make me feel cautiously optimistic about the space. Special Guests: Hugo Bowne-Anderson and Savin Goyal.

This week I spoke with Mateo Rojas-Carulla, the CTO and a co-founder of Lakera (https://www.lakera.ai/) and Matthias Kraft, also a co-founder and the CPO there. Lakera (https://www.lakera.ai/) is an AI safety company that does a lot of work in the computer vision domain, building a platform and tools for users to gain more confidence in the output and functionality of their models. We discuss how they think about the testing of machine learning models, and about how having this safety element upfront has implications for how you go about the testing and ensuring robustness. We specifically dive into how to go about testing computer vision models and the various pitfalls that are to be found in that domain. Special Guests: Mateo Rojas-Carulla and Matthias Kraft.

This week I spoke with Robin Cole, a senior data scientist at Satellite Vu (https://www.satellitevu.com), a company that's about to launch a thermal imaging satellite into space in order to provide new ways of seeing the earth from above. Robin generously took the time to discuss his day to day work involving satellite data, the stack they work with at Satellite Vu as well as some of the difficulties that come up in the domain. We also discuss the extremely popular satellite-image-deep-learning GitHub repo (https://github.com/robmarkcole/satellite-image-deep-learning) that presents resources for those working with or seeking to learn about this kind of data. Special Guest: Robin Cole.