12 posts in this category
This week I spoke with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning. We discussed the challenges around building a tool that is both straightforward to use while also customizable and powerful.
I spoke with Karthik Kannan, cofounder and CTO of Envision, a company that builds on top of the Google Glass and using Augmented Reality features of phones to allow visually impaired people to better sense the environment or objects around them.
This week I spoke with Iva Gumnishka, the founder of Humans in the Loop. They are an organization that provides data annotation and collection services. Their teams are primarily made up of those who have been affected by conflict and now are asylum seekers or refugees.
This week I spoke with Ben Wilson, author of 'Machine Learning Engineering in Action', a jam-backed guide to all the lessons that Ben has learned over his years working to help companies get models out into the world and run them in production.
This week I spoke with Kush Varshney, author of 'Trustworthy Machine Learning', a fantastic guide and overview of all of the different ways machine learning can go wrong and an optimistic take on how to think about addressing those issues.
This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs, where they help partner organizations think through how best to productionise their machine learning workflows.
This week I spoke with Emmanuel Ameisen, a data scientist and ML engineer currently based at Stripe. Emmanuel also wrote an excellent O'Reilly book called 'Building Machine Learning Powered Applications', a book I find myself often returning to for inspiration and that I was pleased to get the chance to reread in preparation for our discussion.
This week I spoke with Johnny Greco, a data scientist working at Radiology Partners. Johnny transitioned into his current work from a career as an academic — working in astronomy — where also worked in the open-source space to build a really interesting synthetic image data project.
Tristan and Alex discuss where machine learning and AI are headed in terms of the tooling landscape. Tristan outlined a vision of a higher abstraction level, something he's working on making a reality as CEO at Continual.
Mohan and Alex discuss neurosymbolic AI and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.
We discuss how to monitor models in production, and how it helps you in the long-run.
We discuss the role of MLOps in an organization, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.
