The Rapid Evolution of the Canonical Stack for Machine Learning


Just a few years ago every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon, rolled their own AI/ML tech stack from scratch. Fast forward to today and we have a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. But how do we make sense of it all? In order for AI apps to become as ubiquitous as the apps on your phone, you need a canonical stack for machine learning that makes it easier for non-tech companies to level up fast.

Session Outline:
Join us in this webinar as we cover:
● What are the components for true MLOps
● How do teams begin their journey into AI and Machine Learning
● Why teams should take a data first approach to ML


Lee is the General Secretary for the AI Infrastructure Alliance. Based out of the UK, he is responsible for crafting and nurturing relationships with companies to build a canonical stack for AI and ML. When not shuttling his 3 children around, he can most often be found cycling, running and swimming around England's South Coast.

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