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.
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
Founder of Pachyderm
Joey is one of the founders at Pachyderm. He’s sort of the free electron of the company — he does a little bit of everything, mostly on the Customer Team side of things. Anytime there’s a job that doesn’t fall onto anyone else’s plate, he’s the catch all. When not at work he plays in two ice hockey leagues, hosts regular poker games (come join!), and is really into complicated strategy games.
About Pachyderm: Company provides industry-leading data versioning, pipelines and lineage that allow data science teams to automate the machine learning lifecycle and optimize their machine learning operations (MLOps). With investment from Benchmark, Microsoft M12, and others, Pachyderm, Inc. offers a user-deployed Pachyderm Enterprise Edition, a hosted SaaS Pachyderm Hub Edition and an open source Pachyderm Community Edition. Company helps customers get their ML and AI projects to market faster, with lower data processing and storage costs, and by supporting strict data governance requirements through data driven automation, petabyte scalability, and end-to-end reproducibility.
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