“I'm gonna make the case that the biggest barrier to adoption is an infrastructure and tooling problem, not necessarily an algorithmic problem.”
quote is from the automated transcript, cleaned for reading:
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More from Dillon Erb
Insight
Erb: Existing CI/CD workflows do not fit machine learning pipelines
“Existing CI CD workflows, talking about how those don't really fit anymore. And then making the case that you know, these new pipelines are fundamentally different and require new tools, new workflows, and generally like new paradigms.”
Dillon ErbFeb 25, 2019▶ 1:50CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
AssertionSupported
Erb: Billions have been invested in pre-product AI hardware unicorns
“A few billion dollars have been invested in new hardware startups. There are a few unicorns already for companies that don't have products fully out yet.”
Dillon ErbFeb 25, 2019▶ 4:14CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
Insight
Erb: AI researchers and infrastructure teams view algorithmic primitives completely differently
“So if you're an AI or machine learning or deep learning developer or researcher, you think of your primitives as, you know, hidden layers and LSTMs and things like that. From an infrastructure perspective, it's actually much higher up”
Dillon ErbFeb 25, 2019▶ 2:22CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
AssertionNot checkable as stated
Tech giants like Google and Uber rely on home-rolled ML infrastructure
“Most of the tooling is kind of home rolled. Best practices haven't emerged yet. We're seeing a lot of, especially large companies, roll their entire own stacks. So Facebook has FB Learner, Google has TFX. Uber has Michelangelo. Airbnb has Big Head.”
Dillon ErbFeb 25, 2019▶ 3:10CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
Insight
CI/CD principles address the black-box interpretability problem in deep learning
“You care about it because it adds reliability, reproducibility, determinism. You know, your systems are no longer inscrutable, which is a very big criticism, especially of deep learning architectures, which is that they're black box or they're, you know, they …”
Dillon ErbFeb 25, 2019▶ 6:43CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
PredictionNot checkable as stated
Erb predicts ten new machine learning hardware accelerators will launch in 2019
“My guess is by the end of the year, there will be 10 more new devices that are coming out.”
Dillon ErbFeb 25, 2019▶ 8:23CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
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