Pipeline Conversations
MLOps and LLMOps, from the trenches
Displaying items 1-10 of 14 in total of Pipeline Conversations with the tag "data-science".
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ML at the British Library with Daniel van Strien
November 10th, 2022 | Season 2 | 57 mins 28 secs
ai, archives, computer-vision, data-science, libraries, machine-learning
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.
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Questioning MLOps with Lak Lakshmanan
October 27th, 2022 | Season 2 | 53 mins 2 secs
ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops, scale
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.
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The Full Stack with Charles Frye
October 12th, 2022 | Season 2 | 57 mins 5 secs
ai, artificial-intelligence, data-science, deep-learning, education, machine-learning, mlops
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.
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Educating the next generation with Goku Mohandas
September 29th, 2022 | Season 2 | 1 hr 8 mins
ai, artificial-intelligence, data-science, education, infrastructure, machine-learning, medicine, mlops
In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML. Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more.
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ML Abstractions with Phil Howes
September 5th, 2022 | Season 2 | 54 mins 13 secs
ai, data-science, infrastructure, machine-learning, mlops, pipelines, platforms, tools
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.
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Building MLOps Tools with Outerbounds
August 22nd, 2022 | Season 2 | 59 mins 43 secs
ai, data-science, infrastructure, machine-learning, mlops, pipelines, tools
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.
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Satellite Vision with Robin Cole
July 28th, 2022 | Season 2 | 47 mins 56 secs
computer-vision, data-science, deep-learning, mlops, satellite, serverless
This week I spoke with Robin Cole, a senior data scientist at Satellite Vu, 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.
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ML Engineering with Ben Wilson
June 8th, 2022 | Season 2 | 1 hr 4 mins
ai, data-science, infrastructure, machine-learning, mlops, pipelines, tools
Today, I'm extremely excited to present this conversation I had with Ben Wilson who works over at Databricks and who has also just released a new book called 'Machine Learning Engineering in Action'.
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Trustworthy ML with Kush Varshney
April 14th, 2022 | 39 mins 8 secs
ai, artificial-intelligence, bias, data-science, ethics, fairness, machine-learning
I enthusiastically read Kush Varshney's book when it was released for free to the world several months back. Trustworthy Machine Learning is a concise and clear overview of many of the ways that machine learning can go wrong, and so I was especially keen to get Kush on to talk more about his work and research.
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Open-Source MLOps with Matt Squire
March 31st, 2022 | 47 mins 41 secs
ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops, open-source
This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs, where they help partner organisations think through how best to productionise their machine learning workflows.