Pipeline Conversations

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.

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.

Practical Production ML with Emmanuel Ameisen

March 17th, 2022  |  58 mins

ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops

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.

From Academia to Industry with Johnny Greco

March 3rd, 2022  |  56 mins 34 secs

academia, applied-ml, astronomy, industry, machine-learning, mlops, nlp

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.

We get into that project in our conversation but we also discuss his experience of crossing over into industry, the skills that have served him in his new job, and his experience of working in a world where the stakes around models in production are much higher.

The Modern Data Stack with Tristan Zajonc

February 10th, 2022  |  59 mins 4 secs

ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops

This week I spoke with Tristan Zajonc, the CEO and cofounder of Continual, a company that provides an AI layer for enterprise companies or, as we'll get into in the podcast, the so-called 'modern data stack'.

Neurosymbolic AI with Mohan Mahadevan

January 27th, 2022  |  58 mins 55 secs

ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops

Our guest this week was Mohan Mahadevan, a senior VP at Onfido, a machine-learning powered identity verification platform. He has previously worked at Amazon heading up a computer vision team working on robotics applications as well as for many years at KLA, a leading semiconductor hardware company. He holds a doctorate in theoretical physics from Colorado State University.

Creating Tools that Spark Joy with Ines Montani

January 13th, 2022  |  43 mins 46 secs

data-science, machine-learning, nlp, open-source

Our guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool.

Monitoring Your Way to ML Production Nirvana with Danny Leybzon

December 16th, 2021  |  40 mins 34 secs

aws, machine-learning, mlops, zenml

This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.

Practical MLOps with Noah Gift

December 2nd, 2021  |  47 mins 14 secs

aws, machine-learning, mlops

Noah Gift is the founder of Pragmatic A.I. Labs and author of 'Practical MLOps'. We discuss the role of MLOps in an organisation, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.