This week I spoke with Karthik Kannan, cofounder and CTO of Envision, a company that builds on top of the Google Glass and using Augmented Reality features of phones to allow visually impaired people to better sense the environment or objects around them.

Their software and devices are pretty popular and as you'll hear in this conversation, they've been on a real journey to get to where they are now.

In particular, I really enjoyed the parts where Karthik explained their development and deployment process in detail. It's not too often that you get a deep dive into the workflows and stacks of an embedded computer vision company and tool and so I think you're going to really enjoy this one.

Special Guest: Karthik Kannan.

Links:

• Karthik Kannan (@meTheKarthik) / Twitter
• Envision - Hear what you want to see.
• Glass – Glass
• Introducing Envision Glasses: AI-powered smartglasses for the Blind & Visually Impaired - YouTube
• Envision Blog

In this episode, I'm really happy to be able to continue the dialogue we've been having with our users and community around the role of data annotation and labeling in MLOps.

We were lucky to get to talk to Iva Gumnishka, the founder of Humans in the Loop. They are an organisation that provides data annotation and collection services. Their teams are primarily made up of those who have been affected by conflict and now are asylum seekers or refugees.

Iva has a ton of experience working with annotation and has seen how different companies build this into their production machine learning lifecycles. We're continuing to work on a feature that will allow you to do this as part of your MLOps workflow when using ZenML, and I welcome any feedback you might have on the back of this podcast or the articles we've been publishing on the ZenML blog.

Special Guest: Iva Gumnishka.

Links:

• Humans in the Loop | Image annotation for ethical AI
• Blog | Humans in the Loop
• 10 of the best open-source annotation tools for computer vision 2022 | Humans in the Loop
• zenml-io/awesome-open-data-annotation: Open Source Data Annotation & Labeling Tools
• Need an open-source data annotation tool? We’ve got you covered! | ZenML Blog
• How to get the most out of data annotation | ZenML Blog
• Foundation | Humans in the Loop
• Your Data Needs a Human Touch. The story of Iva Gumnishka, a Bulgarian… | by Antoaneta Manko | womenintechglobal | Medium

We took a few weeks break to reach out to some new guests and so I think we can go so far as declaring this next series of episodes as season 2 of Pipeline Conversations.

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'. It's a jam-backed guide to all the lessons that Ben has learned over his years working to help companies get models out into the world and run them in production.

I was really lucky to get to talk to Ben about his new book and also about the mental models he thinks are useful to bring to bear on this complicated problem many of us are working on.

Special Guest: Ben Wilson.

Links:

• Ben Wilson (LinkedIn)
• Adventures in Machine Learning (podcast)
• Machine Learning Engineering in Action (Manning book)
• Databricks

Adam and Hamza return for a short discussion of what we've been busy working on during the previous few months, where we're going with ZenML and why it's so amazing to be building an open-source tool.

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.

I also got a stronger sense of appreciation for how good MLOps practices and workflows offered a clear path to ensuring that your machine learning models and behaviours could become more trustworthy. Kush has done a lot of interesting work, particularly with the AI Fairness 360 and AI Explainability 360 toolkits that I'm sure listeners of this podcast would find worth checking out.

Special Guest: Kush Varshney.

Links:

• Trustworthy Machine Learning by Kush R. Varshney
• Home - AI Explainability 360
• Home - AI Fairness 360
• Kush Varshney
• Kush Varshney (@krvarshney) / Twitter
• Kush Varshney | LinkedIn
• Trustworthy Machine Learning: Varshney, Kush R.: 9798411903959: Amazon.com: Books

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.

Matt and FuzzyLabs are also behind the Awesome Open Source MLOps GitHub repo where you can find all the options for an open-source MLOps stack of your dreams.

Matt has been an enthusiastic early supporter of the work we do at ZenML so it was really amazing to get to talk to him and get his take based on the many experiences he's had seeing how ML is done out in the field.

Special Guest: Matt Squire.

Links:

• Matt Squire | LinkedIn
• Open Source MLOps - Fuzzy Labs
• fuzzylabs/awesome-open-mlops: The Fuzzy Labs guide to the universe of open source MLOps
• Evidently AI - Open-Source Machine Learning Monitoring
• Data Version Control · DVC
• Blog - Fuzzy Labs
• The Road to Zen: getting started with pipelines - Fuzzy Labs
• The Road to Zen: running experiments - Fuzzy Labs
• Guides to MLOps - Fuzzy Labs

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.

Emmanuel has previously worked at Insight Data Science where he was involved in mentoring and guiding dozens of data scientists who were working on building their ML portfolio projects. He brings a wealth of experience to the table and I'm really excited to present our conversation to you.

Special Guest: Emmanuel Ameisen.

Links:

• Emmanuel Ameisen (@mlpowered) / Twitter
• Emmanuel Ameisen | LinkedIn — ML Engineer at Stripe, years of experience in Data Science.

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.

Special Guest: Johnny Greco.

Links:

• Johnny Greco
• johnnygreco (Johnny Greco)
• Johnny Greco (@johnnypgreco) / Twitter
• ArtPop — ArtPop documentation
• ‪Johnny P Greco‬ - ‪Google Scholar‬
• johnnygreco/love-thy-pixels: Spreading the love for galaxies one pixel at a time
• Johnny Greco: A New View of Low Surface Brightness Galaxies from the Hyper Suprime-Cam Survey - YouTube

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'.

He previously worked at Cloudera as a CTO for machine learning and as the head of the data science platform there, and he holds a PhD in public policy from Harvard University.

In our conversation we discussed the different levels of abstraction one can take when dealing with the MLOps problem. We spoke about all the different ways that machine learning can fail in production settings and of course we discussed the concept of the 'modern data stack' and what that means.

Special Guest: Tristan Zajonc.

Links:

• Tristan Zajonc (@tristanzajonc) / Twitter
• Tristan Zajonc | LinkedIn
• Continual | AI/ML for Your Cloud Data Warehouse
• Company | Continual - Operational AI for the Enterprise
• The Modern Data Stack Ecosystem - Fall 2021 Edition
• The Future of the Modern Data Stack
• Introducing Continual – the missing AI layer for the modern data stack
• Cloudera | The Hybrid Data Cloud Company
• Tristan Zajonc, Sense Platform // Data Driven #28 // June 2014 (Hosted by FirstMark Capital) - YouTube
• Sense Preview - YouTube
• DC_THURS on Operational AI for the Modern Data Stack w/ Tristan Zajonc (Continual) - YouTube
• Enterprise Machine Learning on K8s: Lessons Learned and the Road... - Timothy Chen & Tristan Zajonc - YouTube

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.

Mohan had mentioned that he thought it might be interesting to discuss neurosymbolic AI, and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.

Special Guest: Mohan Mahadevan.

Links:

• Mohan Mahadevan - Senior VP, Applied Science - Onfido | LinkedIn
• Onfido | Document ID & Facial Biometrics Verification
• Neuro-symbolic AI | IBM Research Teams
• AI’s next big leap
• Neurosymbolic AI - David Cox slides
• MIT 6.S191 (2020): Neurosymbolic AI - YouTube
• Neurosymbolic AI Explained - YouTube