machine-learning

I'll be having some conversations with the people behind the tools that ZenML offers as integrations. We spoke with Ben Wilson a few weeks back, and today I'm pleased to publish this conversation with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning. We discussed the challenges around building a tool that is both straightforward to use while also customisable and powerful. We also got into the thinking behind how they grew their community and blog along the way. Special Guest: Emeli Dral.

I'll be having some conversations with the people behind the tools that ZenML offers as integrations. We spoke with Ben Wilson a few weeks back, and today I'm pleased to publish this conversation with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning. We discussed the challenges around building a tool that is both straightforward to use while also customisable and powerful. We also got into the thinking behind how they grew their community and blog along the way. Special Guest: Emeli Dral.

This week I spoke with Karthik Kannan, cofounder and CTO of Envision (https://www.letsenvision.com/), 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.

This week I spoke with Karthik Kannan, cofounder and CTO of Envision (https://www.letsenvision.com/), 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.

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 (https://www.linkedin.com/in/ivagumnishka/), the founder of Humans in the Loop (https://humansintheloop.org/). 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.

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 (https://www.linkedin.com/in/ivagumnishka/), the founder of Humans in the Loop (https://humansintheloop.org/). 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.

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 (https://www.manning.com/books/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.

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 (https://www.manning.com/books/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.

This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs (https://www.fuzzylabs.ai), 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 (https://github.com/fuzzylabs/awesome-open-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.

This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs (https://www.fuzzylabs.ai), 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 (https://github.com/fuzzylabs/awesome-open-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.

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.

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.

This week I spoke with Johnny Greco (https://johnnygreco.space), 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.

This week I spoke with Johnny Greco (https://johnnygreco.space), 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.

This week I spoke with Tristan Zajonc (https://www.linkedin.com/in/tristanzajonc/), the CEO and cofounder of Continual (https://continual.ai/), 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.

This week I spoke with Tristan Zajonc (https://www.linkedin.com/in/tristanzajonc/), the CEO and cofounder of Continual (https://continual.ai/), 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.

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.

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.

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. I've always found Ines to be personally inspiring in the work that she and her team produce as well as how they present themselves to the world, so it was a real pleasure to get to dive into the weeds as to exactly how that happens. We also discuss how NLP works in production, what reproducibility means for ML projects and much more. Special Guest: Ines Montani.