Tag: bigger-picture

• Podcast: ML Monitoring with Emeli Dral (07 Jul 2022)
  This week I spoke 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 customizable and powerful.
• Podcast: Edge Computer Vision with Karthik Kannan (30 Jun 2022)
  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.
• Podcast: Humans in the Loop with Iva Gumnishka (23 Jun 2022)
  This week I spoke with Iva Gumnishka, the founder of Humans in the Loop. They are an organization 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.
• Podcast: ML Engineering with Ben Wilson (08 Jun 2022)
  This week I spoke with Ben Wilson, author of 'Machine Learning Engineering in Action', 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.
• The Framework Way is the Best Way: the pitfalls of MLOps and how to avoid them (24 May 2022)
  As our AI/ML projects evolve and mature, our processes and tooling also need to keep up with the growing demand for automation, quality and performance. But how can we possibly reconcile our need for flexibility with the overwhelming complexity of a continuously evolving ecosystem of tools and technologies? MLOps frameworks promise to deliver the ideal balance between flexibility, usability and maintainability, but not all MLOps frameworks are created equal. In this post, I take a critical look at what makes an MLOps framework worth using and what you should expect from one.
• Podcast: Trustworthy ML with Kush Varshney (14 Apr 2022)
  This week I spoke with Kush Varshney, author of 'Trustworthy Machine Learning', a fantastic guide and overview of all of the different ways machine learning can go wrong and an optimistic take on how to think about addressing those issues.
• 'It's the data, silly!' How data-centric AI is driving MLOps (07 Apr 2022)
  ML practitioners today are embracing data-centric machine learning, because of its substantive effect on MLOps practices. In this article, we take a brief excursion into how data-centric machine learning is fuelling MLOps best practices, and why you should care about this change.
• Podcast: Open-Source MLOps with Matt Squire (31 Mar 2022)
  This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs, where they help partner organizations think through how best to productionise their machine learning workflows.
• Podcast: Practical Production ML with Emmanuel Ameisen (18 Mar 2022)
  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.
• Everything you ever wanted to know about MLOps maturity models (07 Mar 2022)
  An exploration of some frameworks created by Google and Microsoft that can help think through improvements to how machine learning models get developed and deployed in production.
• Podcast: From Academia to Industry with Johnny Greco (03 Mar 2022)
  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.
• Podcast: The Modern Data Stack with Tristan Zajonc (10 Feb 2022)
  Tristan and Alex discuss where machine learning and AI are headed in terms of the tooling landscape. Tristan outlined a vision of a higher abstraction level, something he's working on making a reality as CEO at Continual.
• ZenML - Why we built it (14 Dec 2021)
  All the advantages that ZenML will bring you if you choose to use it to productionize your model development workflows.
• ZenML will be open source (11 Nov 2020)
  An overview of some of the capabilities that ZenML will unlock for our users.
• MLOps: Learning from history (09 Nov 2020)
  MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.
• 12 Factors of Reproducible Machine Learning in Production (28 Sep 2020)
  A set of guiding principles to help you better productionize your machine learning models.
• Why ML in production is (still) broken - [#MLOps2020] (26 Jun 2020)
  The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.
• Avoiding technical debt with ML pipelines (06 Jun 2020)
  Pipelines help you think and act better when it comes to how you execute your machine learning training workflows.
• Why deep learning development in production is (still) broken (01 Mar 2020)
  Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.