Tag: machine-learning

• Need an open-source data annotation tool? We've got you covered! (10 Jun 2022)
  We put together a list of 48 open-source annotation and labeling tools to support different kinds of machine-learning projects.
• 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.
• How to get the most out of data annotation (02 Jun 2022)
  I explain why data labeling and annotation should be seen as a key part of any machine learning workflow, and how you probably don't want to label data only at the beginning of your process.
• Will they stay or will they go? Building a Customer Loyalty Predictor (27 May 2022)
  We built an end-to-end production-grade pipeline using ZenML for a customer churn model that can predict whether a customer will remain engaged with the company or not.
• Predicting how a customer will feel about a product before they even ordered it (20 Apr 2022)
  We built an end to end continuous deployment pipeline using ZenML for a customer satisfaction model that uses historical data of the customer predict the review score for the next order or purchase.
• 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.
• Spot the difference in ML costs (28 Jan 2021)
  Spot instances are a great option for anyone training machine learning models; they're much cheaper than other on-demand options, albeit with some drawbacks.
• Is your Machine Learning Reproducible? (19 Jan 2021)
  Short answer: not really, but it can become better!
• Can you do the splits? (11 Jun 2020)
  Splitting up datasets is part of the daily work of a data scientist, but there's more complexity and art to it than first meets the eye.
• Predicting the winner of a DotA 2 match using distributed deep learning pipelines (01 May 2020)
  ZenML makes it easy to setup training pipelines that give you all the benefits of cached steps.
• Deep Learning on 33,000,000 data points using a few lines of YAML (01 Apr 2020)
  Use YAML files to help configure pipelines that can run complex deep learning training.
• 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.
• Distributed PCA using TFX (27 Feb 2020)
  We use PCA to reduce the dimension of input vectors while retaining maximal variance.