Tag: tooling

• ZenNews: Generate summarized news on a schedule (24 Feb 2023)
  ZenNews is a tool powered by ZenML that can automate the summarization of news sources and save you time and effort while providing you with the information you need.
• Build ML Models Faster with ZenML Project Templates (10 Feb 2023)
  Getting started with your ML project work is easier than ever with Project Templates, a new way to generate scaffolding and a skeleton project structure based on best practices.
• How to train and deploy a machine learning model on AWS Sagemaker with ZenML and BentoML (14 Dec 2022)
  Learn how to use ZenML pipelines and BentoML to easily deploy machine learning models, be it on local or cloud environments. We will show you how to train a model using ZenML, package it with BentoML, and deploy it to a local machine or cloud provider. By the end of this post, you will have a better understanding of how to streamline the deployment of your machine learning models using ZenML and BentoML.
• Tracking experiments in your MLOps pipelines with ZenML and Neptune (05 Dec 2022)
  ZenML 0.23.0 comes with a brand-new experiment tracker flavor - Neptune.ai! We dive deeper in this blog post.
• Transforming Vanilla PyTorch Code into Production Ready ML Pipeline - Without Selling Your Soul (27 Oct 2022)
  Transform quickstart PyTorch code as a ZenML pipeline and add experiment tracking and secrets manager component.
• Keep the lint out of your ML pipelines! Use Deepchecks to build and maintain better models with ZenML! (06 Sep 2022)
  Test automation is tedious enough with traditional software engineering, but machine learning complexities can make it even less appealing. Using Deepchecks with ZenML pipelines can get you started as quickly as it takes you to read this article.
• ZenML sets up Great Expectations for continuous data validation in your ML pipelines (07 Jul 2022)
  ZenML combines forces with Great Expectations to add data validation to the list of continuous processes automated with MLOps. Discover why data validation is an important part of MLOps and try the new integration with a hands-on tutorial.
• 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.
• 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.
• How we made our integration tests delightful by optimizing the way our GitHub Actions run our test suite (09 Mar 2022)
  As we outgrew our initial template Github Action workflow, here's the five things we added to our Github Action arsenal to fit our growing needs: Caching, Reusable Workflows, Composite Actions, Comment Triggers and Concurrency Management.
• 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.
• Richify that CLI! (28 Feb 2022)
  We recently reworked a number of parts of our CLI interface. Here are some quick wins we implemented along the way that can help you improve how users interact with your CLI via the popular open-source library, rich.
• Aggregating and Reporting ZenML Company Metrics on a Schedule (15 Feb 2022)
  We built a low barrier of entry reporting pipeline tool that collects, stores, and displays key performance indicators without a data lake.
• 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.
• How to improve your experimentation workflows with MLflow Tracking and ZenML (09 Feb 2022)
  Use MLflow Tracking to automatically ensure that you're capturing data, metadata and hyperparameters that contribute to how you are training your models. Use the UI interface to compare experiments, and let ZenML handle the boring setup details.
• How to build a three-pointer prediction pipeline (02 Feb 2022)
  We challenged ourselves to put our own tool to the test and set up a few pipelines to answer two questions: Did Steph Curry change the game of basketball? And how many three-pointers will be in the next NBA game?
• Type hints are good for the soul, or how we use mypy at ZenML (31 Jan 2022)
  A dive into Python type hinting, how implementing them makes your codebase more robust, and some suggestions on how you might approach adding them into a large legacy codebase.
• 10 Reasons ZenML ❤️ Evidently AI's Monitoring Tool (21 Jan 2022)
  ZenML recently added an integration with Evidently, an open-source tool that allows you to monitor your data for drift (among other things). This post showcases the integration alongside some of the other parts of Evidently that we like.
• Get to know ZenML through examples (06 Jan 2022)
  ZenML has a treasure-trove of examples available for users to get to know specific features. Using these examples, running them and pulling refreshed versions is easy with our CLI that takes on the heavy work for you.
• How we track our todo comments using GitHub Actions (01 Dec 2021)
  A programmatic means of ensuring #TODO comments made in code also end up in our Jira ticketing system.
• Lazy Loading Integrations in ZenML (26 Nov 2021)
  How integrations work under the hood to connect you to the tools you know and love.
• 10 Ways To Level Up Your Testing with Python (04 Nov 2021)
  A mix of mental and technical skills you can develop to get better at testing your Python code.