Tag: evergreen

• Unleashing More Power and Flexibility with ZenML's New Pipeline and Step Syntax (26 May 2023)
  The 0.40.0 release introduces a completely reworked interface for developing your ZenML steps and pipelines. It makes working with these components much more natural, intuitive, and enjoyable.
• Using ZenML with LLMs to Analyze Your Databases: A Case Study with you-tldr.com and Supabase/GPT-4 (30 Apr 2023)
  Explore how ZenML, an MLOps framework, can be used with large language models (LLMs) like GPT-4 to analyze and version data from databases like Supabase. In this case study, we examine the you-tldr.com website, showcasing ZenML pipelines asynchronously processing video data and generating summaries with GPT-4. Understand how to tackle large language model limitations by versioning data and comparing summaries to unlock your data's potential. Learn how this approach can be easily adapted to work with other databases and LLMs, providing flexibility and versatility for your specific needs.
• Introducing ZenML Hub: Streamlining MLOps Collaboration with Reusable Components (12 Apr 2023)
  ZenML is launching the ZenML Hub, a novel plugin system that allows users to contribute and consume stack component flavors, pipelines, steps, materializers, and other pieces of code seamlessly in their ML pipelines.
• Productionalizing LangChain and LlamaIndex with a ZenML MLOps Pipeline to Help Community Slack Support (31 Mar 2023)
  We decided to explore how the emerging technologies around Large Language Models (LLMs) could seamlessly fit into ZenML's MLOps workflows and standards. We created and deployed a Slack bot to provide community support.
• 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.
• Detecting Fraudulent Financial Transactions with ZenML (16 Dec 2022)
  A winning entry - 2nd prize winner at Month of MLOps 2022 competition.
• 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.
• 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: 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.
• Move over Kubeflow, there's a new sheriff in town: Github Actions 🤠 (20 Jun 2022)
  This tutorial presents an easy and quick way to use GitHub Actions to run ML pipelines in the cloud. We showcase this functionality using Microsoft's Azure Cloud but you can use any cloud provider you like.
• 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.
• 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.
• All Continuous, All The Time: Pipeline Deployment Patterns with ZenML (11 May 2022)
  Connecting model training pipelines to deploying models in production is seen as a difficult milestone on the way to achieving MLOps maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
• 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.
• Run your steps on the cloud with Sagemaker, Vertex AI, and AzureML (25 Mar 2022)
  With ZenML 0.6.3, you can now run your ZenML steps on Sagemaker, Vertex AI, and AzureML! It’s normal to have certain steps that require specific infrastructure (e.g. a GPU-enabled environment) on which to run model training, and Step Operators give you the power to switch out infrastructure for individual steps to support this.
• 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.
• How to painlessly deploy your ML models with ZenML (02 Mar 2022)
  Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
• 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.
• Podcast: Neurosymbolic AI with Mohan Mahadevan (27 Jan 2022)
  Mohan and Alex 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.
• 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.
• Podcast: Monitoring Your Way to ML Production Nirvana with Danny Leybzon (16 Dec 2021)
  We discuss how to monitor models in production, and how it helps you in the long-run.
• 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.
• Why you should be using caching in your machine learning pipelines (07 Dec 2021)
  Use caches to save time in your training cycles, and potentially to save some money as well!
• Podcast: Practical MLOps with Noah Gift (02 Dec 2021)
  We discuss the role of MLOps in an organization, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.
• 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.
• Taking on the ML pipeline challenge (27 Oct 2021)
  Why data scientists need to own their ML workflows in production.
• Why ML should be written as pipelines from the get-go (31 Mar 2021)
  Eliminate technical debt with iterative, reproducible pipelines.
• 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.
• 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.
• Distributed PCA using TFX (27 Feb 2020)
  We use PCA to reduce the dimension of input vectors while retaining maximal variance.