Tag: zenml

• 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.
• ZenML's Month of MLOps Recap (22 Nov 2022)
  The ZenML MLOps Competition ran from October 10 to November 11, 2022, and was a wonderful expression of open-source MLOps problem-solving.
• ZenML 0.22.0: BentoML Integration and A Revamped Airflow Orchestrator! (05 Nov 2022)
  This release comes with a new BentoML integration and a reworked Airflow orchestrator. We also fixed server-related performance issues and other minor improvements!
• 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.
• ZenML 0.20.0: Our Biggest Release Yet! (05 Oct 2022)
  The 0.20.0 release is a seminal release in the history of ZenML. Following ten months of continuous feedback and iteration, we bring you a whole new architecture and redesign of ZenML - and a new dashboard to boot! Collaboration among teams has also been taken to a new level in the new version.
• ZenML's Month of MLOps: Competition Announcement (26 Sep 2022)
  Join us for a celebration of open-source MLOps, where you get to both express your creativity and solve a problem that is interesting to you! Our MLOps Competition runs from October 10 to November 11, 2022.
• What's New in v0.13: Spark, Custom Code Deployment, Stack Recipes, and More (21 Sep 2022)
  This release blog describes the changes for three releases v0.13.0 (major release), v0.13.1 and v0.13.2 (minor releases). v0.13.0 brings the first iteration of our Apache Spark integration. v0.13.1 and v0.13.2 includes several bugfixes and quality of life improvements for ZenML users.
• 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.
• Deploy your ML models with KServe and ZenML (04 Aug 2022)
  How to use ZenML and KServe to deploy serverless ML models in just a few steps.
• What's New in v0.12.0: Serverless Inferencing on Kubernetes with KServe (02 Aug 2022)
  This release brings KServe integration to ZenML.
• What's New in v0.11.0: Label All The Things! (19 Jul 2022)
  This release brings the first iteration of the ZenML annotation stack component and an integration with Label Studio, the popular open-source tool that supports many annotation types. We've also made significant updates to our documentation.
• 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.
• How to run production ML workflows natively on Kubernetes (29 Jun 2022)
  Getting started with distributed ML in the cloud: How to orchestrate ML workflows natively on Amazon Elastic Kubernetes Service (EKS).
• What's New in v0.10.0: A Kubernetes Native Orchestrator! (28 Jun 2022)
  This release brings the highly-requested Kubernetes Orchestrator and a Vault secret manager to ZenML! We have also added Data Validators as a new stack component and an implementation for Great Expectations to kick things off.
• Serverless MLOps with Vertex AI (27 Jun 2022)
  How ZenML lets you have the best of both worlds, serverless managed infrastructure without the vendor lock in.
• 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.
• What's New in v0.9.0: Everyone Gets an Orchestrator! (13 Jun 2022)
  We added two new orchestrators (Github Actions and Vertex AI), an Azure Secrets Manager integration, a Slack integration and a bunch of other smaller changes in the latest release.
• 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.
• What's New in v0.8.0: Extend ZenML Any Way You Like! (18 May 2022)
  Release notes for the new version of ZenML. We've added a ton of extensibility improvements, documentation and guides that take away most of the hard work of figuring out how to add custom components. Our CLI also has been beautified and it should even run a bit faster too!
• What's New in v0.7.2 and v0.7.3: HuggingFace, Weights & Biases, LightGBM, XGBoost, and more! (28 Apr 2022)
  The release adds experiment tracking stack components, integrations with HuggingFace, Weights & Biases and also improvements to how the Seldon Core deployer handles secrets from the secrets manager component.
• What's New in v0.7.1: Fetch data from your feature store and deploy models on Kubernetes (11 Apr 2022)
  The release introduces the Seldon Core ZenML integration, featuring the Seldon Core Model Deployer and a Seldon Core standard model deployer step. It also includes two new integrations with Feast as ZenML's first feature store addition and NeuralProphet adding to the growing list of training libraries supported.
• What's New in v0.7.0: 🔡 User Profiles and Secret Storage 🤫 (28 Mar 2022)
  Release notes for the new version of ZenML. We've added the ability to store your stacks in profiles accessible system-wide instead of just in individual project folders. We also added a way to store and retrieve passwords and secrets, including the possibility to integrate the AWS Secrets Manager. Also: run individual steps on Vertex AI.
• 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.
• What's New in v0.6.3: Run Steps on Sagemaker and AzureML ☁️ (14 Mar 2022)
  Release notes for the new version of ZenML. We've added the ability to run steps on AWS Sagemaker and AzureML. We added a new Tensorboard visualization that runs when using the Kubeflow orchestrator. You'll also find a lot of smaller improvements, documentation additions and bug fixes in this release.
• 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.
• 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.
• 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.
• What's New in v0.6.2: ♻️ Continuous Deployment and a fresh CLI 👩‍💻 (23 Feb 2022)
  Release notes for the new version of ZenML. We've added the ability to serve models using MLflow deployments, and we've refreshed how our CLI looks using the popular 'rich' library. You'll also find a lot of smaller improvements, documentation additions and bug fixes in this release.
• 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.
• What's New in v0.6.1: Reach for the AWS and Azure Cloud! ☁️ (07 Feb 2022)
  Release notes for the new version of ZenML. We now support AWS S3 and Azure Blob Storage as artifact stores. You'll also find a lot of smaller improvements and bug fixes in this release.
• 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?
• 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.
• What's New in v0.6.0: whylogs integration and some big core architecture changes (26 Jan 2022)
  Release notes for the new version of ZenML. ZenML now supports whylogs (from whylabs) that logs data from your ML pipelines in production. We also made some sizeable (breaking 😢) core architecture changes
• What's New in v0.5.7 (17 Jan 2022)
  Release notes for the new version of ZenML. ZenML now supports MLFlow for tracking pipelines as experiments and Evidently for detecting drift in your ML pipelines in production.
• 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.
• What's New in v0.5.6 (23 Dec 2021)
  Release notes for the new version of ZenML.
• 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.
• What's New in v0.5.5 (13 Dec 2021)
  Release notes for the new version of ZenML.
• 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!
• What's New in v0.5.4 (06 Dec 2021)
  Release notes for the new version of ZenML.
• 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.
• Lazy Loading Integrations in ZenML (26 Nov 2021)
  How integrations work under the hood to connect you to the tools you know and love.
• What's New in v0.5.3 (24 Nov 2021)
  Release notes for the new version of ZenML.
• Pipeline Conversations: Our New Podcast (19 Nov 2021)
  We launched a podcast to have conversations with people working to productionize their machine learning models and to learn from their experience.
• Introducing the revamped ZenML 0.5.x (16 Nov 2021)
  We rewrote much of our code base to make it more robust and also to enable new features.
• 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.
• 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!
• ZenML will be open source (11 Nov 2020)
  An overview of some of the capabilities that ZenML will unlock for our users.
• 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.
• 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.
• 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.