ZenML
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Tag: bentoml

3 posts with this tag

New Features: Enhanced Dashboard, Improved Performance, and Streamlined User Experience
ZenML Updates

New Features: Enhanced Dashboard, Improved Performance, and Streamlined User Experience

ZenML 0.68.0 introduces several major enhancements including the return of stack components visualization on the dashboard, powerful client-side caching for improved performance, and a streamlined onboarding process that unifies starter and production setups. The release also brings improved artifact management with the new `register_artifact` function, enhanced BentoML integration (v1.3.5), and comprehensive documentation updates, while deprecating legacy features including Python 3.8 support.

ZenML Team
Oct 28, 20243 min
Streamlining Model Deployment with ZenML and BentoML
MLOps

Streamlining Model Deployment with ZenML and BentoML

This blog post discusses the integration of ZenML and BentoML in machine learning workflows, highlighting their synergy that simplifies and streamlines model deployment. ZenML is an open-source MLOps framework designed to create portable, production-ready pipelines, while BentoML is an open-source framework for machine learning model serving. When combined, these tools allow data scientists and ML engineers to streamline their workflows, focusing on building better models rather than managing deployment infrastructure. The combination offers several advantages, including simplified model packaging, local and container-based deployment, automatic versioning and tracking, cloud readiness, standardized deployment workflow, and framework-agnostic serving.

Jayesh SharmaJayesh Sharma
Oct 10, 20245 mins
How to train and deploy a machine learning model on AWS Sagemaker with ZenML and BentoML
ZenML

How to train and deploy a machine learning model on AWS Sagemaker with ZenML and BentoML

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.

Safoine El khabichSafoine El khabich
Dec 14, 202211 Mins Read

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