Seamlessly automating the journey from training to production, ZenML's new NLP project template offers a comprehensive MLOps solution for teams deploying Huggingface models to AWS Sagemaker endpoints. With its focus on reproducibility, scalability, and best practices, the template simplifies the integration of NLP models into workflows, complete with lineage tracking and various deployment options.
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.
This release comes with a new BentoML integration and a reworked Airflow orchestrator. We also fixed server-related performance issues and other minor improvements!
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.
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.
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