Unleashing More Power and Flexibility with ZenML's New Pipeline and Step Syntax
May 26, 2023 - Alex Strick van Linschoten - 7 mins read
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
April 30, 2023 - Hamza Tahir - 10 mins read
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
April 12, 2023 - Hamza Tahir - 6 mins read
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
March 31, 2023 - Alex Strick van Linschoten - 10 mins read
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.
How to train and deploy a machine learning model on AWS Sagemaker with ZenML and BentoML
December 14th, 2022 - Safoine El khabich - 11 mins read
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.