ZenML 0.6.1 is out and it’s all about Cloud storage ☁️! We have improved your ability to work with AWS services and added a brand-new Azure integration! Run your pipelines on AWS and Azure now and let us know how it went on our Slack.
Smaller changes that you’ll notice include much-awaited updates and fixes, including the first iterations of scheduling pipelines and tracking more reproducibility-relevant data in the metadata store. For a detailed look at what’s changed, give our full release notes a glance.
You can now use Azure Blob Storage and AWS’ S3 as your artifact store for ZenML pipelines. We implemented all the relevant
fileio methods to enable this. If you prefer to use Amazon’s AWS or Microsoft’s Azure, we hope these new integrations will be the start of more options for you when using ZenML.
To learn more, check out the new documentation page we just included to guide you in deploying your pipelines to AWS, GCP and/or Azure.
Some significant improvements behind the scenes, though some of these are the first in a series of wider improvements to specific areas:
Scheduleobjects (with the exception of the local orchestrator). This is the first part of our implementation of scheduled pipelines in ZenML. Watch this space for more!
As the codebase and functionality of ZenML grows, we always want to make sure our documentation is clear, up-to-date and easy to use. We made a number of changes in this release that will improve your experience in this regard:
mypyin your own codebase, we added the relevant file that will mark it as a ‘typed’ package. You’re welcome! We saved you from some
Join our Slack to let us know what you think we should build next!