Recent releases of ZenML’s Python package have included a better way to deploy machine learning infrastructure or stacks, new annotation tool integrations, an upgrade of our Pydantic dependency and lots of documentation improvements.
Infrastructure-as-code meets MLOps: Terraform modules for deploying ML infrastructure on AWS, GCP, and Azure on the Hashicorp registry.
Now you can easily connect AWS, GCP, and Azure cloud providers with ZenML directly with an easy wizard in the dashboard.
Master cloud-based LLM finetuning: Set up infrastructure, run pipelines, and manage experiments with ZenML's Model Control Plane for Meta's latest Llama model.
Learn how to leverage caching, parameterization, and smart infrastructure switching to iterate faster on machine learning projects while maintaining reproducibility.
Shipping 🤗 datasets visualization embedded in the ZenML dashboard in a few hours
Streamline your machine learning platform with ZenML. Learn how ZenML's 1-click cloud stack deployments simplify setting up MLOps pipelines on AWS, GCP, and Azure.
OpenAI's Batch API allows you to submit queries for 50% of what you'd normally pay. Not all their models work with the service, but in many use cases this will save you lots of money on your LLM inference, just so long as you're not building a chatbot!
On the difficulties in precisely defining a machine learning pipeline, exploring how code changes, versioning, and naming conventions complicate the concept in MLOps frameworks like ZenML.
Exploring the evolution of MLOps practices in organizations, from manual processes to automated systems, covering aspects like data science workflows, experiment tracking, code management, and model monitoring.
How to use ZenML and dbt together, all powered by ZenML's built-in success hooks that run whenever your pipeline successfully completes.
