FloraCast is a production-ready template that shows how to build a forecasting platform—config-driven experiments, model versioning/staging, batch inference, and scheduled retrains—with ZenML and Darts.
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.
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.
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