Analysis of 1,200+ production LLM deployments reveals that context engineering, architectural guardrails, and traditional software engineering skills—not frontier models or prompt tricks—separate teams shipping reliable AI systems from those stuck in demo purgatory.
ZenML's latest release 0.64.0 streamlines MLOps workflows with notebook integration for remote pipelines, optimized Docker builds, AzureML orchestrator support, and Terraform modules for cloud stack provisioning. These updates aim to speed up development, ease cloud deployments, and improve efficiency for data science teams.
Cloud Composer (Airflow) vs Vertex AI (Kubeflow): How to choose the right orchestration service on GCP based on your requirements and internal resources.
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.
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