Discover how to optimize GPU utilization in Kubernetes environments by integrating NVIDIA's KAI Scheduler with ZenML pipelines, enabling fractional GPU allocation for improved resource efficiency and cost savings in machine learning workflows.
Learn how ZenML unified MLOps across AWS, Azure, on-premises, and tactical edge environments for defense contractors like the German Bundeswehr and French aerospace manufacturers. Overcome hybrid infrastructure complexity, maintain security compliance, and accelerate AI deployment from development to battlefield. Essential guide for defense AI teams managing multi-classification environments and $1.5B+ military AI initiatives.
Kubernetes powers 96% of enterprise ML workloads but often creates more friction than function—forcing data scientists to wrestle with infrastructure instead of building models while wasting expensive GPU resources. Our latest post shows how ZenML combined with NVIDIA's KAI Scheduler enables financial institutions to implement fractional GPU sharing, create team-specific ML stacks, and streamline compliance—accelerating innovation while cutting costs through intelligent resource orchestration.
This release incorporates updates to the SageMaker Orchestrator, DAG Visualizer, and environment variable handling. It also includes Kubernetes support for Skypilot and an updated Deepchecks integration. Various other improvements and bug fixes have been implemented across different areas of the platform.
The combination of ZenML and Neptune can streamline machine learning workflows and provide unprecedented visibility into experiments. ZenML is an extensible framework for creating production-ready pipelines, while Neptune is a metadata store for MLOps. When combined, these tools offer a robust solution for managing the entire ML lifecycle, from experimentation to production. The combination of these tools can significantly accelerate the development process, especially when working with complex tasks like language model fine-tuning. This integration offers the ability to focus more on innovating and less on managing the intricacies of your ML pipelines.
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