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Scaling machine learning (ML) experiments is a challenging process that requires efficient resource management, experiment tracking, and infrastructure scalability. neptune.ai offers a centralized platform to manage ML experiments, track real-time model performance, and store metadata. Kubernetes automates container orchestration, improves resource utilization, and enables horizontal and vertical scalability. Combining neptune.ai and Kubernetes provides a robust solution for scaling ML experiments, making it easier to manage and scale experiments across multiple environments and team members. Scaling machine-learning experiments efficiently is a challenge for ML teams. The complexity lies in managing configurations, launching experiment runs, tracking their outcomes, and optimizing resource allocation.…