This article explores the cost-effective and efficient solution of using Dagster and SkyPilot to orchestrate ML training jobs within a single data platform. The combination abstracts the resource acquisition and job execution through an intuitive declarative DSL, allowing data engineering to invite ML teams to bring their existing ML training and inference pipelines into Dagster and orchestrate them with minimal code changes. SkyPilot enables resilient and cost-effective AI/ML training jobs across cloud environments and regions by implementing the Sky Computing paradigm, where workloads can be transparently executed on one or more clouds, abstracting the provision of resources and execution of arbitrary workloads across cloud vendors while automatically maximizing cost savings and availability for users. The solution is particularly useful for organizations that want to execute AI training jobs in a cost-effective fashion with plug-in support for spot instances and automatic recovery from preemption.