Hybrid Cloud MLOps: Train Anywhere, Deploy Everywhere
Treat your ML infrastructure like your applications—a consistent platform that runs anywhere, avoiding siloed stacks for data science and app dev. Use it to train on cloud GPUs but deploy on-prem for low latency, ensuring dev/prod parity across environments.
Treat your ML infrastructure like your applications: a single, consistent platform that runs anywhere, from on-premise data centers to public clouds. This integrates MLOps and DevOps, breaking down silos between data scientists and app developers. This is key for training data-heavy models on-prem for security, then deploying them to the cloud for global scale, all using the same tools. The main footgun is underestimating the deep platform engineering investment needed to unify orchestration, data, and security across environments.
Read the original → developers.redhat.com
- #mlops
- #hybrid cloud
- #infrastructure
- #kubernetes
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