AI workloads are evolving fast-from traditional ML pipelines to complex generative models. But one thing remains constant: data growth. And the ability to store, access, and manage massive volumes of unstructured data efficiently is now a critical architectural decision.
In our latest IBM blog, we unpack why Object Storage is the backbone of scalable AI-and how it fits into modern data lakehouse and AI stack designs.
🔍 Key takeaways for technical teams:
- Native support for unstructured data at petabyte scale
- Seamless integration with query engines like Presto, Spark, and watsonx.data
- Tiered storage for cost optimization without sacrificing performance
- Immutable data retention and quantum-safe encryption for compliance
- Proven 2x price-performance gains vs. traditional cloud storage
Whether you're building out a lakehouse, optimizing AI pipelines, or architecting for multi-cloud, this blog is a must-read.
📘 Read the full playbook: The object storage playbook for scalable AI: Use cases and best practices | IBM
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Danielle Kingberg
Sr. GTM Product Manager, IBM Cloud Object Storage
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