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NetApp Integration Solution

A Comprehensive MLOps, DevOps, and DataOps solution for data scientists and ML teams

Increase productivity with an industry-first solution for ML dataset caching

With and NetApp joint solution, datasets are ready to be used in seconds rather than hours. The solution improves sharing and collaboration for data science teams with’s cached dataset capability that can be authorized and used by multiple teams in the same compute cluster connected to the NetApp cached data. Organizations using NetApp and are able to reduce cost as machine learning models pull datasets from the cache, and not from remote storage, which may require payments per download. As a hybrid cloud solution dataset caching can present the on-premises mirror storage for the data lake residing in the cloud.

Accelerate ML workflows with automated MLOps, DevOps, and DataOps

Automate your machine learning Kubernetes infrastructure with a unique architecture that allows IT and engineers to attach different compute resources to one unified control plane, and manage ML jobs across all resources. Attach multiple on-premises Kubernetes clusters, VM servers, and cloud accounts and run ML workloads on all resources. 

Scale with hybrid machine learning
"Not only does offer dynamic dataset caching at scale, but it also delivers a comprehensive AI OS for hybrid cloud data teams all the way from data to modeling to production. NetApp customers benefit from's unique container-based architecture with integrated Kubernetes, and resource management systems enabling teams to utilize all their resources including NetApp's cloud connected high performance storage . We're excited to partner with in providing a unified data fabric that increases productivity and reduces technical complexity and cost for enterprise data teams. Together with we bring enterprise level AI through ONTAP AI and the rest of NetApp AI offerings from Edge to Core to Cloud”
Hoseb Dermanilian
Hoseb Dermanillian
Sr. Manager, WW AI Business Development, NetApp