Scalable Storage for AI Systems
Artificial intelligence (AI) and machine learning (ML) are revolutionizing industries, driving the next wave of business automation. These technologies heavily rely on vast amounts of data, putting significant demands on IT systems, especially storage infrastructure. Cloudian HyperStore offers the ideal storage for AI systems, providing a scalable, affordable, and fault-tolerant object storage platform that meets the unique requirements of AI workloads. With geo-distribution, you can locate Cloudian systems wherever needed. All flash and HDD-based options let choose the performance required for your workload.
Addressing Unstructured Data Challenges
AI, ML, and analytics environments require large volumes of unstructured data for training and processing. Cloudian HyperStore offers exabyte-scalability and parallel processing to deliver scalable throughput as demands grows. It serves as an optimal foundation for building both stream and batch AI pipelines, enabling organizations to efficiently leverage unstructured data for valuable insights.
Enhancing Performance and Compatibility
Cloudian HyperStore optimizes AI workloads by supporting popular machine learning frameworks like TensorFlow, PyTorch, and Spark ML. These frameworks are specifically designed for parallel training from object storage, providing improved performance and compatibility. Organizations can harness the power of GPUs without storage limitations, maximizing the utilization of expensive and high-demand resources.
Enabling Seamless Data Pipelines
Cloudian HyperStore serves as a central storage for various components of AI systems. It acts as a feature store, facilitating the registration and discovery of curated features. For vector databases like Milvus, Cloudian HyperStore provides scalable data infrastructure. Moreover, it supports the storage of machine learning models, ensuring model reproducibility, governance, and secure serving. With Cloudian HyperStore, organizations can streamline their data pipelines by leveraging a single storage system for different AI components.
Streamlining Data Management
Effective data management is crucial for successful AI applications. Cloudian HyperStore offers rich object metadata, object versioning, and object tags, allowing organizations to organize and catalog their data efficiently. HyperStore’s multi-tenancy feature enables multiple data scientists or teams to work simultaneously with the same data, fostering collaboration and accelerating AI workflows. Additionally, Cloudian HyperSearch simplifies data discovery, providing an easy-to-use interface for finding the needed data quickly.
NVIDIA GPU and Triton Support
NVIDIA’s powerful inference server, NVIDIA Triton™, supports S3-compatible storage, Cloudian HyperStore’s primary storage type. This means that organizations can harness the capabilities of NVIDIA GPUs for accelerated and scalable AI model deployment and execution, while also benefiting from the robust and scalable storage infrastructure provided by Cloudian HyperStore. By combining the power of NVIDIA AI Enterprise, including Triton, with the storage capabilities of Cloudian HyperStore, organizations can streamline the data science pipeline, expedite the development and deployment of AI models, and achieve efficient and reliable production AI.
Read about Cloudian’s support for NVIDIA DIGITS here.
Support for Multiple Types of Stores
Cloudian HyperStore may be deployed as a data platform for multiple types of stores, including feature stores, vector databases, and model stores, providing organizations with the necessary infrastructure to effectively manage curated features, conduct large-scale similarity searches, and securely store machine learning models within their AI systems.
- Feature Store: A feature store serves as a repository for curated features that are used in model training and inference pipelines. Cloudian HyperStore supports rich object metadata, enabling the storage of features in the metadata layer for faster online access. Additionally, offline feature storage can be facilitated using external tables, making Cloudian HyperStore an ideal solution for managing and storing features. The HyperSearch feature of HyperStore simplifies the management and discovery of features, enhancing the efficiency of feature utilization in AI systems.
- Vector Database: A vector database enables large-scale similarity search, including semantic search and cross-modality data analysis. Cloudian HyperStore can provide the data infrastructure for vector databases like Milvus. By leveraging HyperStore’s object storage capabilities, organizations can efficiently store and manage data embedded into a common vector space, empowering advanced search and analysis functionalities in AI systems.
- Model Store: A model store serves as a repository for machine learning models, storing model files, artifacts, and metadata. Cloudian HyperStore S3 buckets offer a secure and reliable storage solution for ML models. It provides essential features such as object versioning, IAM (Identity and Access Management), object lock, and tagging, ensuring proper storage, sharing, and protection of models. The HyperSearch feature further enables easy discovery and search of models, facilitating efficient model management in AI systems.
Protecting Valuable Data
Raw data is a valuable asset that cannot be easily replicated. Cloudian HyperStore ensures data protection through features such as data replication and erasure coding. It provides military-grade data security, object versioning, fault-tolerance, and data immutability, ensuring the integrity and durability of critical AI data assets.
Conclusion
Choosing the right storage for AI is paramount for the success of AI systems. Cloudian HyperStore emerges as the ideal solution, providing a scalable, affordable, and resilient data storage foundation. With its comprehensive data management capabilities, robust data protection features, and seamless integration with AI workflows, Cloudian HyperStore empowers organizations to store massive amounts of data, automate machine learning workflows, and unlock the full potential of their AI initiatives.
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