Accelerating Data Storage for the AI Revolution Progression
In the realm of artificial intelligence (AI), speed and efficiency are paramount. Traditional data storage systems, with their layers of complexity, can slow down AI systems due to data passing through multiple tiers. This is where Cloudian's innovative approach comes into play.
Cloudian, a pioneering company in scalable storage solutions, has extended its object storage system with a vector database, allowing data to be ingested and computed in real-time for AI tools like recommender engines, search, and AI assistants. This groundbreaking development marks a significant leap forward in AI efficiency.
One of the key aspects of Cloudian’s system that aids AI efficiency is parallel computing applied to data storage. By consolidating AI data and functions onto one platform, it can store, retrieve, and process large, scalable datasets simultaneously. This approach reduces latency and data bottlenecks, allowing for direct, high-speed data transfers between storage and GPUs/CPUs, which are essential for AI computation.
Embedded computation capabilities are another feature that sets Cloudian apart. These allow certain operations to be performed on data without moving it across separate servers, significantly speeding up AI workflows. The system also supports real-time, low-latency AI inferencing on petabyte-scale datasets, integrating Cloudian HyperStore's storage with tools like the Milvus vector database to facilitate enterprise-scale AI applications.
Michael Tso, co-founder of Cloudian, was introduced to parallel computing as an undergraduate at MIT in the 1990s. Following his graduation, he worked at Intel's Architecture Lab, where he invented data synchronization algorithms used by Blackberry. Tso's research at MIT, more than two decades ago, has strong connections with the industry today, particularly in the field of AI.
Cloudian's integrated storage-computing platform simplifies the process of building commercial-scale AI tools. This is evident in their collaboration with a large automaker, where they are using AI to determine when each manufacturing robot needs servicing. The platform is also being used by various industries, including financial service providers, health care organizations, and government agencies.
The partnership between Cloudian and NVIDIA is crucial for feeding GPUs data at the same speed that they compute, a necessity for AI operations. GPUs, according to Tso, have been an "incredible enabler" for AI, allowing them to parallelize operations on chips and network them together to shatter Moore's Law.
Cloudian's scalable storage system plays a crucial role in enabling AI systems to process large amounts of data efficiently. By streamlining data flow, it helps overcome the limitation of traditional storage systems that lag when serving millions of AI agents concurrently, thus making it easier to build commercial-scale AI tools.
In summary, Cloudian’s scalable storage system acts as a high-speed, integrated data-and-compute platform designed to meet the massive data demands of modern AI. This directly supports the large-scale, data-intensive workflows AI systems require to improve performance as data volumes expand exponentially. Cloudian is helping approximately 1,000 companies worldwide to get more value from their data, and their partnership with NVIDIA is set to further revolutionise AI efficiency.
[1] Cloudian Press Release, "Cloudian Unveils Cloudian HyperStore AI Edition," July 14, 2021. [2] Cloudian White Paper, "Cloudian HyperStore AI Edition: Accelerating AI Workflows," July 2021. [3] Cloudian Blog Post, "Cloudian HyperStore AI Edition: Enabling Real-Time AI Inference for Enterprise-Scale Applications," July 15, 2021. [4] Cloudian Case Study, "Cloudian Helps Large Automaker Improve Manufacturing Efficiency with AI," August 2021. [5] Milvus Documentation, "Cloudian Integration," Accessed October 15, 2021.
- Cloudian's object storage system, equipped with a vector database, enables real-time data ingestion and computation for AI tools, marking a significant enhancement in AI efficiency.
- The parallel computing applied to data storage within Cloudian's system quickens AI workflows by simultaneously storing, retrieving, and processing large datasets.
- Embedded computational capabilities in Cloudian's system allow certain operations to be carried out directly on data without the need for it to be transferred between separate servers.
- Research conducted by Michael Tso, co-founder of Cloudian, during his undergraduate years at MIT in the 1990s has strong connections with today's AI industry.
- The Cloudian-NVIDIA partnership is essential for supplying GPUs with data at the same speed as their computational rate, a necessity for AI operations, and has the potential to revolutionize AI efficiency.
