MinIO's AIStor: Revolutionizing AI Infrastructure and Data Management

MinIO's AIStor simplifies AI infrastructure and data management - SiliconANGLE

Innovations in AI infrastructure and data storage: MinIO's AIStor The increasing demands of artificial intelligence and scalable AI infrastructure are driving innovation in enterprise technology. As businesses seek to manage ever-growing volumes of data, new tools are emerging to simplify access, organization and integration with AI-driven workflows. "It's an entirely new stage for MinIO that we are introducing this week, the AIStor ... because people associate MinIO with rock solid data store that scales at massive scale. Now we just make it a little bit better by actually introducing AI tools," said Daniel Valdivia (pictured), engineer at MinIO Inc. Valdivia spoke with theCUBE Research's Savannah Peterson and Rob Strechay at KubeCon + CloudNativeCon NA, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They explored the innovative features and possible impact of the company's latest breakthrough -- AIStor. (* Disclosure below.) MinIO's AIStor introduces features to simplify data access and organization. With the new prompting objects API, users can interact directly with stored files to extract insights. These capabilities eliminate the need for extensive expertise and make AI workflows more intuitive. "With [the] AIStor, give me some GPUs, and this is meant to run on-premise, on your own infrastructure, ... [and] deploy the AIStor. And now you can actually ask things," Valdivia said. "[You can ask] 'What's going on in this file?' And we can be like, 'OK, there's some personally identifiable information, or there is a picture of two cats hanging out.' You can automate all of this with your applications." The platform also addresses the complexities of managing large and diverse datasets, particularly in industries such as pharmaceuticals and biotech. These fields require robust tools to analyze and manage vast amounts of data critical to their operations. AIStor is designed to make these tasks more accessible, encouraging industries to embrace AI on their own terms. "The main advantage of us introducing prompt AI into the AIStor is that all these enterprises ... [they] don't really know. They're like, 'I'm a pharmaceutical [company]. I'm here to make people healthier, but I don't know anything about running AI infrastructure.' But now with the AIStor, it's so easy," Valdivia noted. "Just buy some GPUs, deploy [the] AIStor, [and] you're in business. Now you can actually start talking to your data." By leveraging Kubernetes, the AIStor empowers enterprises to repatriate data storage from cloud-based services to on-premises infrastructure. This shift reduces the reliance on SaaS providers and enables companies to take full control of their operations while maintaining simplicity and scalability. "The biggest trend that we've seen over the past 12 months is people repatriating from the cloud to on-premises," Valdivia said. "Kubernetes makes it trivial to just run all your applications today. We're going to give them that assurance. You can come back to on-premise and everything will be fine. You can do this. It's not that hard. " Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of KubeCon + CloudNativeCon NA:

MinIO AIStor simplifies enterprise AI data management solutions - SiliconANGLE

MinIO's mission to streamline AI infrastructure: Inside AIStor Artificial intelligence infrastructure increasingly focuses on streamlining AI data management solutions to meet enterprise demands. For software company MinIO Inc., this priority led to the creation of its AIStor, a commercial product designed to centralize data for AI applications, according to Anand Babu Periasamy (pictured), co-founder and chief executive officer of MinIO. Built on MinIO's open-source platform, AIStor represents a shift toward delivering enterprise-scale solutions for data-driven AI needs. "If you think AI, you're going to think data, and we need to be the data part," Periasamy said. "That part is the only commercial focus: Do one thing really, really well. That's the AIStor bet we are talking about. We are going to put all our muscle power behind this commercial product, and it's going to take off." Periasamy spoke with theCUBE Research's Rob Strechay and Savannah Peterson, as well as guest host Sanjeev Mohan, principal at SanjMo, at KubeCon + CloudNativeCon NA, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed MinIO's AIStor, the company's focus on simplifying AI infrastructure and how centralizing data advances enterprise AI. (Disclosure below.) MinIO's success lies in its broad adoption across diverse environments, from large-scale data centers to unconventional settings, such as 5G towers and home network-attached storage systems, positioning it as a leader in AI data management solutions, according to Periasamy. This widespread reach is part of MinIO's deliberate strategy to embed its technology wherever data storage needs arise, scaling to meet enterprises' demand for reliable, adaptable storage solutions. "We are the dominant player in this market by adoption, the largest player by numbers," Periasamy said. "But then, when it comes to business, you don't want to boil the ocean. You are better off winning one use case at a time. As the product matured into the commercial market, we started seeing where is the most powerful business use case." MinIO's AIStor, explicitly tailored for AI data management solutions, exemplifies the company's approach, according to Periasamy. It simplifies AI infrastructure while providing the performance essential for data-intensive applications, illustrating MinIO's commitment to integrating AI solutions directly into data environments. "We took those improvements ... and then created a commercial version of MinIO optimized for AI workloads, and that's AIStor," Periasamy said. "Every one of our customers is now restructuring their organization to put AI at the heart of their business." To further this mission, MinIO designed AIStor to embed AI capabilities directly within the storage platform, removing the inefficiencies of transferring data to separate AI systems, according to Periasamy. This integration keeps data accessible and actionable at the source, maximizing the value of AI investments. "Unless you put data at the heart of your business, bring all of your data from different teams and centralize and build an AI data repository, you're not going to have an AI practice," Periasamy said. "We actually brought in cool AI capabilities inside the product itself ... you can directly prompt the data and talk to the data." MinIO's commitment to making AI accessible lies in its ability to conceal complex infrastructure within simplified, familiar application programming interfaces, according to Periasamy. Features such as Remote Directory Memory Access support and promptObject allow developers to use MinIO's platform without specialized knowledge, keeping workflows streamlined. This approach enables organizations to maximize their AI infrastructure's efficiency and keep data-intensive systems running smoothly, according to Mohan. "Simplification is something that every client I talk to wants, and this is not just ... in object storage," Mohan said. "In a world where skills are so hard to get ... it's inevitable that we need to move into simplification." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of KubeCon + CloudNativeCon NA:

MinIO debuts object storage built for AI workloads - SiliconANGLE

MinIO Inc., developer of a high-performance, Kubernetes-native object store compatible with Amazon Web Services Inc.'s S3 service, today announced a version of its cloud storage service designed to support all types of artificial intelligence training data in one infrastructure. Object storage is a highly scalable method for storing various structured and unstructured data types distributed across multiple hardware devices. MinIO says that more than half of Fortune 500 companies use its service. Citing research that found that the top reasons organizations adopt object storage are to support AI initiatives and to provide public cloudlike performance and scalability, MinIO said its new AIStor is specifically tuned to workloads that require understanding the characteristics of the data being stored. The new service includes an S3 application program interface called promptObject that lets users manage unstructured objects like they converse with a large language model. PromptObject function calls can be combined with chained functions to address multiple objects simultaneously. For example, a user can query about abnormalities on a stored MRI scan without going through an LLM. This enables developers to expand the capabilities of applications without requiring domain-specific knowledge of retrieval-automated generation models or vector databases, simplifying AI application development. A private API compatible with Hugging Face Inc.'s repository of open-source AI models lets organizations create their own data and model repositories on a private cloud or in air-gapped environments without code changes, reducing the risk of data leakage. A redesigned console user interface for managing MinIO storage supports identity and access management, information lifecycle management, load balancing, firewall, security, caching and orchestration from a single pane of glass. The console also features a Kubernetes operator that further simplifies the management of large-scale data infrastructure across hundreds of servers and tens of thousands of drives. Support for S3 over remote direct memory access takes advantage of high-speed Ethernet networking by leveraging RDMA's low-latency, high-throughput capabilities to improve performance with reduced CPU usage. MinIO's survey of 656 information technology executives found that an average of 70% of their cloud-native storage is object form today and that the average will grow to 75% in two years. The survey found that object stores are the primary foundation for advanced analytics, AI model training and data lakes/lakehouses. Over half of respondents plan to build a data lakehouse on an object storage foundation within the next 12 months and 41% use or plan to use object stores to support AI workloads. In a blog post, MinIO said the survey results should shock makers of storage-area network/network-attached storage, which is valued for its low cost but doesn't provide the cloud-native features that are needed for AI development. "SAN/NAS technologies are ill-suited for the cloud-native world and you can't containerize an appliance," wrote Jonathan Symonds, the company's chief marketing officer.