MinIO | Object Storage is AI Storage

The AI Datalake software stack is built on high performance, S3 compatible object storage. MinIO is a pioneer in the space and everything from Tensorflow to Kubeflow is tightly integrated and will run “out of the box.” Learn more about what makes us special below.

AI Storage for Performance at Scale

MinIO delivers exceptional performance for model training and model serving by leveraging its distributed architecture and object storage capabilities. During model training, MinIO’s distributed setup allows for parallel data access and I/O operations, reducing latency and accelerating training times. For model serving, MinIO’s high-throughput data access ensures swift retrieval and deployment of data stored for AI models, and enables predictions with minimal latency. More importantly MinIO’s performance scales linearly from 100s of TBs to 100s of PBs and beyond. This optimizes the end-to-end AI workflow, enhancing both model development and serving, leading to more efficient AI workloads and increasingly responsive applications.

At the Heart of the AI Ecosystem

MinIO is the standard in S3 compatible object storage for AI workloads. That ubiquity means that the AI/ML ecosystem all integrates with MinIO. Don’t take our word for it, enter in your favorite framework and let Google provide you with the evidence.

The Scale Required for Training and Inference

Enterprises are constantly collecting and storing data for AI applications and large language models can use this data to retrain models for improved accuracy. The scalability of MinIO allows organizations to expand their storage capacity on-demand, ensuring smooth data access and high-performance computing, essential for the success of AI/ML applications.

Resilient (Fault Tolerant) AI Storage

MinIO allows organizations to store vast amounts of data, including training datasets, models, and intermediate results, in a fault-tolerant manner. This resiliency is essential for ML and AI storage because it ensures that data is always accessible, even in the event of hardware failures or system crashes. With MinIO’s distributed architecture and data replication capabilities, AI/ML workflows can operate seamlessly and continue to deliver accurate insights and predictions, enhancing the overall dependability of AI-driven applications.

Reliable (Always On) Storage for AI Workloads

MinIO’s active-active replication capabilities enable simultaneous access to data across multiple geographically distributed clusters. This is essential for AI/ML because it enhances data availability and performance. AI/ML workloads often involve teams collaborating globally and require low-latency access to data stored for AI model training and inference - ensuring that data can be accessed from the nearest cluster location, reducing latency. Additionally, it provides failover capabilities, delivering uninterrupted access to data even in the event of a cluster failure, which is critical for maintaining the reliability and continuity of AI/ML processes.

A Storage Solution for Large Language Models

MinIO can be seamlessly integrated with Large Language Models (LLMs) as a reliable and scalable storage solution for the massive amounts of data required by such models. Organizations can use MinIO storage for pre-trained LLMs, fine-tune datasets and other artifacts. This ensures easy access and retrieval during model training and model serving. The distributed nature of MinIO allows for parallel data access, reducing data transfer bottlenecks and accelerating LLM training and inference, enabling data scientists and developers to leverage the full potential of large language models for natural language processing tasks.

A Context Store for Retrieval Augmented Generation

MinIO can be utilized for Retrieval Augmented Generation (RAG) by acting as a high-performance object storage backend for AI models and datasets. In a RAG setup, MinIO can store a custom corpus used for creating domain-specific responses from a Large Language Model (LLM). An AI-enabled application can access the corpus and create context for the LLM. The result is more contextually relevant and accurate responses in natural language generation tasks, enhancing the overall quality of generated content.

The Cloud as an Operating Model - Starting with S3

MinIO adheres to the cloud operating model - containerization, orchestration, automation, APIs and S3 compatibility. This allows for seamless integration of AI storage across clouds and cloud types by providing a unified interface for storing and accessing data. Since most AI/ML frameworks and applications are designed to work with the S3 API, having the best compatibility in the industry matters. With more than 1.3 billion Docker pulls to its name - no object store has more developers and applications validating its compatibility - 24/7/365. This compatibility ensures that AI workloads can access and utilize data stored in MinIO object storage regardless of the underlying cloud infrastructure, promoting a flexible and agnostic approach to data management and processing across diverse cloud environments.

Storage for AI at the Edge

At the edge, network latency, data loss, and software bloat degrade performance. MinIO is the world’s fastest object store, is less than 100 MB for the binary and can be deployed on any hardware. Furthermore, features like MinIO Bucket Notifications and Object Lambda can be easily leveraged to build systems that can instantly run inference across new data as it is ingested. Whether it is object detection onboard a high-altitude drone or traffic trajectory prediction within an autonomous vehicle, MinIO's AI storage enables mission-critical applications to store and consume their data in a way that is fast, fault-tolerant, and simple.

Lifecycle Management for ML/AI Workloads

Modern AI/ML workloads require sophisticated lifecycle management. MinIO's lifecycle management capabilities automate data management tasks, optimizing storage efficiency and reducing operational overhead. With lifecycle policies, organizations can automatically move infrequently accessed AI data to lower-cost storage tiers, freeing up valuable resources for more critical and active workloads. These features ensure that AI/ML practitioners can focus on model training and development, while MinIO intelligently manages data, enhancing overall workflow performance and cost-effectiveness. Additionally, lifecycle management helps maintain data compliance by enforcing retention and deletion policies, ensuring AI/ML datasets adhere to regulatory requirements.

Object Retention for the AI/ML Workflow

Fewer workloads depend more on what-happened-when than AI/ML. MinIO solves this with advanced object retention capabilities that ensure the integrity and compliance of stored data over time. By enforcing retention policies, MinIO helps organizations maintain data consistency for AI/ML models and datasets, preventing accidental or unauthorized deletions or modifications. This feature is especially vital for data governance, regulatory compliance, and reproducibility of AI/ML experiments, as it guarantees that critical data remains accessible and unaltered for a specific duration, supporting accurate model training and analysis.

Data protection for core AI datasets

MinIO provides robust data protection for AI storage datasets through a number of different features. It supports erasure coding and site replication, ensuring data redundancy and fault tolerance to safeguard against hardware failures or data corruption. MinIO also allows for data encryption at rest and in transit, securing the data from unauthorized access. Additionally, MinIO’s support for identity and access management (IAM) allows organizations to control access to their data stored for AI workloads, ensuring that only authorized users or applications can access and modify the data. These comprehensive data protection mechanisms offered by MinIO help maintain the integrity, availability, and confidentiality of AI datasets throughout their lifecycle.