Explore the Machine
Learning Workloads
Driving AI Innovation

Explore the machine learning workloads that create artificial intelligence and understand the architectures that create efficient, scalable, and cost-effective AI data infrastructure systems.

Machine Learning Workloads

Not all AI workloads are the same—but they all need object storage.

Machine learning workloads are the engines that drive AI use cases. Whether you‘re building natural language processing models, image recognition systems, or recommendation engines, these processes depend on handling large*, distributed datasets efficiently. AI workloads are different. They need fast, performant, scalable storage that can ensure that data is readily available across the entire machine learning pipeline. Each stage in the workflow—training, fine-tuning, and serving models—demands robust storage solutions that integrate seamlessly with the hardware, software, and application stack.

*Our large is hundreds of petabytes to exabytes.

The most prominent AI workloads fall into four categories:

Model Training
Retrieval Augmented Generation (RAG)
LLM Fine-Tuning
Model Serving

Machine Learning Workloads

Model Training

Model training is the most resource-intensive step in building AI systems. In this phase, algorithms learn from massive amounts of data to develop predictive models. The training process requires access to large datasets that are almost guaranteed to exceed the available memory on any single machine.

Why it matters: The faster you can train models—the more experiments you can run when experimenting with hyperparameters and model architectures. The more experiments you can run—the more accurate your models will be. Model training needs a fast, distributed AI storage layer that ensures data is accessible for continuous processing without bottlenecks.

Challenges: Exascale datasets don’t fit into memory. They require distributed ecosystems that have ultra-high throughput and can constantly feed the GPUs. This is a capacity utilization problem from a business perspective and a software architecture problem from a technical perspective. This game is played with smart software and dumb hardware.

Solution: MinIO’s AIStor is purpose-built for high-performance, distributed machine learning workflows. That’s why most of the large private cloud AI deployments are built on MinIO, from full self-driving programs to threat detection.

Machine Learning Workloads

Retrieval Augmented Generation

Generative AI requires Retrieval Augmented Generation (RAG) to combine the knowledge in an organization’s custom corpus of documents with the knowledge trained into Large Language Models (LLMs). RAG powers LLMs used for customer support bots, medical AI applications, and legal document searches in a file system. It is a proven technique for greatly reducing hallucinations.

This workload includes:

Document Pipeline Subsystem

The Document Pipeline Subsystem is the backbone of the RAG workload. It is responsible for ingesting, preprocessing, indexing, and retrieving relevant documents destined for the embedding subsystem (or vector database). By using distributed, scalable storage systems like MinIO, the document pipeline can handle large-scale machine learning workloads efficiently, ensuring that LLMs have timely access to the big data they need to deliver high-quality, context-rich responses.

Embedding Subsystem

The Embedding Subsystem is at the heart of the RAG workload. It is responsible for populating a vector database by translating the unstructured text in the custom corpus into a format (embeddings) that can be semantically understood and retrieved in response to user queries. This subsystem enhances LLMs by enabling more intelligent and context-aware prompts, making it a key component of generative artificial intelligence. With scalable storage systems like MinIO, handling large custom corpuses and vast amounts of embeddings becomes easier, allowing enterprises to add the proprietary knowledge in their custom corpus to the knowledge trained into LLMs using RAG.

Prompt Engineering Subsystem

The Prompt Engineering Subsystem is a critical component in the RAG workload, bridging the gap between the embedding subsystem and the LLM. It uses the semantic search capabilities of the vector database to find snippets from an organization’s custom corpus that are relevant to user queries. By dynamically generating, refining, and optimizing prompts, this subsystem ensures that LLMs receive relevant, accurate, and contextually rich prompts.

MinIO’s object store supports the performance and scalability needs of this subsystem, enabling real-time prompt generation that allows an LLM to use an organization’s proprietary knowledge. This is critical to ecommerce, customer support, and even technical support use cases where automation can play a key role. Finally, in this subsystem, organizations should use an object store like MinIO to save the user query, generated prompt, and the LLM’s response. Users interacting with an LLM are having a conversation about your company or product - understanding this conversation is the best way to improve.

Machine Learning Workloads

LLM Fine-Tuning

Fine-tuning in the context of artificial intelligence models—especially for large language models (LLMs) or other machine learning models—refers to the process of adapting a pre-trained model to perform specific tasks using a domain-specific training dataset. This process adjusts the model’s weights and biases to generate more specialized or accurate outputs for a given task.

Fine-tuning relies on large, high-quality datasets that are ideal for high-performance object stores like MinIO. They provide the ability to store, access, and retrieve data at scale with low latency. Fine-tuning tasks might require real-time access to thousands or millions of training examples, especially when training on high-resolution images, long text corpora, or time-series data.

When training (or fine-tuning) a model, the speed at which data can be fed into the GPU or TPU clusters is a key factor that impacts training times. High-performance object storage is essential for ensuring high throughput, enabling large batches of data to be streamed to the compute nodes without minimal delay.

Data Parallelism

Training on large datasets is often done in a distributed fashion. In this context, the data needs to be available to multiple nodes, which might be running parallel tasks. MinIO’s distributed architecture makes this possible by allowing multiple nodes to read and write data simultaneously.
MinIO’s Role

MinIO’s object storage supports high-performance parallel I/O, ensuring that each GPU or TPU in the training cluster can access the data it needs as quickly as possible, optimizing the training loop.

Throughout the training process, the system periodically saves model checkpoints. These checkpoints allow software development teams to resume training from specific points and ensure that partial progress is not lost due to interruptions or downtime. Storing and retrieving these checkpoints requires high-performance object storage that can handle frequent, large file operations. This is very powerful as there are often hundreds of model checkpoints, and each are multi-gigabyte files. It’s essential to have these backups.

When fine-tuning LLMs or other large models, the volume of data being processed grows exponentially. High-performance object storage systems like MinIO ensure that even as the dataset size and model complexity increase, storage performance remains optimal.

Machine Learning Workloads

Model Serving

Once a model has been trained or fine-tuned, it is ready to be tested or deployed into production. The performance of the storage system directly impacts inference speed for LLMs using the RAG workload to incorporate an organization's proprietary knowledge. If a model is an LLM used for generative AI, then a storage solution like MinIO should be used to save the user’s query, the prompt, and the LLM's response. For traditional models, all inputs and outputs should be saved. By instrumenting all AI models in this fashion, organizations can ensure that engineering teams have the data they need to make future versions of artificial intelligence models more accurate.

Key Components of Model Serving:

Model Deployment

After fine-tuning or training, the model is packaged and deployed to a serving platform (e.g., cloud, on-premises, or edge environments). Deployment platforms like Kubernetes or Docker containers are often used to ensure the model can scale horizontally and handle different machine learning workloads. High-performance object storage like MinIO is essential for storing both model artifacts (such as weights, checkpoints, and versions) and the associated data. The model needs to access data quickly and frequently in real time to make predictions, and object storage ensures that these demands are met.

Data Ingestion for Inference

In production, the model processes incoming data (e.g., user input, transaction data, or sensor readings) and returns predictions in real-time. This data is usually ingested through APIs or message queues like Kafka. Model serving platforms like TensorFlow Serving, TorchServe, or Seldon Core manage the interaction between the incoming data and the model. When unstructured data for inference is stored in a high-performance object storage system like MinIO, it ensures fast, distributed access. This technology is particularly important for applications like recommendation engines, where real-time data processing is critical.

Scalability and Load Balancing

Model serving must scale to handle varying levels of user demand, from a few requests per second to thousands or millions. Load balancers distribute the prediction requests across multiple instances of the model, ensuring that each instance has sufficient resources to handle its AI workload. MinIO’s distributed architecture allows data storage to scale horizontally, ensuring that no single node becomes a bottleneck for data access. This is critical in model-serving scenarios where data demands grow with increased traffic and more complex models.

Latency Optimization

Low latency is key in scenarios like real-time fraud detection or recommendation engines, where users expect immediate responses. Minimizing the time between a model receiving input and delivering output is essential for maintaining a smooth user experience. MinIO ensures low-latency data retrieval, making it possible for models to access large datasets or model checkpoints without unnecessary delays. Its ability to serve data at high throughput rates ensures real-time inference can proceed without data storage becoming a bottleneck.

Model Versioning and Rollbacks

In production environments, multiple versions of a model may be running simultaneously (e.g., A/B testing or canary deployments). This allows the system to test and compare the performance of different models and easily switch between versions. MinIO’s versioning capabilities allow models to be updated seamlessly while keeping previous versions accessible. This enables easy rollback to older model versions if a new deployment introduces errors or underperforms.

Monitoring and Logging

Once a model is in production, it must be monitored for performance (latency, throughput) and correctness (prediction accuracy, false positives/negatives). Monitoring tools track these metrics and log events to diagnose issues, allowing engineers to troubleshoot problems in real-time. MinIO provides a scalable and cost-effective solution for storing large volumes of log and monitoring data. Its object storage architecture allows you to store and retrieve logs easily, making it ideal for archiving monitoring data over time.

Security and Privacy

Many model-serving applications deal with sensitive data (e.g., personal health information or financial data). Ensuring data security and maintaining privacy is critical, especially in industries with strict regulations (e.g., HIPAA in healthcare, GDPR in Europe). MinIO provides object-level encryption and strong access control mechanisms, ensuring that sensitive data remains protected during both data storage and retrieval. This makes it easier to comply with privacy regulations while serving AI models in production.

Inference as a Service (IaaS)

Model serving is increasingly offered as a service (Inference as a Service), where businesses deploy models to the cloud (public or private) and allow users or systems to make API calls for real-time predictions. This service-based approach requires a robust infrastructure for data management, model serving, and API handling. MinIO’s cloud-native object storage is an ideal solution for IaaS platforms. It provides the high-performance, scalable storage needed to manage large amounts of inference data and model artifacts in distributed cloud environments.

Model serving is the critical final step in the machine learning pipeline, where predictions are delivered to end users in real-time or near real-time. Efficient model serving requires scalable, high-performance infrastructure to handle large datasets, rapid inference, and model versioning. MinIO’s high-performance object storage plays a key role in ensuring that these machine learning workloads can be supported efficiently, providing the storage backbone for data retrieval, logging, and model management across various use cases and industries.

Conclusion

AI workloads—whether focused on model training, fine-tuning, or serving—are the backbone of modern AI applications. Each of these machine learning workloads relies on fast, distributed, and scalable storage to manage large datasets effectively. As artificial intelligence continues to evolve, data becomes even more critical, and MinIO provides the infrastructure that makes AI workloads efficient and scalable.