• Choose the Right NAS:
  Select the ASUSTOR Lockerstor Gen3 series, which is well-suited for AI development due to its powerful hardware.
• Install AI Development Software:
  Use Docker on ASUSTOR NAS to install commonly used AI development tools such as TensorFlow.
• Data Preparation:
  Organize company data and upload it to an ASUSTOR NAS for easy access during development.
• Model Training:
  Train AI models within a virtual environment on the NAS, as well as evaluate model performance.
• Model Deployment:
  Deploy trained models to production environment to provide AI services for enterprises.

Leading cloud service providers offering AI model training include Google Generative AI, AWS SageMaker, and Microsoft Azure AI. AWS, in particular, is renowned for its extensive options and flexibility, providing users with a wide variety of AI services and tools to meet their training needs.

By installing ASUSTOR Cloud Backup Center, AI training materials, such as images and text, can be easily uploaded to popular cloud storage services like Amazon S3, Microsoft Azure Blob Storage, or Google Cloud Storage as data sources for AI model training.

Cloud Backup Center supports the continuous input of large amounts of new training data into AI models during training, helping improve the accuracy of predictions on unseen data. Using an ASUSTOR NAS as a central hub for training data collaboration or as a caching center for data iteration ensures low latency and real-time access to large datasets, as well as efficient management, collaboration, and RAG data cleaning processes.

In AI training, large datasets can often become a bottleneck. Use your ASUSTOR NAS device for your edge storage and caching center needs to make it easy in establishing an internal collaborative work environment. The cross-platform access features of an ASUSTOR NAS allows employees to access and edit centrally managed data from anywhere, preventing network slowdowns from simultaneous large dataset downloads. Remote employees can edit data directly online via NAS, avoiding the need to transfer massive datasets using external drives. Once collaboration is complete, the data can be uploaded to the cloud through scheduled tasks on an ASUSTOR NAS.

RAG (Retrieval-Augmented Generation) is a technique that uses external information to enhance the capabilities of AI generative models. In RAG, data quality directly impacts a model’s performance. Effective data cleaning, a complex yet critical process that combines both manual and automated methods, significantly improves the performance of RAG models by generating more accurate and useful content.

Before sending data to platforms like SageMaker or AWS S3, training data needs to be prepared. This typically involves data preparation, cleaning, transformation, feature engineering, splitting, and uploading, all of which require collaborative processing. ASUSTOR NAS devices play a critical role in coordinating this process, allowing for collaboration on data cleaning before uploading it to the cloud.