Hybrid AI Workflows: On-Prem NAS + Cloud AI Services
How to Use On-Prem NAS and Cloud Services to Build Hybrid AI Workflows
Artificial intelligence is now a key part of how businesses get value from data. AI workloads are growing quickly, from image recognition and predictive modeling to analytics and automation. At the same time, companies are more worried than ever about data privacy, latency, compliance, and the costs of using the cloud. Hybrid AI workflows solve these problems by combining on-premises NAS systems with cloud-based AI services. This lets businesses process data intelligently without giving up control.
Instead of having to choose between fully on-prem or fully cloud AI, hybrid architectures let businesses use both in smart ways. When you need them, you can get to advanced AI models and scalable compute resources from the cloud. Sensitive data can stay on your computer.
Why Hybrid AI Is Getting More Popular
A lot of AI projects fail when moving data becomes dangerous or inefficient. Sending big datasets to the cloud can cause latency, bandwidth costs, and regulatory issues, especially in fields that deal with private or proprietary information. But running all AI workloads on-premises can make it harder to scale and get to the latest AI models.
Hybrid AI workflows fix this problem by keeping raw and sensitive data on local servers and only using cloud AI services for training, inference, or advanced processing when they are needed. This method lowers risk, boosts performance, and keeps costs stable.
What On-Prem NAS Does in Hybrid AI
On-site NAS systems are the building blocks of hybrid AI workflows. They keep a lot of structured and unstructured data, like videos, images, logs, documents, and sensor data. Organizations keep full ownership and compliance control because the data stays on their own servers.
NAS platforms also let you quickly access preprocessing tasks like data cleaning, indexing, labeling, and aggregation. These steps are very important for AI accuracy, and they are often done over and over again. Keeping them close to the data source makes things work better and cuts down on cloud usage that isn’t needed.
How Cloud AI Services Work with Local Storage
Cloud AI platforms give you access to powerful tools that are hard or expensive to run on your own. Some of these are big language models, computer vision APIs, speech-to-text engines, and training environments that can grow.
In a hybrid workflow, cloud AI services only get the preprocessed data they need from NAS for certain tasks. Then, the NAS stores the results, which could be predictions, classifications, or insights. This reduces the amount of data that needs to be sent while still taking advantage of cloud innovation.
Common Uses for Hybrid AI
Hybrid AI workflows work best in places where there is a lot of data. Media companies use on-premises NAS to store raw video footage and cloud AI to find scenes or transcribe them. Manufacturing teams look at sensor data on their own and use cloud AI to make models for predictive maintenance. Healthcare and research organizations keep patient data on their own servers while using cloud AI to find patterns without giving away raw datasets.
The hybrid approach allows for faster insights across industries while still following rules and procedures.
A Look at Hybrid AI from a Synology Point of View
Synology NAS platforms are great for hybrid AI workflows because they are fast, can grow with your needs, and work well with other systems. Organizations can efficiently prepare and manage AI datasets with features like high-capacity storage pools, SSD caching, container support, and secure file services.
Synology systems can also connect to cloud services for syncing, backing up, and sharing only certain files. This makes it easier to set up workflows where AI-ready data is stored locally and can easily connect to AI platforms outside of the company. Built-in security features help make sure that only approved datasets are shared. This helps with compliance and data governance.
Safety, following the rules, and keeping costs down
Compared to cloud-only approaches, hybrid AI workflows lower risk by a lot. Sensitive data stays on-site, which lowers the risk of breaches and compliance violations. Only the necessary parts of the data are sent to the cloud, and they are often anonymized or grouped together.
Organizations don’t want to pay for cloud storage and egress fees all the time because it costs too much. Local NAS stores data for a long time, and cloud resources are only used when advanced AI processing is needed.
Creating Hybrid AI Architectures that Last
To make hybrid AI work, you need to plan the architecture carefully. Results are affected by storage performance, network bandwidth, security policies, and lifecycle management. AI workflows also need to be flexible so that businesses can change them as models, rules, and business needs change.
This is when it is important to have a lot of experience with infrastructure planning.
About the Epis Technology
Epis Technology helps businesses build, set up, and improve hybrid AI infrastructures that use both secure on-premises NAS and cloud AI services. Epis Technology makes sure that AI workflows are scalable, compliant, and cost-effective by using their extensive knowledge of Synology consulting, large storage solutions, data protection, and performance optimization.
Epis Technology helps businesses get the most out of AI while still being able to control their most important data. They do this through storage architecture, data governance, hybrid integration, and ongoing support.