Artificial Intelligence (AI) is set to become an increasingly important aspect of your operations in the near future. Indeed, AI will help to automate and accelerate many of your core business operations.
However, the choice of technology will also play an important role in ensuring AI delivers on expectations. Here are some factors to consider moving forwards.
Machine Learning, model training and storage
Building and training models takes time, usually because the algorithms need to process vast amounts of data for pattern analysis. Google’s famous Deepmind cancer detection model was trained using images and health records from more than 90,000 patients .
While developing a machine learning model, the primary focus is the accuracy of the results. This means that speed can take a back seat while the data science team tweaks and refines algorithms and verifies that all is working within expected parameters.
During development, low-cost cloud storage or higher latency spinning disk arrays will (normally) be adequate.
Production AI and speed
Once the model moves into production the choice of storage becomes much more important. As well as improving the accuracy of decision-making, AI is intended to accelerate outcomes by performing calculations and inferences more quickly than a human.
This is particularly true when dealing with real-time calculations. In these deployments, speed of storage (and the rest of the infrastructure) will be critical. All-flash storage is the only viable technology available that reduces latency, both at the edge and the core.
Ultimately production Artificial Intelligence systems must be designed to improve data flow at every point of the lifecycle – with the exception of archived records that are retained for auditing or compliance purposes. This information can be stored on lower-performing hardware, like spinning disk arrays or cloud archives to help reduce costs.
AI at the edge
One of the most challenging aspects of AI design is performance at the edge. What needs to be processed and auctioned at the edge, what needs to be passed to the core for action, what can be redirected immediately to the cloud for cold storage?
NetApp Artificial Intelligence solutions have been developed to address these questions, using a tiered approach to data service that automatically directs incoming data to the correct location. Through the use of edge-level analytics, data is processed and categorised in such a way that movement is accelerated, eliminating bottlenecks for the fastest, most efficient AI operations.
For existing NetApp customers, the ability to provision and manage AI storage and infrastructure using familiar ONTAP tools. This allows your business to accelerate deployments and reduce the learning curve so your data science team can focus all their efforts on building a model that delivers true business value.
To learn more about NetApp Artificial Intelligence solutions and how they will help your business meet the challenges of the data-driven future, please get in touch.