Artificial Intelligence

All Flash Arrays and Your Artificial Intelligence Future

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.

Intelligent Data Management with Machine Learning and Artificial Intelligence

The next step of your digital transformation – Intelligent Data Management

Digital transformation projects are intended to help businesses improve efficiency by using data to drive strategic and operational decision making. But while efforts are focused on generating actionable insights, much less attention is being given to the underlying infrastructure. Or more specifically, the management of the infrastructure.

Which is why you need an Intelligent Data Management Strategy to support your digital transformation efforts.

Generating insights – and administrative headaches

Currently, Machine Learning (ML) capabilities are directed towards linking disparate data sets and extracting previously unknown insights. Similarly, Artificial Intelligence (AI) is turning those insights into action, accelerating decision-making, automating low-level tasks and flagging anomalous data for review by human operators.

ML and AI are helping to make sense of unstructured data. But at the same time, corporate computing environments are becoming increasingly complex. The exponential growth of data coupled with the use of a disparate set of hardware, applications and services is creating a data estate that requires a disproportionate amount of administrative intervention and oversight.

Under the current paradigm, data is easier to use but increasingly difficult to manage. Unless the administration can be simplified and automated, businesses will begin drowning in data again.

Widening the scope for ML and AI

An Intelligent Data Management Strategy seeks to apply ML and AI technologies to virtually any problem – including systems management. Some vendors, like HPE, are building these capabilities into their hardware stacks, creating an intelligent data platform.

Machine Learning can be used to establish a baseline for normal operations for instance. By monitoring network traffic, server activity, application usage and other variables, infrastructure gains an understanding of what “normal” looks like.

Using the insights generated by ML, AI can then be applied to solving common network management challenges. Where an excessive load is detected, AI can automatically offload processing to reserve servers – or even to the cloud. If a system begins generating suspicious network activity, AI will throttle bandwidth, or even disable the system, until an engineer can resolve the issue.

Automated actions are not limited to problems either. AI can be trained to take proactive steps to ensure the entire stack is performing optimally. This relieves systems engineers of another important but time-consuming responsibility and ensures infrastructure continues to deliver value.

Because AI can make these adjustments in real-time, administrators can focus on other strategic tasks. Automated detection and remediation are also much faster than a similar human response, helping to ensure the entire infrastructure stack is functioning optimally.

To avoid being overwhelmed by unmanageable system complexity in the near future, your business must consider how ML and AI can be applied. Your Intelligent Data Strategy needs to be rebalanced to consider infrastructure overheads alongside analytics and insights.

Contact us today to learn more about adding automation and intelligence to your data strategy – and what you will gain in the process.

Useful Links

White Paper: Why Organizations Need an Intelligent Data Strategy