Artificial intelligence (AI) is the concept of training computers to perform tasks as well as or better than humans can. Machine learning (ML) is essentially a type of AI that uses algorithms to “learn” from a set of data and draw conclusions or make predictions from it – without the need for humans to write code that helps it along.

It takes huge amounts of data to “train” AI and ML applications to get meaningful results. An image recognition application, for example, needs to see “hundreds of thousands, even millions of images” before it can perform reliably, according to the GPU hardware provider NVIDIA.

Given those sort of requirements, without a cost-effective data storage strategy, AI and ML projects may never get off the ground. Wasabi offers just such a strategy, at costs of only a fraction of alternatives including AWS S3, Azure Blob Storage and Google Cloud Platform’s Cloud Storage.

Human Genome Sequencing
Human genome sequencing is opening up new opportunities for highly individualized medicine. With sequencing data in hand, healthcare professionals can accurately predict which medications will be most effective for a particular patient – and which won’t.

But a single human genome takes up 100 GB to 200 GB of storage space, depending on which source you prefer. By 2025, an estimated 40 exabytes of storage capacity will be required for human genome data. Not knowing when a given patient’s genome data will be needed, healthcare providers can’t simply relegate inactive data to tape or other second- or third-tier options. They need to know they can get at any patient’s data when they need it.

With its single-tier, hot cloud storage approach, Wasabi provides the answer. All customer data is always readily available, at prices more than four times less than competing cloud storage solutions like AWS. What’s more, it’s highly secure, as all Wasabi data is encrypted, enabling providers to comply with federal HIPAA (Health Insurance Portability and Accountability Act) and HITECH (Health Information Technology for Economic and Clinical Health Act) regulations. And it’s infinitely scalable, able to handle data from as many patients as needed.

Industrial IoT – from Aircraft Engines to Wind Turbines
Imagine if you could get 5% more power out of every turbine in a wind farm while lowering maintenance costs by 20%. That’s what GE is doing by applying AI to vast quantities of data collected from turbines and combining it with weather prediction data. The data paints a picture of how slight changes in turbine blade positioning can deliver improved power generation performance. AI also enables predictive maintenance capabilities, while weather data informs as to the best time to perform maintenance on individual turbines.

This is just one example of how the Industrial Internet of Things (IIoT) is enabling companies to improve performance while saving money. But it’s only possible if AI/ML applications have vast amounts of historical data to examine. Consider that a twin-engine Boeing 737 aircraft generates 333 GB of data per minute per engine. An IoT-ready oil and gas drilling rig produces 7 to 8 TB of operational data a day. Connected automobiles can generate more than 1 PB of operational data each day.

The issue then becomes how best to securely and cost-effectively store that data, which is where Wasabi comes in. At a cost of one-fifth that of competitors like AWS, and with speeds faster than the competition, Wasabi is a high-performance, affordable alternative to both premises-based storage and first generation cloud providers. And Wasabi’s policy of encrypting data both at rest and in transit ensures that data is always protected.

Video and Image Analysis for Law Enforcement
Video and image analysis have long been used in the law enforcement community to obtain evidence in criminal investigations. AI/ML applications make analysis of such images, including facial recognition, far faster, easier, and more accurate – because computers don’t tire like people do, and aren’t subject to human error.

With more and more departments outfitting officers with body cams, the amount of data law enforcement is producing is growing by leaps and bounds. Nearly all large law enforcement agencies (250+ officers) have either already bought or are studying the use of body cams, according to a study by the Police Executive Research Forum. And one of their top challenges is storing all the resulting data, with some paying as much as $4 million per year for storage.

About two-thirds of the large agencies surveyed are turning to the cloud to store all that data. Wasabi hot cloud storage can store data at 80% less than the cost of competitors like AWS. Because it’s a single-tier platform, data is always accessible whenever it may be needed for analysis, at speeds faster than competitors. And it’s encrypted both at rest and in motion, providing the kind of security law enforcement demands.