Artificial Intelligence & Machine Learning

AI/ML projects are poised for takeoff, do you have cost-effective data storage supporting you?


Artificial Intelligence & Machine Learning

AI/ML projects are poised for takeoff, do you have cost-effective data storage supporting you?

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. OSS 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.

AI/ML: Full of promise and challenges

AI/ML applications, along with the fast rising technique of “deep learning,” hold tremendous promise in practically any industry. Whether it’s parsing the human genome to find the most effective drug for a specific patient, fine-tuning operations in a manufacturing plant to save time and resources, or performing predictive maintenance on all manner of machines, these technologies are poised to help organizations extract great value from their data.

And the time is right for AI/ML projects to take off. Increasing amounts of compute power are now readily available from graphics processing units (GPUs), both on-premises and in the cloud. Advances in sensor technology, such as for Internet of Things (IoT) applications, are driving the creation of the massive data sets that AI/ML applications require.

But this very same data presents challenges when it comes to storage. AI/ML projects can be pricey undertakings, from the tools required (hardware, software, custom code) to the required expertise, which can be even harder to come by. ( has nearly 10,000 open AI-related jobs and some 25,000 for ML.) Companies need cost-effective storage solutions to help ensure the projects don’t break the bank.

That’s where OSS comes in. With prices that are 80% less expensive than AWS S3, and performance that’s faster than the competition, OSS hot cloud storage is the perfect fit for AI/ML applications. What’s more, OSS is secure, infinitely scalable and highly reliable, delivering eleven nines (99.999999999%) of object durability over a given year.


You need lots of data to fuel AI/ML projects; don’t spend 5x more to store it than you have to.


Faster performance means more time modeling and less time waiting for your data.


Protect data objects from loss with the cloud’s first immutable data storage, with 11 nines durability – you’ll never lose a file again.

Sample Industry Use Cases for AI/ML

Life Sciences
Whether it’s mining data from human genome sequencing, examining X-ray, CT and MRI images, predicting patient issues and outcomes or developing new drugs, there’s no shortage of AI/ML applications in the healthcare and life sciences industry.

Predictive maintenance for energy and utility company equipment, bringing efficiency and stability to the grid, natural resource exploration, and monitoring energy consumption behavior are just a few of the ways energy companies are applying AI/ML technology.

Law Enforcement
AI/ML solutions can help law enforcement agencies with tasks including automated video and image analysis, detecting and responding to traffic safety violations and accidents, predicting crime levels to help with resource allocation, DNA analysis, and more.

OSS in Action: AI/ML Use Cases

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, OSS 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 OSS 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 OSS comes in. At a cost of one-fifth that of competitors like AWS, and with speeds faster than the competition, OSS is a high-performance, affordable alternative to both premises-based storage and first generation cloud providers. And OSS 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. OSS 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.

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