Artificial intelligence (AI), machine learning, and deep learning are becoming a significant part of how businesses operate and use technology. Deep learning—only one small part of machine learning that’s concerned with algorithms based on the human brain—is expected to reach a value of $935 billion in the U.S. software market by 2025.
What else should you know about machine learning? Facebook is one impressive example of what can be done with machine learning. Facebook has 2.4 billion active users. There are 350 million photos uploaded to the popular social media platform each day, and 500,000 comments are posted every minute.
Even with a team of thousands, there’s no way for Facebook’s employees to pore through all this data and suggest relevant content or personalized ads, or even to search for inflammatory material or harmful posts on their own. They use machine learning and artificial intelligence to get the best possible outcome for both users and their bottom line.
Machine learning is also being used in all kinds of other industries, including:
- Law enforcement
But how do you access and enable this powerful tool? And how can machine learning impact the work you are already doing and maximize the way your systems perform? We’re taking a look at the mutually-beneficial relationship that’s developing between solid-state drives (SSDs) and machine learning.
About Machine Learning
Machine learning is no longer an emerging field; it’s a vibrant technology here to stay. It’s an approach to computing that gives our computers a way to learn without programming them. Using quality data and building machine learning models and personalized and curated algorithms, machine learning changes the way businesses handle large amounts of information.
What kinds of technology are powered through machine learning?
- Intelligent gaming
- Self-driving cars
- Dangerous work like bomb disposal and welding
- Environmental protection
- Better health care
- Financial advancements
- Home security and smart home technology
- Improved logistics and distribution
- Customized news and better market reports
Here’s what InfoStor has to say about machine learning and how this technology pairs with solid-state drives:
“Machine learning is revolutionizing the way technology is deployed in many different fields, but when it comes to machine learning in data storage systems, things have been a little less dramatic. Big data storage and storage tiering are two areas where machine learning in storage systems shows promise. Still, it’s in the area of solid-state drive (SSD) storage that machine learning may offer the biggest opportunities for improvement.”
SSDs have incredible endurance, which is only improved by the implementation of machine learning. And persistence isn’t the only factor that is enhanced by machine learning. It can increase data retention and optimize your SSD performance from the very beginning.
Improving Your Cluster SSD with Machine Learning
How do machine learning and solid-state drives play into one another? At first glance, it may seem that flash SSDs can benefit machine learning in your enterprise systems by supporting machine-based learning algorithms. Still, in actuality, the opposite can also be true. Machine learning can also improve the way your solid-state drive performs.
Machine learning techniques can cluster input-output accesses. With machine learning, you can store data in different blocks based on their access patterns. Offsets, or relative addresses and addresses, are clustered, and data is stored in physical blocks based on the frequency of writes. By separating “hot” and “cold” data based on how frequently it needs to be accessed in this way, machine learning can minimize your write amplification and improve your SSD’s performance.
When you incorporate machine learning into your cluster SSDs, you boost your overall quality of service.
Those in the tech world call this “garbage collection” or GC. Machine learning can help optimize storage for flash SSD devices. In flash devices, machine learning and GC relocate existing data and deletes data that’s stale or outdated. Garbage collection and machine learning track and learn the temporal trends of your input and output access to remove unnecessary data and reduce unwanted performance variance that you might otherwise see with SSDs.
Machine Learning, Solid-State Drives, and NVMe Flash
Solid-state drives can positively impact your machine learning capabilities similarly to how machine learning can benefit your flash SSD. But truthfully? It’s also about what kind of file system you are using and how your data is managed.
While flash storage is an increasingly common solution chosen by many organizations, Non-Volatile Memory express (NVMe) is quickly becoming the medium of choice, especially when applications need fast access to data. NVMe is a protocol for storage on solid-state drives that is highly scalable, feature-rich, and designed for a non-volatile user experience.
One frequent misconception is that artificial intelligence and machine-based learning require high input/output operations per second (IOPS) capability. While this may help to a degree, the most helpful way to improve performance is with a solution that can deal with randomized data—which is where NVMe can genuinely come in handy. It’s not just choosing an SSD to power your machine learning; it’s about choosing one that can do what you need it to do, and NVMe can help make that happen.
Clustering in Machine Learning
Clustering isn’t just for solid-state drives. It’s also a particular technique used for machine learning that focuses on unsupervised learning methods. This means your system draws datasets from input data without any labeled responses. It can help find meaningful structures, explain underlying processes, and groups like data together.
Machine learning and clustering can help sort unlabeled data based on data reduction, finding natural clusters and data types, and finding useful data classes.
Here’s why data clustering is so essential when it comes to machine learning. When tackling real-world data, it doesn’t typically come with predefined labels. Machine learning models can classify data accurately by pinpointing commonalities in the data features to sort and predict classes for your new data.
At VIOLIN Systems, our all-flash, NVMe solutions are the perfect place to build machine learning algorithms to power new technologies, optimize performance, and manage your data. Our clustered SSD solutions can transform your organization’s operations, especially when paired with machine learning. Ready to learn more? Contact us today!