Machine learning is advancing in a broad range. Here are a few applications proposed today (listed without any particular order):
Object identification techniques can answer whether a piece of fruit in a photo is a banana or an apple. Self-driving cars will know where to maneuver and when to stop for a pedestrian on the road. Linguistic cues can determine when the word "book "in a sentence represents a paperback novel or securing a hotel reservation. Between auto-labelling emails as “spam” or generating closed captions for an online video, machine-learning powers many operations we depend on every day.
Even more of our daily operations will depend on a systems integrity, so here’s how to improve reliability and performance with SSDs.
Machine learning (ML) is an automatic process that "teaches" a computer system on how to make insights and predictions based on data. As if learning by experience, ML computer algorithms differ from traditional software due to its independence from humans writing out every coded instruction. The machine-learning model will receive large amounts of data which enables the system to "acquire knowledge and skills" to navigate or interpret meaning on its own (such as identifying nuances for the word "book").
However, let's examine the bigger picture, ML techniques surpass far beyond word recognition. Machine-learning matters because the growing benefits include:
- Detecting credit card fraud quicker and responding automatically to protect personal identities.
- Recommending products and services relevant to your consumers based on their browsing habits.
- Listening to what public sentiment believes about your company on social media.
- Already managing aspects of financial systems.
- Assisting health care data and medical research.
- Administrating to government agencies and services.
- Sponsoring retail companies and the digital economy.
- Managing oil and gas mining operations.
- Networking transportation systems and supply-chain logistics.
- More ideas are expected to emerge as ML benefactors too.
Of course, to achieve the growing business benefits of ML will require much more processing power than legacy hardware. For those looking to implement machine learning capabilities into their enterprise, here's how you can support this kind of technology with room to grow: You need solid-state drives (SSDs) for machine learning.
Let's explore some technical requirements necessary to employ machine learning.
To Create an Excellent Machine Learning System
Understandably, machine learning is becoming increasingly popular with many more organizations that couldn't previously afford it. To manage exponentially growing volumes and varieties of data available with machine learning is a real option when computational processing and data storage are more accessible and affordable.
Data-driven decisions through machine learning will mean organizations can create models that can analyze vast amounts of data on an elaborately complex scale with fast and accurate results. ML means helping companies determine which opportunities might be profitable or pose significant risks, all based on existing data.
But machine learning models need certain things to make this happen, including:
- Data preparation.
- Basic and advanced algorithms.
- Automation processes.
- Ensemble modeling.
These requirements call for a high level of performance unique to an SSD storage solution.
SSD is the Right Option for Machine Learning Models
Data is the primary reason driving SSD implementation. Machine learning and artificial intelligence (AI) relies on consuming large volumes of data and synthesizing insights. Our decisions will depend on a system that can process it all.
However, dataset growth is off the charts since the widespread adoption of machine learning. So much so, humans are estimated to create 2.5 quintillion bytes of data each day. Speculations exist that sometime within 2020, the amount of data within our digital universe will reach 44 zettabytes —that's 44 trillion gigabytes. Since large numbers are so difficult to imagine, like a million or let alone a trillion, check out what a trillion dollars actually looks like and multiply that picture 44 times —anything to comprehend the massive scale of data is sobering.
Big data will continue expanding at an unprecedented rate. Our digital universe will grow 40 percent each year, and all of this data has to go someplace! However, by adding machine learning as a factor, traditional storage methods are unequipping organizations for successful encounters within our digital economy. All-flash NVMe SSDs can store large volumes of machine learning data, predict future capacity accurately, and readily match performance requirements. An SSD solution optimizes virtual memory placement on its own to save space for more data and more machine learning.
Choosing an SSD Solution Comes Down to Storage and Performance
When you're employing ML or AI models within your enterprise, why choose an all-flash NVMe SSD solution? Here's how SSDs will power your ability to do all the things worth accomplishing with machine-learning applications today.
As self-driving car technology continue leveraging ML models, Tesla is amassing 1.3 billion miles of driving data on diverse road conditions. Even without a sense of scale, tons of data do need to be stored and analyzed. Flash SSDs have high availability, low latency, and high throughput required to manage all of this data and run the ML models.
Examine the performance of legacy disk arrays: you'll discover higher latencies can drag your rate one thousand times slower. Traditional disk arrays represent longer wait times for users. In contrast, SSDs offer seemingly instantaneous results by boasting the lowest latencies available (measuring rates in microseconds). Disks must spin and rotate, requiring far more time to retrieve the same data that's instantly available in an SSD; hence, the "flash" term.
An all-flash system is unrestrained by the same limits that would impose slower processing speeds. Older storage methods will increase delay times when you're sending through larger quantities of data workloads. All-flash arrays remove bottlenecks to give you the maximum performance capabilities for machine learning.
Additionally, all-flash NVMe SSD arrays are much denser than rotating disks, capable of storing even more data in a fraction of the space, and require less power to maintain it. Imagine storing an entire petabyte of data in multiple SSD drives in a single rack, consuming less electricity and cooling costs, and taking up much less real estate than older legacy solutions like rotating disk drives.
The choice becomes obvious when storage and performance matters. Set up machine learning on a legacy drive and you might as well give Van Gogh a box of eight crayons—SSDs are all the paints in the store! The VIOLIN QV2020 Array is a top-notch SSD option that affordably empowers organizations like yours to be more competitive with ML.
You might be able to finagle machine learning models on older infrastructure like a hard disk drive, but the question is, "Are my systems three years or older?" Machine learning is an incredible technology that can deliver unbelievable results, but only if it's unlimited by the drive's capabilities.
Explore what machine-learning can do for you with the power of all-flash, solid-state drives by contacting VIOLIN Systems. Talk to an expert to determine the best solution for your specific ML and AI application. Let us know if you work with a preferred IT partner, so we can build together the right system to deliver on your goals.