Whether consumers realize it or not, machine learning is a significant part of our everyday lives. There are all kinds of current and upcoming applications that utilize the power of machine learning, including:
- Self-driving cars.
- Personalized news feeds and social media feeds.
- Speech recognition technology.
- Data security, including antivirus programs and anti-malware programs.
- Online search tools.
- Spam filters.
As technology continues to grow and evolve, machine learning will increasingly become a huge component of our daily lives. It’s even used to predict the making of superior wines!
Machine learning can make a huge difference in how your organization does business but only if it’s reliable, fast, cost-efficient, and straightforward. Many organizations are pairing machine learning with structured query language (SQL) servers to make this happen. Here’s why this is such a perfect combination:
The Specifics of Machine Learning
We hear lots of talk about artificial intelligence and machine learning. Machine learning refers to the construction of algorithms that can learn from data and use this data to make predictions. It’s closely aligned with computational statistics and prediction making as well as mathematical optimization of data.
The algorithms created through machine learning build a model through example inputs to make data-driven decisions.
Essentially, machine-learning algorithms use the statistics they collect from massive amounts of data. You can find patterns in things like numbers, words, images, or clicks through this data. Machine learning powers things like recommendations to discover your new favorite show on Netflix or Hulu or a new artist on your Spotify account—or who pops up on your Instagram feed.
With any of these platforms, their server collects all the data they can, then using machine learning to make data-driven decisions about what you might like to see next. However, machine learning is being used; it requires an incredible amount of data to happen.
Machine learning also has applications in the business world, where each institution has petabytes of data regarding transactions, customers, bills, and more. With all this data, the financial industry is a perfect fit for machine learning. Other industries making the most of this invaluable technology?
- Oil and gas
There’s no end in sight for the uses of machine learning. Finding a server and a database to handle it all with speed and reliability is increasingly vital to the success of organizations worldwide.
About SQL Servers
A SQL server is a relational database management system. Its use is becoming more and more prevalent in corporate and information technology environments thanks to its many services across various applications, from transaction processing to business intelligence and analytics—all critical aspects of machine learning.
SQL servers are built using SQL, or structured query language. These servers store and retrieve data needed for other applications, run using the same or a different computer. SQL servers use a robust database with a built-in configuration so that organizations can make the most of their data with effortless aggregation for a complete view of their data and their system.
The shift to SQL servers is happening for several reasons. Here are the benefits to using a SQL server:
- Scalability, so that no matter the size of your growing database, data is always available
- Security, with administrators having the power to give individual users secure, custom access
- Reliability, with impressive uptime and unparalleled recovery even in the event of an outage
- Data analysis, so you can access and analyze information instantaneously
If you need a simple, easy way to manage machine learning, look no further than SQL servers.
Machine Learning and Data: How SQL Servers Can Help
With previous database systems, machine learning had to take place outside of the database. What did this older machine learning process look like without an SQL server?
- Data was sent from the application to the database.
- Data would then have to travel from the database to the analytics server to go through model training and data transformations.
- Once this has happened, the data would have to be sent to a separate service or embedded logic, where it was scored to make predictions.
- Finally, this prediction would be sent back to the application.
There are many steps to take to use the power of machine learning, with lots of room for latencies, failures, and other issues, which limits the capabilities of what machine learning can do for an organization.
SQL server creators like Microsoft implemented machine learning into their database to solve this problem, creating a simplified, one-stop-shop where data can be stored and analyzed for ultra-high performance without all the extra steps. What does this look like?
With SQL servers like Microsoft’s MS-SQL, the process of enabling machine learning is a much more straightforward one: With SQL server-based machine learning, data is sent to a SQL server where data transformations, model training, and scoring all happen in the same place to make predictions for use within the organization’s application.
Essentially, the most significant benefit of powering machine learning through a SQL server is that data doesn’t have to be moved from place to place. It’s a more streamlined process that gives you faster—and, therefore, more accurate and more useful—data.
Using SQL servers, you can leverage robust data analysis and run the algorithms you need for machine learning without having to send data elsewhere for predictions and scores. Without SQL servers, machine learning can quickly turn into an expensive operation, especially when you are consuming and analyzing large volumes of data.
What does this mean? SQL servers bring the once unattainable, out-of-budget power of machine learning to organizations that never before had access to these kinds of database tools. And? They’re making it fast, scalable, secure, and reliable.
SQL Servers, Machine Learning and All-Flash Arrays
If you’re intrigued by the possibility of what machine learning can do for you, and you see the appeal of pairing machine learning with SQL servers, there’s no better way to power your system than through an all-flash array.
All-flash arrays are the platforms that power SQL servers. And in our opinion? They enhance everything that makes a SQL server great:
- They improve reliability and performance without the latencies of older legacy systems, even when you have a great deal of data.
- They simplify data management, make performance tuning a thing of the past, and store everything in a single volume for a lightweight, easy-to-maintain infrastructure.
- They support all kinds of other incredible features, like server and desktop virtualization, unstructured data storage, etc.
- They allow you to test data for machine learning while systems are up and running without impacting performance.
For peak performance of machine learning and SQL servers, you need the capabilities of an all-flash array. At VIOLIN Systems, we’re well versed in powering SQL servers for machine learning and AI to maximize your processes and give you smart solutions to run your organization.
We’re standing by, ready to show you what all-flash arrays, SQL servers, and machine learning can do. Contact us today to learn more!