In-memory computing has incrementally improved various technology systems but one of the greatest services it has done is powering Artificial Intelligence and its subset Machine Learning. For a long time, ever since the term Artificial Intelligence was coined, it has been nothing but a buzzword in the tech industry.
Due to the maturation of in-memory computing, AI and Machine Learning have become a reality that is being used in real life each and every day. Here is how in-memory computing is powering and benefiting the development of machine learning applications ever since this integration was discovered.
Snapshot of in-memory computing
In-memory computing has been hailed as one of the most versatile yet highly functional data management and processing systems currently available. With in-memory computing, you can process huge amounts of data and be able to use the insights within mobile native applications without overburdening the smartphone. There are countless applications of in-memory computing, most of them tie in with machine learning processes.
Through this integration, in-memory computing can process the data and use various nodes for RAM to store the required insights for quicker access. Machine learning requires massive amounts of data and there is no better way to hasten the data processing process than using in-memory computing. Since in-memory computing is a cloud-based solution, integrating it with any machine learning-powered application can be easier.
Expediency of in-memory computing supports complex ML processes
Machine Learning applications are a cluster of complex processes that might require multiple disparate data sources at a time. If that is not the case, machine learning processes glean data from large data sets, which would take time to sort through manually. With in-memory data processing, processing that data and aggregating it from disparate sources, the process is expedited to a great extent.
Machine learning processes can then be executed faster and in real-time whenever needed because of the expedited processing timeframes. This technology can support a wide variety of Machine Learning processes, even real-time reporting applications. The versatility of in-memory computing makes this technology an asset in the AI/ML development field.
In-memory computing powers real-time analytical ML applications
Due to the expediency of in-memory computing, this technology can power real-time analytical machine learning applications. There is a wide variety of real-time analytical machine learning applications and some might include equipment servicing programs. These computer programs help companies with large fleets to determine the best time to service each vehicle.
The company can then service each vehicle at the exact right time, not too soon or late which saves revenue. In-memory computing helps process insights from all the sensors in each vehicle that collect data on the current state of critical vehicle parts. There are other real-time reporting applications that are powered by machine learning that use in-memory computing to provide timely insights.
Implementing HTAP systems with in-memory computing
One of the most notable deployments of in-memory computing in machine learning applications is the HTAP system. Hybrid Transactional/Analytical Processing (HTAP) powers some critical machine learning applications. With HTAP systems, machine learning-powered data virtualization solutions can be developed and deployed at enterprise-level applications.
The whole idea behind HTAP systems is properly managing and processing insights from hybrid systems used by machine learning applications. Organizations can easily use insights from their on-site data locations in parallel with insights stored in the cloud. The data can be used in parallel with each other by deploying HTAP in-memory computing systems. As a result, machine learning applications can have a broader data pool to process and draw insights.
Powering eCommerce AI/ML processes with this tech
E-Commerce is one of the industries that have yet to reach its peak, which makes full use of most new technologies. AI and machine learning processes have been deeply ingrained in this industry. The application of these technologies spans from processing payment transactions to sending confirmation and marketing emails. Not to mention personalizing content and products to potential customers on the website.
All of these eCommerce AI/ML processes require large data set processing. At the same time, the website should respond quickly, which might be tough to pull off when processing large amounts of data. In-memory computing resolves this problem by caching relevant data and providing it to the relevant user in record time.
Other applications of in-memory computing
In-memory computing has also made waves in the Business Intelligence field by powering machine learning with all the relevant data. BI tools require rapid processing of data, especially when providing real-time insights. Therefore, in-memory computing is the best method to bridge the gap between big data processing and providing real-time and accurate insights.
On the other hand, in-memory computing powers machine learning applications that power technologies such as autonomous driving cars. There is also a lot of data to consider, including the best routes to take as well as the information from the sensors on the vehicle. All of this data is aggregated and computed using in-memory computing to make these vehicles as safe as they are.
How does in-memory computing benefit Machine Learning
The core benefit of in-memory computing in machine learning is aggregating information from disparate sources and making it available as fast as possible. By using various nodes for RAM, machine learning applications can have rapid access to stored data from disparate sources.
This technology also works when there is one huge data source that will take a lot of time to process and sort through. In-memory computing benefits machine learning by providing quick insights, which might be essential in business operations. Therefore, the domino effect of expedited data processing can be streamlined revenue and financial savings. In the long run, the investment made to implement in-memory computing can be easily repaid.
Machine Learning is highly dependent on effective data processing systems such as in-memory processing. There are various implementations of in-memory computing in the machine learning application development field. Some of these implementations include HTAP, which helps expedite data processing insights gleaned from a hybrid data storage system.