The global pandemic has pushed industries and economies to put a renewed focus on data to get insights and push their businesses forward.
With the boom of data, artificial intelligence (AI) has also garnered some extra attention due to its capability to learn patterns from data that come from a variety of sources, including smartphones, IoT, analytics, and other connected devices.
As data grows in quantity and complexity, the sheer urgency of managing and processing it becomes more apparent. A Gartner shows that unstructured data growth grows 50% year over year, as reported by organizations in different industries.
In-memory computing has been heralded as a viable solution to this dilemma due to the computing power it provides that allows for the processing of large amounts of data in seconds.
The combination of in-memory solutions, AI, and machine learning presents advantages that will help businesses make sound, data-driven decisions by transforming complex data into actionable insights.
With AI set to create substantial business opportunities and societal value in the coming years, this era has been dubbed the AI-enabled era. A Grand View Research report states that the CAGR of the AI market will be 56.8% between 2018 and 2025.
The rapid growth in data has transformed the proper management of artificial intelligence workloads and compute requirements a necessity, if a business wants to keep or gain a competitive edge.
Even popular consumer mobile services like Facebook, Instagram, Siri, Google Assistant, and a number of eCommerce websites have been using AI for quite some time now. Suffice to say, the future of business looks to be one dominated by AI and big data.
4 Ways To Accelerate Your Artificial Intelligence Workload
1# Innovative AI Strategies
Although a number of organizations have turned to scale-out storage systems to contend with data’s exponential growth, this doesn’t solve the challenges of processing that data—and doing it fast.
High-speed data processing is vital for businesses to immediately respond to consumer and market demands. Legacy storage and systems were designed to address past problems and simply can’t keep up with the challenges of today’s workloads.
For example, feeding data into compute resources can’t be done fast enough and they can’t be scaled to petabytes of capacity when the need arises. Generally, compute time is wasted for expensive CPU’s or GPU’s because legacy systems have to contend with billions of files within a dataset.
This shortcoming of legacy systems leads to a phenomenon referred to as performance tax, wherein organizations need to invest more in infrastructure to keep up with increasing performance and capacity requirements.
Accelerating AI workloads and optimizing strategies has become one of the main goals of every organization, and finding a solution that’s both cost-effective and that meets business needs is of paramount importance. Below are a few ways organizations can rethink their approach to AI and big data.
2# Parallel processing
Organizations want to gain deeper insights through AI technologies, and in-memory computing makes this possible through a process referred to as parallel processing.
In parallel processing, the system uses multiple processors to handle separate parts of a single, larger task. Breaking up a task into smaller parts and using multiple processors to handle each part significantly reduces the amount of time required to run a program.
As organizations come to realize the importance of parallel processing for different use cases, the adoption of AI and data science technologies continue to grow.
Even now, a number of organizations use AI and machine learning in data analytics, cybersecurity, fraud detection and other ways to help provide advanced products and services to customers with changing needs and demands.
3# Optimized Infrastructure Investments
Due to the above mentioned performance task, organizations should work to optimize their IT and infrastructure spending.
It might be practical for some to invest in high-value, high-performance CPU and GPU infrastructures, but without the required storage infrastructure, you won’t be able to maximize the use of these systems and, consequently, it would be hard to justify their benefit-cost ratio.
Modern in-memory solutions help reduce this I/O bottleneck by using main memory or RAM instead of disk. By doing so, they ensure low latency while also maximizing throughput.
4# Going In-memory
To gain better insights from data, organizations must be able to process it at high speeds. Real-time data is the name of the game since it will help provide actionable insights as the need arises. This means that organizations will have an advantage because they will always be a step ahead of the competition and their customers.
However, this requires high performance and computing capabilities, because aside from high-speed data processing, computing solutions must be able to conduct machine learning through the examination of large datasets and tune neural net models.
Fortunately, AI’s computing requirements are similar to other compute-intensive applications like big data analytics, modeling, and forecasting.
In-memory computing has been heralded as the enabler of data analytics due to its capability to provide processing speeds that are more than 100 times faster than other solutions.
4# AI for the Long Term
The integration of AI technology into modern systems is gaining popularity as businesses see the inherent benefits of adopting it in conjunction with big data analytics.
As AI and in-memory computing mature as platforms, it has proven to be helpful in enhancing customer experiences, making data-driven business decisions, and ultimately delivering on its promise of operational cost savings.
In the coming years, organizations need to design their infrastructure in such a way that these benefits can be maximized and scaled for the long term.
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Author Bio: Edward is a freelance AI and Big Data consultant. He specializes in finding the best technical solution for companies to manage their data and produce meaningful insights. Follow him on LinkedIn.