Why Is AI Gaining Popularity?
Gains in AI’s popularity are clear. Artificial intelligence is becoming a need, not a want because it creates the ability to make sense of increasing large amounts of incoming data. According to IDC, worldwide data will grow 61% to 175 zettabytes by 2025.
With 37% of organizations having implemented AI in some form, Gartner has seen an increase of 270% over the last 4 years. Businesses are seeing the advantage of AI helping businesses maximize efficiency through a partnership of augmented reality and people.
For example, chatbots have transformed stale customer service into an engaging experience. From here, machine learning can leveraging the incoming information to extract patterns and search for common customer problems to solve these issues within FAQs or the product itself.
The crux of AI lies within the latest means of deployment for businesses—including the use of advanced analytics to better predict needs, intelligent processes to reduce costs, and advanced user experiences to promote brand value and customer retention.
Stages of Data Storage Within an AI Workflow.
The stages of data storage within an AI workflow describe the process used by AI and machine learning to harvest and apply data. Remember that the AI depends on information to survive and thrive, so it is imperative to understand the data storage requirements throughout both workflows.
- Data Collection Stage: The data collection stage is the simplest. This is the point where data enters the AI. Of course, data must be of high quality, definition, and accuracy. The model’s output depends on the data’s integrity, and failures within storage systems could degrade data. Capacity and scalability are the two most critical aspects of this stage so if your current storage cannot support this, you have your first failure point.
- Preprocessing Stage: The preprocessing stage is when the data goes through a cleansing process. Normalizing, standardizing, and cleaning data ensure the algorithms within the AI and machine learning can understand and process the information. Data also receives labeling and computer-generated tags to outline the characteristics that the AI needs to review.
- Training Stage: After the data completes preprocessing, the real work of training the AI begins. Running through an GPU-accelerated cluster, data trains the algorithms to recognize patterns between data. During this stage, volume of data is amplified because of the due to a series of events and activities need to process the data sets. In this area in particular, faster GPU processing results in faster training, again promoting efficiency and productivity.
- Inferencing Stage: The inferencing phase requires less computing power because there is no active learning however, the data moves faster through the system here. Additional throughput therefore demands greater bandwidth from your storage and contributes to an increased demand on available resources. Because your data is now jumping through more loops, AI validation, data transformation and revalidation, traditional storage methods are ill-equipped to handle this surge an can slow business processes if your storage cannot keep up with the data coming in and the AI information you are gaining.
How Are Traditional Data Center Storage Solutions Failing?
Traditional storage methods rely on a pre-defined data volume. Storage is purchased and retained, then expanded when needed. Though cloud solutions and software defined solutions have helped extend the life of these storage solutions, traditional solutions aren’t optimal for AI throughput, here’s why:
- Finite Storage Space Results in Machine Lag, Limiting Performance of Algorithms. If there is limited storage space, the processing of your AI can be halted, and data collection will not be able to move through the preprocessing and inferencing steps to improve the intelligence and ability to use your data for actionable results.
- Limited Cybersecurity Can Open the Door to Risk in Managing Consumer Data. Another problem includes cybersecurity. Since AI requires large amounts of information to make decisions, storage with older security build in can create more opportunity to allow backdoor access for hackers to steal your data thereby losing the insight it could have provided. Newer storage solutions actually have their own AI build in to learn from your interactions and identify security threats so breaches tend to limit data theft.
- Inability to Maintain Data Consistency Results in Inaccurate AI Insights. AI thrives on consistency. While inconsistencies are a part of life for data collection, the preprocessing stage creates the consistency needed. Without adequate data storage availability, preprocessing falters.
- Poor Scalability Results in Inability to Add More Data to Improve the AI. Data remains subject to the flurry of human activity. Sudden peaks and lulls in data generation do occur, and these sudden changes will require the rapid scaling of storage to succeed. If storage options fail, including cloud-based storage, the AI is incapable of learning and will miss opportunities for business improvements.
How Can HPE’s Nimble Infosight AI Help With Business Applications and Processes?
HPE’s Nimble data center storage solution is an innovative storage solution that leverages flash storage and AI-guided analytics to eliminate the data gap and guarantee around-the-clock storage accessibility and performance. Combined with the power of HPE Infosight—HPE’s self-managing, self-healing, and self-optimizing AI—Nimble becomes something more, an intelligent storage solution that can be a solid base for your AI environment.
- Nimble AI Provides Immediate Boost of Capacity Efficiency. Nimble Infosight continuously leverages analytics to assess processes performed and storage needs. If the system recognizes rapidly changing data volume, automatic controls identify available storage space and ready it for use. In other words, the system recognizes the need for added storage capacity and makes it possible.
- Scalability to Meet Demands Regardless of Day or Time.The surge of data may occur at any time, and Nimble Infosight can rely on historical data to move functions around the clock and meet demands regardless of day or time. Infosight collects and analyzes data from more than 100,000 systems around the globe, and through its advanced AI, it continuously improves storage use and allocation based on trends it identifies.
- Predictive Architecture Resolves Problems Before They Cause Disruption. Nimble Infosight also leverages a predictive architecture that identifies and resolves 86% of problems before they cause actual disruptions. This is critical to building better customer experiences and allowing your AI to flow with the data it needs. If your storage is more available, your AI won’t have issues processing.
The culmination of these powerful functions working in the background to support your own AI environment is ground-breaking; businesses achieve a future-proof storage and dependable AI-based processing solution.
Don’t ignore the AI revolution, it’s coming!
Businesses continue to rely on their data for information but the sheer mass of data coming in is making it impossible for humans alone to process this data. Relying on traditional storage methods will eventually become too cumbersome and expensive to maintain long term. Take a look at your current infrastructure and understand the future of AI for your business and how storage with built-in AI can accelerate your AI journey. Talk to one of Comport’s technology experts today to accelerate your AI road map and ensure your storage is ready for your future.
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