AI-based startups need to draw insights from the data available to them and they often collect plenty of data right from the start by using AI. They need new solutions to store, retrieve and process large volumes of data speedily.
In-memory technology moves data completely into memory so there’s no need to access information stored in a disk-based database. Storing it in the main memory offers easy access and analysis.
Getting insights from the endless flow of data was impractical in the past but with the rise of distributed data architectures and in-memory processing, extremely fast processing is now possible.
In-memory computing
A lag between user action and the response of an application to the action is called latency and this has become a real issue today when so many applications require low latency. Disk latency is measured in milliseconds and in memory computing latency is measured in nanoseconds.
In-memory databases have become more affordable as the cost of RAM drops and they provide the latency, speed, agility and scalability that AI-based startups require today.
An in-memory database (IMDB) consists of clusters of computers that work in parallel. In contrast to a single centralized server to manage and provide processing capabilities to connected systems, parallel processing means that many computers across different locations share processing capabilities. This offers quicker processing and more storage capacity. All data is stored in an in-memory database in a usable format which isn’t the case with a traditional on-disk database.
Lightning-fast performance
When a system has to serve high traffic volumes, it can start taking a long time to reply. With a high-performance in-memory database, it is possible to serve more requests and do so in a safer way. Spikes in traffic do not incapacitate the system and as downtime can be expensive, reliability saves money. Reading from RAM is extremely fast and high-performance in-memory databases can perform millions of read/write operations per second.
Why AI-based startups need in-memory computing
Quick and complete access to data and analytics can inform all functions within a startup such as operations, marketing, customer service etc. Using in-memory computing enables faster speed, reduces costs, boosts business insights and increases efficiency for AI-based startups.
Artificial intelligence has become an integral part of success for many startups today. It offers them intelligent solutions to many different business problems and the ability to self-learn in a logical way to address future problems of a similar nature. New advancements are making it easier for startups to integrate AI into their software or cloud solutions.
AI can reduce costs by creating more efficiency and replacing the need to employ extra people or outsource for repetitive tasks. For example, when startups add an AI-based chatbot with access to all customer data, they can handle millions of calls without having a huge call center.
AI and machine learning help to take operational data and learn from it in various ways so as to optimize transactional flow. Some real-world examples of this include mobile phones, smartwatches, drones, self-driving cars and much more. AI is becoming embedded in so many devices and all of this technology generates data continuously at volumes that go way beyond the ability of humans to process. Processing is, however, within the scope of AI and machine learning.
A 21-year-old entrepreneur in India recently launched a podcast on climate change and is busy developing an app and website with the help of AI that will telecast information in all 22 languages Indian languages. His project aims to support grassroots activists by sharing climate change information and successfully connecting with individuals.
Salesforce is a well-known platform for customer relationship management and sales that launched an AI platform called Einstein for startups. The platform provides helpful services to them, such as the ability to identify new prospects, predict customer service issues, personalize marketing based on customer preferences, automate emails, and predict which leads are most important.
Analyticly is a startup that makes AI-driven recommendations on Shopify eCommerce sites that learn over time. It enables businesses to offer customers products they want based on their behavior.
AI-based productivity apps
A number of interesting startups are coming up with AI-based productivity tools such as apps that help with time management, productivity, and creativity. The world of artificial intelligence and advances in mobile computing are coming together in many innovative ways.
The remote and hybrid workplace revolution that has taken place over the past two years is also leading to the development of many innovative workplace productivity and collaboration applications. Applications that generate high volumes of streaming data require performance on a different scale. They can benefit a great deal from the speed that in-memory technology can offer. The idea of millions of transactions taking place every second becomes possible when memory moves from disk to RAM.
There are various key considerations for entrepreneurs when starting an entrepreneurial venture, such as knowing where customers will come from because without customers, there will be no revenue. AI offers them predictive analysis that can take much of the guesswork out of many business decisions and in-memory computing is applicable in any industry where real-time insights, analysis and predictions based on streaming and historical data create business value.
In-memory computing offers real-time performance
With in-memory computing, analytics do not have to run for hours but can take only seconds. Startups can make effective business decisions using relevant, fresh data. They can unlock new opportunities and prevent revenue loss due to an immediate understanding of the impact and consequences of events.
In-memory computing also enables competitor analysis and understanding of customer trends which is another important part of business intelligence. As startups start to scale up, they can identify new niches to target while retaining their existing customers.
Conclusion
The fast performance offered by in-memory computing can deliver so much value for AI-based startups in a broad range of industries and in different use cases. In-memory computing can power AI and improve the latency and energy efficiency in AI computing by orders of magnitude.