Artificial intelligence as a service (AIaaS) is defined as a service that outsources AI to enable individuals and companies to explore and scale AI techniques at a minimal cost. Artificial intelligence benefits businesses in numerous ways, right from improving customer experiences to automating redundant tasks. However, developing in-house AI-based solutions is a complex process and requires huge capital investment. That’s why businesses are openly embracing AIaaS, where third-party providers offer ready-to-use AI services. This article looks at the definition and architecture of AIaaS and lists the top AIaaS trends to watch out for in 2021. Show
What Is Artificial Intelligence as a Service?Artificial intelligence as a service (AIaaS) is defined as a service that outsources AI to enable individuals and companies to explore and scale AI techniques at a minimal cost. Artificial intelligence benefits businesses in numerous ways, right from improving customer experiences to automating redundant tasks. However, developing in-house AI-based solutions is a complex process that requires huge capital investment. That’s why businesses are openly embracing AIaaS, where third-party providers offer ready-to-use AI services. Artificial intelligence as a service refers to out-of-box AI services rendered by companies to potential subscribers. AI refers to a paradigm where computer systems perform human-like tasks by reasoning, picking up cues from past experiences, learning, and solving problems. Broadly, disparate technologies such as machine learning (ML), natural language processing (NLP), computer vision, and robotics come under the AI roof. Like software as a service (SaaS) and infrastructure as a service (IaaS), AIaaS provides an ‘as a service’ package that a third-party provider hosts. This is a cost-effective and reliable alternative to software developed by an in-house team. As such, AI becomes accessible to everyone in the corporate ecosystem. With AIaaS, end users can harness the capabilities of AI through application programming interfaces (APIs) and tools without having to write any complex codes. Like any other ‘as a service’ solution, AIaaS uses cloud computing models effectively to leverage AI. It adds substantial flexibility in overall organizational operations and enhances efficiency, thereby driving productivity levels. AIaaS is highly dynamic and adaptable. It is primarily effective in optimizing the outcomes of big data analytics projects. These readily available AI services allow companies to extract the key benefits of AI without making huge capital investments (or even bearing the related risks) to build and execute their cloud platforms. Global businesses continue to adopt AIaaS as they see the great value it has to offer. According to a June 2021 report by Technavio, the global AIaaS market is expected to grow by $14.70 billion from 2021 to 2025 at a CAGR of 40.73%. Overall, AIaaS offers several benefits, including ease of setup, which can even be accomplished within weeks. However, initial research is essential for any organization to understand its requirements for AIaaS adoption better. See More: What Is Artificial Intelligence: History, Types, Applications, Benefits, Challenges, and Future of AI Merits and demerits of AIaaSIf you are considering AI as a service for your business, it is worth looking at the merits and demerits it comes with. This can paint a clearer picture of whether it would be a good strategic investment for your company or just an add-on liability. Merits and Demerits of AIaaS
See More: What Is Anything/Everything as a Service (XaaS)? Definition and Key Trends Key Architectural ComponentsThe AIaaS architecture has three basic components: AI infrastructure, AI services, and AI tools. Each component is further detailed in the section below. Key Architectural Components of AIaaS 1. AI infrastructureAI infrastructure supports underlying AI and ML models. Data and compute are the two fundamental pillars of these models.
ML relies heavily on input data that can be sourced from multiple sources. Data can come from relational databases, unstructured data (binary objects), stored annotation in NoSQL databases, and a pool of raw data in a data lake. All these are used as inputs to the ML models. Advanced ML techniques, including neural networks, perform complex computations that require a blend of central processing units (CPUs) and graphic processing units (GPUs). Both these components complement each other and enable faster processing. Cloud providers offer clusters of GPU-CPU combination-backed virtual machines (VMs) and containers in an AIaaS setup. Clients can use this infrastructural arrangement to train ML models and choose to pay on a use per basis.
2. AI servicesPublic cloud vendors provide APIs and services that are readily available and do not need custom ML models for their consumption. These APIs and services extract benefits from the underlying infrastructure, which the cloud provider owns.
3. AI toolsIn addition to APIs and infrastructure, cloud vendors provide tools that can help data scientists and developers. These tools promote the usage of VMs, storage, and databases as they are in sync with the data and compute platforms.
See More: Virtual vs. Private Cloud: 10 Key Comparisons Top 8 Artificial Intelligence as a Service Trends for 2021With growing competition across industries, businesses are increasingly investing in digital technologies such as AI to gain a competitive edge over their competitors. As such, AIaaS trends are set to take center stage in the cloud computing world. Let’s look into the top eight AIaaS trends to watch out for in 2021. Upcoming Artificial Intelligence as a Service Trends 1. Zero-in on managed servicesWith the growing AIaaS market, managed services have become the focus of many companies as they opt for AI services specific to a particular function, process, and application. An example of this could be third parties offering AI-based contract interpretation services for legal ventures. Some financial firms are tying up with third-party providers that offer end-to-end exception handling services. Similarly, top technology companies such as IBM partner with telecom giants such as Samsung, Nokia, and Cisco to provide end-to-end managed services to increase automation and deliver better customer and enterprise value. 2. Rise in microservicesAs AI penetrates most industries, enterprises (small or big) are expected to get their hands on AI microservices. These microservices deliver AI as a package of independently deployable services that are tailored to specific business needs. Microservices tackle various critical issues, such as:
3. Add bot storesLarge enterprises can automate repetitive tasks by buying readymade and pre-built bots. These can include chatbots that employ natural language processing (NLP) algorithms to identify language patterns from human conversations and provide answers based on the identified patterns. Such a framework allows customer service employees to focus on critical and complicated tasks without answering each customer. 4. Develop more computing APIsAPIs are built to add additional functionalities to any kind of application, i.e., new or existing. Companies only need to figure out the type(s) of AIaaS features they require to propel their ROI numbers. Once the features are finalized, the enterprise can approach an AI provider, purchase the AI package, and implement it immediately. Smaller updates or patches can be made as and when the need arises. Common API services include voice recognition, emotion detection, NLP, language translation, and computer vision. 5. Use ML frameworks & servicesDevelopers use ML frameworks to build a customized AL model. These data models can read patterns from existing datasets (customer data) and use their learning to make future predictions (sales, market growth, and revenue). The USP of ML frameworks is that they do not need big data to operate or work. As a result, the frameworks are suitable for all types of companies, from small companies that do not have large volumes of data at their disposal to large ones that thrive on big data. 6. Build in-house foundational capabilitiesAIaaS calls for systematic coordination between the AI service provider and the subscriber company to prevent sensitive data from being compromised. These coordinated systems undergo regular maintenance and updates to keep vulnerabilities (internal and external threats) in check. Hence, enterprises are expected to train their employees who work with sensitive systems to keep them cyber-safe. Over the coming years, it will become essential for all working staff to know, understand, and engage in security practices to collaborate with AIaaS seamlessly. This will ensure that the networks are not compromised and vulnerabilities aren’t allowed to creep in. 7. Outsource AI componentsA third-party service provider has a pivotal role to play in AIaaS. Firms can use this by outsourcing their AI components (ML, complex and out-of-the-box algorithms, end-to-end AI services, developing virtual assistants, and conversational AI) to service providers. Companies need not worry about the required setup, maintenance or necessary improvements. With such an AIaaS facility, enterprises can invest their time in critical tasks that need attention. 8. Test AI setupsAIaaS demands extensive testing and validation of AI components before their final deployment. Companies can therefore use AIaaS to test their AI setups. This will considerably reduce capital expenditure on robotics, skilled staff, and embedded systems. Also, the cost incurred to develop, upgrade, and maintain AI testing skills within in-house teams will go down significantly. See More: Top 10 Hybrid Cloud Security Solution Companies in 2021 TakeawayAI as a service allows companies to exploit state-of-the-art AI, ML, and cognitive solutions without heavy investments into infrastructure, skilled personnel, or maintenance overheads. Instead, it acts as a driving tool to boost add-on functionalities into existing products and services. Most service providers promise to lend high-quality services with minimal efforts from the subscriber’s end. AIaaS may completely not replace the existing task force, but it will enable organizations to zero in on business-centered functions. With AIaaS, small firms can collaborate with state-of-the-art AI platforms to deploy cognitive functionalities for wider customer reach. However, businesses adopting AIaaS also need to cross-check a few details before they dive in. Questions related to data residence, data protection regulations, and others need to be answered, as it can affect your business. All in all, organizations need to perform due diligence with utmost care to avoid adverse business impacts. Do you think AI as a service will take off in 2022? Comment below or let us know on LinkedIn, Twitter, or Facebook. We’d love to hear from you! MORE ON ARTIFICIAL INTELLIGENCE
How does artificial intelligence help the community?The Fusion of AI and Communities
The fuel that ignites community activity in combination with AI is real-time data. The data collected can be used to suggest changes, define patterns, automate strategies, and test them as and when required.
What does AI do for businesses?By deploying the right AI technology, your business may gain the ability to: save time and money by automating and optimising routine processes and tasks. increase productivity and operational efficiencies. make faster business decisions based on outputs from cognitive technologies.
How would you define AI?Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
How AI could improve business and society?The impact of AI on your business is huge as it helps to solve a great variety of tasks:. automate and optimize routine processes to save time and money.. increase operational efficiencies.. use cognitive technologies to make business decisions.. avoid 'human error'. predict customer preferences and grow your customer base.. |