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.
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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.
Merits and demerits of AIaaS
If 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
| Merits | Demerits |
Building in-house capabilities call for significant investment and expertise. Developing and testing AI models take a considerable amount of time before final deployment. However, with AIaaS coming to the fore, the task of smooth AI deployment by an organization is reduced to a minimal scale. |
 To take charge of AIaaS, you need to share valuable company data with a third-party provider, which can cause security and privacy concerns.  To avoid such a scenario and ensure that legitimate entities access your data, you need to secure your data storage, data access mediums, and data transit paths. Some organizations limit cloud data storage extensively. This hinders the business from accessing full-fledged AIaaS. |
AIaaS offers easy setup as no complicated installation is required. You can plugin and get direct access to the required AI features. This reduces the need to have a dedicated team of IT professionals or even any complex infrastructure. |
2. Blurry visibility
 In an AIaaS solution, you only pay for services offered by the provider. However, you are not given access to the actual underlying process. This implies that you are aware of the input and output of the system but do not have any insight into the AI algorithms that are in use to deliver a specific result. |
In an AIaaS solution, you are only liable to pay for the features that you actively use. You are not expected to pay for the AI functions your organization doesn’t need from the overall AIaaS package. |
3. Third-party dependency
 Since you rely on a third party to provide correct information, any error in their software can cause operational issues or delays. This can be problematic with real-time use cases. |
AIaaS allows companies to scale their AI feature list up or down based on the business or project needs. Such flexibility makes AIaaS suitable for companies trying their hands at AI for the very first time. |
4. Add-on costs
 Although AIaaS is a cost-effective solution, the ongoing fees can quickly pile up as you add additional AI features. On the flip side, additional AI capabilities can only give you more insights into the underlying operations, thereby boosting your profitability in the long run. |
See More: What Is Anything/Everything as a Service (XaaS)? Definition and Key Trends
Key Architectural Components
The 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 infrastructure
AI infrastructure supports underlying AI and ML models. Data and compute are the two fundamental pillars of these models.Â
- AI data: When you apply large volumes of data to statistical algorithms, it is regarded as a functional ML model. These models are built to learn from patterns in the existing data. The sheer volume of data decides the accuracy percentage of the predictions. For example, numerous medical reports train deep learning networks, which further evolve and detect medical emergencies, cancer, or tumors.
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.
- AI compute: AI compute services include VMs, serverless computing, and batch processing. These computing methods are used to enhance parallel processing and automate ML tasks. For example, Apache Spark is a real-time data processing engine that has a scalable ML library. On training the ML models, they are used in VMs and containers to perform computations.
2. AI services
Public 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.
- Cognitive computing: Cognitive computing APIs include speech, text analytics, voice translation, and search. These services are accessed as REST endpoints by developers and integrated with applications with a single API call.
- Custom computing: Although APIs serve the purpose in generic cases, cloud providers are shifting toward custom computing, enabling users to experience cognitive computing using custom datasets. Here, users employ their data to train cognitive services. The custom approach reduces the overhead of selecting the right kind of algorithms and also training the custom models.
- Conversational AI: Today, the world is becoming increasingly familiar with virtual assistants as end-users continue to accept AI readily. Thus, cloud providers are helping developers to integrate bots (voice, text) across platforms by leveraging bot services. Using this service, web and mobile developers can add digital assistants to their applications.
3. AI tools
In 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.
- Wizards: Amateur data scientists are served with wizards to reduce the complexity of training ML models. At the backend, these tools, in totality, act as a multi-tenant development environment.
- Integrated development environment (IDE): Experienced cloud vendors are making substantial investments in IDEs and notebooks (browser-based) that help in easy ML model testing and management. Such tools enable developers and data scientists to build smart applications with ease.
- Data preparation tools: The performance of ML models heavily depends on the quality of data. To ensure the top-notch efficiency of ML models, public cloud vendors are providing data preparation tools that can perform the extract, transform, load (ETL) job. The output of these ETL jobs is then fed into the ML pipeline for training and evaluation purposes.
- Frameworks: Cloud providers offer ready-to-go VM templates with frameworks such as TensorFlow, Apache MXNet, and Torch, as setting up, installing, and configuring the required data-science environment has become complicated. Such VMs train complex neural networks and ML models as they are GPU-supported entities. Public cloud providers are adopting AI on a large scale as they are looking to attract more customers to their platforms. Although AIaaS is still evolving, it can be a game-changer in the context of data and compute services over the coming years.
See More: Virtual vs. Private Cloud: 10 Key Comparisons
Top 8 Artificial Intelligence as a Service Trends for 2021
With 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 services
With 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 microservices
As 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:
- Solution design flexibility: Microservices allow flexibility around designing solutions as each individual AI microservice can have substantial complexity with the requirement for monitoring, retraining of ML models, and maintenance.
- Speed up AI capabilities: Microservices enable speeding up of explainable AI capabilities and consequently take care of AI ethical use.
- Ease of decision-making: Microservices reduce the complexity of data science, which is a core part of the decision-making process. Additionally, it allows experts to design AI applications that are secure.
- Promote rapid digitization: Microservices allow seamless ingestion, testing, and effective usage of domain-specific ML services. These services give direct access to ML technology that is designed to tackle specific problems. It also enables industries to adapt themselves to the rapidly growing digital world.
3. Add bot stores
Large 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 APIs
APIs 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 & services
Developers 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 capabilities
AIaaS 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 components
A 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 setups
AIaaS 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
Takeaway
AI 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.
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