Artificial intelligence has had a significant impact on global markets. It has enhanced products and consumer experiences while assisting enterprises in making data-driven decisions that significantly impact their processes. Artificial intelligence has benefitted various financial industry areas, including health, education, banking, marketing, and more. People and organizations who want to scale AI techniques but don’t have many resources or expertization can immediately obtain pricey AI-based solutions for specific industries. AIaaS is quickly becoming an attractive alternative for anyone seeking access to artificial intelligence without investing in costly infrastructure. For organizations that want to obtain insights from their essential corporate data, AI solutions are becoming the standard. With such a low-cost solution available to anyone, it’s no wonder that AIaaS is becoming the norm in most businesses. This article discusses this technology, its advantages, applications, and challenges.
What is AIaaS?
Artificial intelligence as a service (AIaaS) is a service that implements AI to allow people and businesses to experiment with and expand AI approaches at a low cost. AI improves organizations’ efficiency in various ways, from enhancing customer experiences to automating repetitive tasks. On the other hand, building in-house AI-based solutions is a time-consuming and costly process. That is why organizations enthusiastically adopt AIaaS, wherein third-party suppliers deliver ready-to-use AI services.
Artificial intelligence as a service provider refers to organizations that provide out-of-the-box AI services to potential customers. AI is itself is about a set of technologies that do human-like functions such as thinking, picking up hints from previous experiences, learning, and problem-solving. AI includes a broad range of technologies, including machine learning (ML), natural language processing (NLP), computer vision, and robotics.
Difference between AIaaS and AI
Artificial intelligence (AI) is the programming of machines to perform tasks that humans traditionally perform. AI is the demonstration of intelligence comparable to that of humans in machines or anything else under computer control, enabling them to carry out activities with little to no human involvement. Contrarily, AIaaS is a third-party offering of AI tools and solutions designed to make AI more accessible to a larger spectrum of companies searching for fresh prospects in the digital sphere.
AI refers to the programming of robots to perform jobs that are traditionally performed by humans while simulating human intellect. Artificial intelligence (AI) is the demonstration of human-like intellect in machines or anything under a computer’s control, enabling them to complete tasks with little to no human involvement. On the other side, AIaaS is a third-party offering of AI tools and solutions to make AI accessible to a larger variety of organizations searching for new prospects in the digital sphere.
Artificial intelligence has completely changed how companies interact with their customers by providing more intelligent products and services and automating the majority of business procedures. All major industries, including healthcare, banking, gaming, entertainment, social media, travel & transportation, e-commerce, automotive, etc., have seen substantial advancements in AI. For companies wishing to automate their operations, develop smarter products, and better understand their target market, AIaaS offers ready-to-use AI solutions.
What are the Types AIaas?
There are many different types of AI services, and one may select the one that best meets the organization’s demands. The three most common types of AIaaS options are as follows:
Machine learning is one of the largest subsets of AI. Machine learning as a service models is pre-trained and supplied with tools to enhance company operations. Most accessible ML models do not require prior knowledge, yet they can help you provide insights to simplify your business.
Nowadays, whether you surf the web looking for anything from official websites to shoe stores, you are bound to stumble across bots, notably their most frequent variety, chatbots.
Chatbots employ AI algorithms to replicate human dialogue. They use NLP and machine learning to comprehend user inquiries and give appropriate replies.
Application Programming Interface (API)
AIaaS solutions provide dynamic APIs, which allow services to interface with one another. APIs serve as a bridge between two pieces of software, allowing them to communicate.
APIs for natural language processing now provide sentiment analysis. They can also extract concepts from the text, among many other things. APIs may be accepted and implemented quickly when supplied as a service, and developers must write a few lines of code. APIs are also commonly used for the following purposes:
- Computer vision and computer speech
- Emotion detection
- Emotion detection
- Search Knowledge mapping
Data labeling is the process of annotating a large amount of data to sort them efficiently. It has many uses, such as ensuring data quality, classifying data based on size, and creating artificial intelligence. Data is labeled using human-machine learning in the loop, enabling humans and machines to constantly interact, making it easier for AI to evaluate data in the future.
Classification of data
Data classification occurs when data is labeled under one or more categories. Data classification is usually content-oriented, based on context and user. With the help of artificial intelligence, data can be classified
Use cases of AIaaS
Businesses can no longer construct and operate their own artificial intelligence (AI) infrastructure. Companies may now access powerful artificial intelligence capabilities with a few clicks thanks to artificial intelligence as a service platform like saiwa. The following are the most important applications of AIaaS:
The AI system identifies and stops illicit money activities. MasterCard’s finest exemplifies the capabilities of these AI-powered systems. It uses 1.9 million rules to analyze each transaction. It also uses ML algorithms to process 165 million transactions each hour. Everything occurs in milliseconds.
Image recognition systems examine images to detect locations, persons, and objects and derive inferences from them. Many industries, such as security, medical, education, finance, manufacturing, telecom, utilities, and defence, quickly adopt image recognition systems to improve visual data processing and analysis speed, accuracy, and efficiency.
Natural Language Processing
NLP systems make use of computer-generated voice and text. They interact with consumers in real time. Chatbots are built with Natural Language Processing and Machine Learning, which means they can understand the complexities of human languages and determine the true meaning of a statement while learning from human conversations and improving over time.
Why should we use AIaaS?
We should employ artificial intelligence as a service now for numerous reasons. The following are the primary causes:
AIaaS is one of the most accessible and available methods to use artificial intelligence to improve your business. You do not require any technical expertise to get the benefits of AI. Most AIaaS solutions do not necessitate any coding or complex setup procedures. Given the scarcity of AI professionals, Artificial intelligence as a service makes the most sense for the vast majority of small and medium-sized organizations. However, specific alternatives may be more challenging to adopt depending on the old software you utilize.
While many AI choices are free and open source, they are not usually easy to utilize. This signifies that your developers are investing time to deploy and build the AI technology. Instead, AIaaS is ready to use right now, allowing you to leverage the potential of AI without first becoming a technical expert.
One of the most important reasons to use AI as a service is that it reduces expenses, including creating AI solutions. Furthermore, because Artificial intelligence as a service allows us to pay per usage, organizations may benefit from pricing transparency, as they will only pay for what they need.
We’ve never heard of a company with the same objectives! Indeed, targets are always business-specific. As a result of the changing natural goals, Artificial intelligence as a service may be really well suited to adapt to the business, data, or project requirements.
What are the challenges of AIaaS?
As previously stated, artificial intelligence as a service has several benefits and applications; however, in addition to these benefits, you may encounter the following challenges while utilizing this technology:
Data confidentiality and security
Because AI and machine learning rely on large volumes of data, your organization must share that data with third-party partners. To ensure that data is not inappropriately accessed, transmitted, or tampered with, storage, accessibility, and transit to servers must be protected.
But still, some Artificial intelligence as service providers like saiwa provide interesting features for local or hybrid execution of services that follow the principle of “data do not leave its home” and still benefit the resourcing power of cloud processing.
Companies in heavily regulated industries must minimize data storage in the cloud. Artificial intelligence as a service may have constraints for companies in finance and healthcare.
AIaaS is no exception when it comes to expenses. You may seek more advanced products as you explore AI and machine learning. These services can also be more expensive since you must acquire and educate specialized personnel. However, like with everything, the expense might be a beneficial investment for your business.
Transparency has been reduced
In AIaaS, you buy the service but not the access. Some regard ML service offerings, particularly, as a black box—the input and output. Still, you do not understand the inner workings, such as which algorithms are being used, if the methods are updated, and which versions apply to which data. This might result in confusion or misinterpretation about the stability of your data or the results.
Imagine using a different API that uses different response formats. You might think that changing it is easy, but anyway, different response formats and changing APIs require a lot of effort.
Also, end-to-end ML services or even ML components are more difficult to change tools because the development team must be familiar with them. All of these aspects lead to vendor lock-in, where companies must understand the pain points of switching between competing products.
Trying to implement without bugs
Another concern with implementing AIaaS software is that it may not be without bugs and the implementation will require a lot of effort for a seamless and successful transition.
Realizing the Potential of AIaaS:
Selecting the right AIaaS provider is key to successful implementation. Businesses must consider factors such as the breadth and depth of AI capabilities offered, the level of customization available, and the scalability of the platform. It is essential to choose a provider that aligns with the organization’s long-term AI strategy and can cater to its evolving requirements.
The first step in realizing the potential of AIaaS is identifying the specific AI-driven needs and goals of the organization. Whether it’s optimizing operations, enhancing customer experience, or automating repetitive tasks, defining clear objectives is crucial. By understanding the business requirements, organizations can effectively evaluate various AIaaS providers and platforms to find the best fit for their unique needs.
As mentioned before, by adopting AIaaS, businesses avoid the need for substantial upfront investments in AI infrastructure and talent. Instead, they can access AI capabilities on a pay-as-you-go basis, reducing operational costs and increasing flexibility.
Ultimately, realizing the potential of AIaaS is a journey of continuous innovation and exploration. As organizations witness the positive impact of AI on their operations, they can expand AI adoption across various business functions. By harnessing the power of AIaaS, businesses can remain agile, competitive, and future-ready in an increasingly AI-driven world.
The benefits of using an AIaaS platform
Organizations can implement AI at a reasonable cost without having to build and maintain individual AI projects using the AIaaS delivery method. Businesses can use AIaaS platforms to create specialist AI services that are flexible, scalable and easy to use. AIaaS systems also have the following advantages:
One of the easiest ways to introduce AI into a business is through AIaaS. The installation and setup are simple. It’s not always practical for an organization to develop and maintain an AI tool for each of the various AI use cases. Customizable options are particularly helpful, as they allow organizations to quickly implement AI services and modify them according to their unique limitations and business objectives.
Requires low- to no-code skills
Even if an organization doesn’t have an on-board AI developer or programmer, AIaaS can still be used. As there is typically no need for coding or technical knowledge during the setup process, all that is required is a layer of no-code infrastructure within the organization.
Cost reduction is a key factor in the adoption of AIaaS in the IT industry. By eliminating the need for large upfront investments and only paying for the AI capabilities that businesses actually use, AIaaS is cost-effective for them.
In addition to reducing non-value-added effort, AIaaS provides access to AI with a high degree of service pricing transparency. Businesses only pay for the AI technology they use, as most AIaaS pricing models are consumption-based.
AIaaS is a good fit for businesses that want to grow. It’s perfect for tasks that don’t add a lot of value, but still require a certain amount of cognitive judgement. Team members have more time to focus on other activities because AIaaS uses industrial automation to perform simple tasks without human intervention.
Availability of high-tech infrastructure
With the help of AIaaS, it is easier to access the powerful and fast graphics processors needed to implement AI and ML models. As a result, access to advanced technology infrastructure is welcomed, especially when most small and medium-sized companies do not have the resources and time to develop internal solutions. Also, with AIaaS being adjustable, different businesses have this opportunity to manage and build a specific task-oriented model.
It’s great to get an open-source platform that can be modified easily. However, if there are challenges for installation and development, the whole purpose will fail. AIaaS is an excellent solution that offers aspects that are completely ready to use. Also, process owners can use and implement artificial intelligence software without any formal training.
Developers can explore end-to-end ML sources such as pre-built models and custom-created models. There are also drag and drop interfaces to reduce complexity. The best part is that business leaders can start ML projects in hours without experts.
We have never heard that all businesses have the same goals. Yes, it’s true that businesses have different goals, so AIaaS can be customized to map to business, data, or project needs.
How do artificial intelligence providers make this possible?
Public cloud service providers have made significant contributions to the growth of AI as a service. These companies offer services and APIs that can be used without the need for specific machine learning models. This is because the cloud architecture offered by the cloud provider has an advantageous architectural underpinning. In addition, developer access to REST endpoints for various AI-related cognitive computing APIs is a critical element. Speech, vision, text analytics, translation and search are some examples of these endpoints. One API call can easily include all of these endpoints.
In addition, cloud service providers are using digital helpers such as text and voice bots to leverage artificial intelligence services. They are also using AI services through technologies that are very useful for developers and data scientists. These are similar to pre-configured virtual machine templates for popular frameworks such as TensorFlow. Such pre-configuration reduces the number of containers, virtual machines, databases and storage that need to be used. These frameworks can be used by data scientists to develop machine learning and neural network models.
Top Artificial Intelligence as a Service vendors:
Artificial Intelligence as a Service system can assist businesses in exploring the potential of their data. By experimenting with the algorithms and services of many providers, enterprises can discover what works and enable scale before committing. These major providers’ resources can provide the computing capacity necessary to support the required scaling once a platform that scales to needs is established. Popular vendor platforms that provide AIaaS services include the ones listed below:
Amazon Web Services (AWS):
AWS is a platform that provides over 200 services globally and various cloud services, such as Artificial Intelligence as a Service. For typical machine learning and AI use cases, AWS offers a number of solutions, including Amazon SageMaker and Amazon Alexa. These Amazon AI services help all parties involved, including clients, businesses, and people with disabilities.
Several AI and machine learning tools are available on Google Cloud, including the Tensor Processing Unit (TPU), which speeds up the training of AI models. Google also provides a number of additional AI technologies to quicken the development process, such as Google Lending DocAI, which automates the processing of mortgage papers.
Companies can choose from various prebuilt IBM Watson apps, such as Watson Assistant for building virtual assistants and Watson Natural Language Understanding for handling challenging text analysis jobs. Developers can construct, train, and deploy machine learning models across any cloud with IBM Watson Studio without any prior data science or machine learning experience.
Microsoft Azure AI:
Data scientists, engineers, and machine learning specialists frequently use Microsoft Azure machine learning and Artificial Intelligence as Service systems. One such platform that helps with text interpretation and analysis is the cloud-based service known as Azure NLP. Azure also offers support for the Python and R languages. Microsoft Azure provides prebuilt libraries, specific code packages, and additional Artificial Intelligence as a Service, such as Azure Cognitive Services and conversational AI.
Oracle provides various services for developing and deploying models, creating custom-trained models, and providing support services for a seamless ML experience, including learning options for AI and ML for all skill levels. Additionally, Oracle’s solutions assist its clients in creating, honing, and deploying models using open-source frameworks like PyTorch and TensorFlow. Additionally, it enables clients to extend the functionality of applications and processes with the prebuilt chatbot, anomaly detection, natural language processing (NLP), speech, and computer vision capabilities.
Annotation AlaaS is an annotation platform that provides outsourcing services for ML and AI models.
It is a SaaS startup that uses Live person conversational cloud. These systems integrate voice, email, and messaging customer experiences with the goal of using intent discovery to inform brands about what their customers want.
One of its most famous services is AIOps, which is actually an artificial intelligence platform designed to help simplify IT operations. With products like AI Contact Center and AI Customer Care, Service Now offers options for digital security.
It is an AI-based analytics platform that uses artificial intelligence to manage big data and manage and retrieve data from various sources. The company provides NLP and visual data mining services and provides an easy GUI through the SAS language.
The solution provided by this platform is exactly what AlaaS should be. To get started, you need to define your workflow, upload sample data, train the custom AI, and let it learn and modify itself. At first the tool prompts you when it’s unclear about how to handle a problem, but it’s designed to perform trivial tasks over time. This platform does not require you to be an expert in the field of artificial intelligence knowledge. The tool aims to provide an intuitive and guided experience in all aspects.
This service is a chatbot service that provides automated messages and calls with human artificial intelligence. Chats are fully automated and can do their work completely on their own. Odus has a user-friendly approach that makes it simple to manage for those without technical skills.
- Provides call and text automation
- Fast integration with a variety of tools
- Provides complex scenarios with entities
The company is a medical imaging company based in San Francisco, California that specializes in the use of artificial intelligence in healthcare. The company uses advanced deep learning to quickly transfer information about stroke patients to specialists who can treat them.
They have also introduced a module that helps control the influx of patients during COVID-19 and allows medical professionals to rate the level of severity for each patient. As a result, their solution enables a better flow of patients and provides the safest workplace for employees.
- Improve patient management
- A safer environment for medical workers and patients with COVID-19
- Reduction of treatment and diagnosis time
It is an exciting AIaaS platform with ready tools for sentiment analysis. The AIaaS platform simplifies text analysis through intuitive, no-code tools. Businesses looking for customized models can consider their process a pre-trained version of the starter where a survey analyzer can be used to categorize customer feedback by topic. It’s also easy to build custom machine learning models for sentiment, keyword, and other detection in a simple point-and-click interface. These models can be easily integrated into other applications.
Is AIaaS solution right for you?
Before going all-in to implement a solution, it’s best to ask yourself the following questions:
- Can I write specific rules for my process?
If the answer is yes, artificial aquarium may not be suitable for your automation plan
- Does the potential seller offer an option to test the product?
You want to test the product with your data, but any AIaaS platform should be able to answer your data security questions in a completely transparent manner.
- Does the product have a secure API?
Again, before deciding on a vendor, it’s a good idea to check any data compliance laws you may have in place.
How big is the AI as a service market?
The market for artificial intelligence as a service was estimated to be worth USD 5.9 billion in 2021, and by 2028 it is anticipated to be worth USD 52.8 billion. The global market is expected to expand at a compound annual growth rate (CAGR) of 44.1% over the projection period.
A third party that offers artificial intelligence outsourcing is known as artificial intelligence as a service. It enables a company to identify several channels for various goals. There is less danger involved, and it doesn’t call for a big investment in installation or service. As a result, AI solutions are a major source of support for most software companies. The majority of IT-based businesses, like Google, Microsoft, and others, have integrated AI as a Service into their technology.
Artificial Intelligence as a Service (AIaaS) is the term for outsourcing AI (AIaaS). It makes it possible for people and organizations to compare several solutions for various goals without making a sizable initial investment and posing a small risk. The majority of intelligent suppliers, including software developers, consultants, and experts in their fields, offer or partner with organizations that can offer a wide range of complete services to support large-scale AI solutions.
Top Artificial Intelligence as a Service Trends
Companies are increasingly investing in digital technologies such as artificial intelligence to gain a competitive edge over their rivals in the face of escalating industry-wide rivalry. As a result, AIaaS developments are set to take center stage in the cloud computing space.
Focus solely on managed services
As more businesses opt for AI services tailored to a specific operation, process or usage, managed services have come to dominate the AIaaS industry. Companies that provide AI-based contract interpretation services for legal endeavors could serve as an example of this. Some financial institutions have partnered with companies that provide end-to-end exception management services. Similarly, leading technology companies such as IBM are working with telecoms giants such as Samsung, Nokia and Cisco to provide end-to-end managed services that increase automation and improve value for consumers and organizations.
The evolution of microservices
Businesses (large and small) are likely to acquire AI microservices as AI permeates most industries. These microservices deliver AI as a collection of independently deployable services that are specialized to meet specific business needs. Microservices address a number of key issues, including:
- Flexibility in solution development
- Acceleration of AI capabilities
- Simplify decision making
- Encouraging rapid digitization
- Add bot stores
Large organizations can purchase pre-prepared and pre-built AIaas bots to perform monotonous tasks. These include chatbots that use natural language processing (NLP) algorithms to extract linguistic patterns from human interactions and offer solutions based on the patterns found. This structure allows customer service agents to focus on difficult and important tasks without having to respond to every customer.
Develop additional APIs for computers
Any type of program, whether new or old, can benefit from adding new features thanks to APIs. To increase their ROI, businesses simply need to identify the type(s) of AIaaS capabilities they need. Once the features are complete, the business can talk to an AI provider, purchase the AI package and start using it immediately. Smaller updates or patches can be applied as needed. Speech recognition, emotion recognition, NLP, language translation and computer vision are examples of common API services.
Utilize ML tools and services
ML frameworks are used by programmers to create a unique AL model. These data models can identify trends in current data sets (such as customer data) and apply what they have learned to predict future events (such as sales, market expansion, and revenue). The USP of ML frameworks is that they don’t require huge amounts of data to work, or to work properly. As a result, the frameworks are suitable for companies of all sizes, from tiny start-ups with limited access to data to huge corporations that benefit from big data.
Develop internal basic skills.
To prevent critical data from being compromised, AIaaS requires regular collaboration between the AI service provider and the subscribing organization. These integrated systems will require regular maintenance and upgrades to keep vulnerabilities (internal and external threats) under control. As a result, organizations need to train staff who work with critical systems to keep them secure online. To operate effectively with AIaaS, it will be necessary for all employees to know, understand and practice security procedures. This will help prevent network intrusions and vulnerabilities.
The future of AIaaS
Artificial intelligence (AI) is a pioneering field of computer science that is prepared to become a vital component of various future technologies such as big data, robots, and the Internet of Things (IoT). In the following years, it will continue to be a technical pioneer. Artificial intelligence has moved from science fiction to reality in a few years.
Machines that assist people with intelligence exist in science fiction films and real life. We now live in a world of artificial intelligence that was previously only a story. We use AIaaS in our everyday lives, which has become a part of our lives. Everyone utilizes artificial intelligence regularly, from Alexa and Siri to chatbots.
AIaaS is rapidly developing and evolving. Getting AI to this level has taken several years and a lot of hard work and contributions from numerous individuals. As a revolutionary technology, AI raises concerns about its future and influence on humans.
Most frequent questions and answers
AIaaS stands for Artificial Intelligence as a Service. It refers to delivering artificial intelligence (AI) capabilities through a cloud-based platform or application programming interface (API). This allows users to access advanced AI tools and technologies without investing in expensive hardware or developing their own AI capabilities.
Some of the benefits of using AIaaS include increased efficiency, improved accuracy, and reduced costs. AIaaS can automate routine tasks, identify patterns and insights, and provide real-time analysis of large volumes of data. In addition, AIaaS can be scaled up or down as needed, allowing organizations to adapt quickly to changing business needs.
AIaaS offers various services, including natural language processing (NLP), computer vision, speech recognition, and machine learning. These services, such as chatbot development, image recognition, or predictive analytics, can be customized to meet specific business needs.
Semantic segmentation is a type of image labeling that involves labeling each pixel of an image with a corresponding class label. It is used in applications such as medical imaging and satellite imagery analysis.
AIaaS providers take security seriously and offer various security measures, including encryption, access control, and network security. However, it is still important for users to provide their own security measures, such as implementing strong passwords and ensuring that data is properly backed up.
The cost of AIaaS varies depending on the provider, the services required, and the amount of usage. Some providers charge a flat fee for access to their platform, while others charge based on the amount of data processed or the number of API calls made. It is important to carefully consider pricing structures and compare providers to ensure the best value for your organization.