What is Artificial Intelligence as a Service (AIaaS) in the Tech Industry?



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What is Artificial Intelligence as a Service (AIaaS) in the Tech Industry?

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AIaaS stands for Artificial Intelligence as a Service, which refers to businesses that offer ready-made AI solutions.
Artificial intelligence as a service (AIaaS) is the third-party outsourcing of artificial intelligence (AI). With AI as a service, companies and individuals can experiment with AI for various purposes without large initial investments and with lower risks.

AlaaS is being used in many sectors like — Healthcare, Finance, Aviation, Education, and other sectors. Adopting the AlaaS technology has become a primary need for various data-driven industries.

With complete scalability and reliability, AlaaS is one solution that caters to the needs of a data-driven industry. More than 75% of commercial applications will use AI by 2021, according to a prediction.

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Artificial Intelligence as a Service: What is it?

AI as a Service is a tool of Artificial Intelligence that enables companies to integrate and implement different AI techniques at a low cost. The concept of ‘As a service’ refers to software that works on a network relying on Cloud computing.

AlaaS can be bought from any third-party vendor, and after modifying it — used according to your needs. Artificial Intelligence is most commonly used by companies to improve their core business functions. Data Analytics and Artificial Intelligence can be used by companies that want to understand their target audience and create better products.

Artificial Intelligence as a service offers comprehensive AI solutions for startups and mid-level companies that don’t want to build their AI systems from scratch. By using AIaaS, companies can focus on their core business functions and benefit from AI without hiring Machine Learning experts.

With AlaaS, the companies can decrease the risk of investment and increase revenue. AlaaS allows different companies to leverage the power of Artificial Intelligence for data analysis, automation, and customer service needs. Companies can decide the AIaaS services they need by comparing the costs and features of the service.

AI as a Service bears witness to many attractive concepts in business. By adapting AIaaS — there will be a growth of the business ecosystem. Working with a new tech stack will give a boost to your business.

It helps to increase the accessibility of Artificial Intelligence in business core concepts. Implementation of AI in business is relatively challenging. However, with cost reduction, you can use the money in other business functions.

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Pros and Cons of AlaaS:

Pros of AIaaS:

Companies are often reluctant to use Artificial Intelligence as a tech stack in their business operations. But AlaaS works on a pay-as-you-use model. On integration with the service, half the expense is reduced. In addition, companies can buy specific plans for AIaaS at a fixed rate — it works like the hosting plans for a website.

You don’t have to buy all the complex AI specifications and features, as you need to pay for what you use. In addition, artificial intelligence requires power in the application, but AlaaS does not require the same power as it works in a short amount of time.

You don’t need software developers to implement the AlaaS technology. The companies that offer the services have pre-created packages of AI services available — National Language Processing, Computer translation, Speech recognition, and vision.

A company can buy and change the features of the packages according to their need. But if the company decides to adopt AI solutions from scratch, it may require trained experts.

With AIaaS, companies can scale up their projects as required. This is because corporate demands keep changing over time. With scalability options, the companies can focus on other business functions. Most AlaaS vendors offer the scalability feature, so it’s an advantage.

AI infrastructure is expensive, and companies need to invest heavily in AI tools for integration. As a replacement for this, you can implement Artificial Intelligence as a Service as they are available with minimal costs from third-party vendors.

But, this is useful only if your company does not have the AI infra as a core business. An advanced infrastructure provides better tech advantages and enables better decision-making. AI infra as a technology requires high-speed GPUs and parallel machines. AIaaS integrates based on your company needs with improved efficiency.

Cons of AIaaS:

Companies that buy AIaaS from the vendors can only access the service but not all the features. It works like a black box, where the companies have access to input and know about output. How the result is obtained and which AI algorithm is used for the output can be tricky.

Companies are not aware of how the data AI processes for output, which lacks transparency. There is also a security issue that leads to misunderstandings between the AIaaS vendors and the company buying the service. Different AI models need supervision in the implementation process.

  • Increased reliance on third-party vendors

AIaaS implies that most companies rely on their service vendors to provide them with software. AIaaS works on this pattern, so increased reliance on the vendors can be frustrating. Companies may gain from the minimum investment, but they suffer due to time lag or miscommunication by the vendors.

If there is no proper communication, it may disrupt the business functions. Companies need to check whether access to AIaaS is cost-effective before working on the new AI tech stack. Accessing the AIaaS services for a long time may increase the budget. Increased reliance is a drawback.

Companies will need to share the data with the AIaaS service providers for the adoption of technology. Its essential as Data Analytics and Artificial Intelligence depends on the quality of data to obtain different services. However, it also means that the company data isn’t secure, and a company may need to implement extra measures for security and data transit between different servers.

The transmission of data ensures that the data isn’t stolen or is tampered with. It depends on the quality of data and service requests. Due to security risks, most companies don’t want to implement AIaaS services.

AlaaS types:

  • Digital Assistants and Chatbots

Digital Assistance and Chatbots use Natural Language Processing algorithms. They learn conversations from human beings and emulate the language patterns. Chatbots technology is widely used in the customer service industry, as it frees up customer service employees.

You must have seen it — on websites that automatically start with the chat option the moment you visit the website. The use of chatbots and digital assistance is predominant in the healthcare and banking sector. Chatbots are in high demand for customer care and marketing goals. A business can use chatbots 24/7 for customer service and increase sales.

  • Machine Learning Functions and Cognitive Computing API

Most developers use ML and AI tools to build new models for AIaaS. Machine learning technology works with big data, but companies use the services in other processes. The frameworks provide features that help to build scalable machine learning tasks. API is a medium through which services can easily communicate with each other. It allows tech developers to add different services into applications.

The best part is; it doesn’t require any code. The most common options for API include — Computer speech and vision, Emotion Detection, Knowledge mapping, translation, and Natural Language processing.

Vendors offering Artificial Intelligence as a Service (AIaaS)

Microsoft allows you to build and deploy different Machine Learning models using Microsoft Azure. The Azure cognitive function enables other companies to search for patterns in their content. They use built-in Artificial Intelligence capabilities with a Cloud search option that allows a company to scale better.

The Azure cognitive service provides functions like — speed, embedded vision, and decision-making abilities that help companies make profits. In addition, you can integrate with Microsoft Azure technology without the need for machine learning in all your applications. With Azure Artificial Intelligence, you can make your business come alive. It lets you create innovative AI solutions that accelerate development.

Amazon Web Services provide a pre-trained AIaaS that helps the company personalize its experience for its customers. The service helps to create accurate forecasting models, video analysis, text analysis, and other services.

Amazon Sage Maker is a tentatively new service that helps data scientists — build and deeply differentiate machine learning models without getting into the depth of the machine learning process.

Amazon Web Services have successfully bought the AIaaS offerings to many companies across the globe. As a result, you can start building your applications using different AWS models.

Google Cloud offers enterprise-grade sharing capabilities that include the core technology of Artificial Intelligence. Companies can use the AI building blocks and tools of Google Cloud to create different applications.

Google Cloud allows companies to add technologies like — Natural Language Processing, Speech recognition, Computer vision, and Translation. The Cloud model allows the developers to work on expertise that doesn’t require training which saves time. In addition, the models are customized based on company needs.

Future of AlaaS

Artificial Intelligence as a Service is rapidly making its place in the IT industry. There are many benefits of AIaaS, which give startups and mid-size business companies an option to leverage AI technology and save costs.

However, with certain drawbacks, there is room for improvement in the coming years. While the technology is new, significant players and companies will surely adapt it as an experiment. AI is a core concept of AIaaS, and more companies will harness the power of the technology. The flexibility the service offers is unmatched.

Conclusion,

Artificial Intelligence as a Service is used in many industries, however, its implementation is still a challenge. But instead of using AI infrastructure, companies can use AIaaS to meet their core business needs.

Third-party vendors provide the services at the required need, and they can be tweaked to meet the needs of business. As a result, AIaaS is an effective technology that companies can use with little risk involved. Building an AI application model can be complex, but AIaaS provides a feasible option to overcome the complexity and get started with AI.

The four most prominent players — Google, Amazon, Microsoft, and IBM offer AIaaS services, so you can start using the service according to your business need.

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