Artificial Intelligence (AI) is one of the fastest-growing technologies used worldwide. It helps companies to improve products and customer experiences, make data-driven decisions, and automate manual tasks through applications such as chatbots, text analysis software, complex predictive analytics tools, etc. According to a survey report, 97.0% of participating organizations are investing in Data initiatives, and 91.0% are investing in AI activities. Companies are actively hiring AI engineers to become data-driven and drive business growth.
However, for small to medium businesses, this can involve huge upfront costs. Herein they opt for AIaaS (AI as a service) to get the solutions right away.
What is AIaaS (AI as a service)?
AIaaS (AI as a service) is a term used to describe AI tools offered by third-party vendors through off-the-shelf solutions upon a one-time payment or subscription fee. With this, you get AI solutions for your projects without building a model from scratch.
With services offered by AI development companies, the technologies which were previously inconceivable for companies have now become more attainable. They offer intuitive APIs and low-code tools allowing users to get AI functionalities without writing a single line of code.
Popular examples of AlaaS include
- Google Assistant,
- Amazon Alexa.
Benefits of using AIaaS
Whenever a new AI solution comes up in the market from a third-party vendor or an AI development company, it gains enough attention as it serves to eliminate specific business problems. Let’s take a look at what AlaaS offers in general.
No need for specialized skills
Companies not having a skilled AI programmer can also benefit from AIaaS as there is no code infrastructure. Therefore, to set up AIaaS, there’s little need for coding or tech skills.
Fast and advanced infrastructure
Earlier, when AI was not available as a service, AI and machine learning models required powerful and fast GPUs. This restricted many small and medium businesses from leveraging AI as they lacked the resources and time to afford such a setup internally. With AIaaS, organizations can customize their model for specific AI-based tasks easily.
AIaaS helps reduce the need for non-value-added labor and with a good amount of transparency. This means you pay only per usage through its pricing model.
If we talk about other “as-a-service” platforms, they are not as user-friendly as they claim to be. With AIaaS, owners don’t face any such issues.
If your model has been initially trained to behave in a certain way, AIaaS allows you to scale it up and down when needed.
Models of AI as a Service platform
Here are different types of AIaaS used by businesses today:
The most common type of bots used to serve customers is chatbots. These chatbots use natural language processing (NLP) algorithms to learn from human interactions and emulate language patterns to offer answers to customers’ most recurrent queries. This, in turn, allows the customer service representatives to focus on more complex tasks.
Cognitive computing APIs
Application programming interfaces (or APIs) serve as a middleman to connect two services. They save developers time and include a certain technology or service without starting from the ground up.
Machine learning frameworks
Machine learning is increasingly used by companies to analyze and identify data patterns. Developers can also utilize ML and AI frameworks to create models that learn from the existing data over time. With AIaaS, it is now easy to adopt ML technologies.
Fully managed ML services
While frameworks for ML are the starting point toward the use of machine learning, with a fully managed service, developers can design a customized ML framework. This can include templates, pre-built models, drag-and-drop tools, etc.
As the name suggests, it includes labeling large quantities of data for easy management. Some use cases involve maintaining data quality, categorizing by size, and training the AI.
Classification of data involves putting data in one or more categories such as content-based, context-based, or user-based. Now, if data classification outlines and criteria are clearly defined, AI can classify data on a larger scale.
Challenges of AIaaS
Although AIaaS has several benefits, it still has challenges to counter before it blooms to its full potential. Some of the challenges are as follows:
Data privacy and security
Because of the prevalent work-from-home model setup, it has become vital for businesses to be extra cautious about data privacy and security. And, with AI and ML requiring large volumes of data to be shared with third-party providers, privacy-enhancing mechanisms and technologies can be leveraged to safeguard data..
While it may seem easy to switch between APIs, it is not true. Since each API uses different response formats, this can be cumbersome. Besides, end-to-end ML services find it difficult to switch tools since the teams need to get familiar with them. And all this contributes to vendor lock-in.
Since you start depending upon third-party services to deliver the information, it can cause an unexpected lag time.
With a third-party provider, you buy the service but not the access. So you remain oblivious of the inner workings that can sometimes create misunderstandings about data.
Companies in the banking and healthcare sector may face limitations in using third-party services due to high regulations restricting data storage in the cloud.
Costs in the long run
While AIaaS solutions are easy to set up at an affordable cost, the long-term costs could be high.
Major vendors of AIaaS
Before you choose an AI service, you should consider your goals, business size, and budget. Also, you should access your organization’s technical capabilities and the data you need to process.
Here are some of the top vendors of AIaaS:
IBM is well known for its AIaaS offerings. It hosts various AI tools to help large companies achieve the best through their data. It comes with pre-built applications like Watson Assistant (to build virtual assistants) and Watson Natural Language Understand (to perform advanced text analysis tasks).
The platform is helpful in building, training, and deploying machine learning models across any cloud.
One of the well-known AIaaS players, Microsoft Azure is Microsoft’s public cloud computing platform that offers a suite of AI and machine learning solutions for developers. Its Azure Cognitive Services lets you add different AI capabilities to your apps using APIs. Also, there is Azure Bot Service which you can customize as per your needs.
Google Cloud ML
Google Cloud ML engine is the AI platform offered by Google for helping businesses with their ML projects. Data scientists and developers working with big data significantly benefit from this platform.
With Google Cloud ML, you can train custom machine learning models for text analysis, image classification, translation, and more.
How to choose your AIaaS solution?
Before selecting the right AIaaS solution, you should ask yourself the following questions:
Is there an option to test the API?
You need to test whether the API is working at the desirable level of security.
What is the average rating of the API?
Before making the final selection, make sure to compare the results with other APIs offering the same service.
Is the API secure and reliable?
You should start by checking their SOC 2 and ISO 27001 credentials.
As AlaaS is a rapidly growing sector, it has emerged as the most suitable option for SMEs planning to bring AI to their processes. Outsourcing your AI needs from AI development services helps you leverage AI technology to improve your business processes. You can take their help for data mining and statistics, predictive analytics, text mining, and face & text recognition, to list a few. Also, the solutions offered by third-party providers are easy to set up and get you started.
While some companies may prefer developing AI software in-house, it is not viable in most cases. One may have to start from the ground up. With AIaaS, you can simply pay for the tools or services needed and get started right away. In case you want to upgrade to other premium features, you can do so with ease.
Using AlaaS services will not only optimize but also boost your business processes. This helps support the goal which a business is striving for. However, some minute flaws indicate room for growth which hopefully it would be able to cover up in coming years.
Disha Prakash is a writer with around eight years of experience writing in diverse domains. Besides, she holds a few research papers in computer vision and image processing published in international publications. In her free time, she loves to read books, do yoga, and meditate.
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