The founding father of Artificial Intelligence (AI) John McCarthy, Allen Newell, Herbert A. Simon, and Marvin Minsky gave an outstanding gift to the technology industry in the 1960s.
With years, we were introduced to the fair share of Artificial Intelligence products, gadgets, and apps. The use of AI is increasing with time and we are slowly moving towards the economic growth where AI as a Service is going to be a common thing.
Already millions and trillions of money are invested in the projects for Artificial Intelligence integration. Now, AI can be used as an integral part of the business and take it to the new heights of success.
Not only this, but companies also invest in AI application development. As per Markets and Markets Research about AI as a Service, it has a lot of scopes to grow in the market and thrive. To give you a proper insight, here is everything:
With this said, many people now ask the question “Why it’s time for AI as a Service for businesses?”
AI has faced a number of challenges over time that has made it difficult for AI to showcase its actual potential in the market. To name a few:
- Data issues
- Lack of infrastructure
- Weak vision at the top
- Lack of expert talents
- Learning curve
However, we all know that digital transformation is never a cakewalk and needs a lot of time, effort, and money to evolve. Especially when it is about implementing technology in the real world.
Now before moving ahead, let us understand what AI as a Service is?
What is AI as a Service?
Just like Software as a Service, Platform as a Service, and Infrastructure as a Service; AI as a Service is the services offered to the business by the third-party. The third-party outsource their artificial intelligence services to others in the form of AIaaS.
This makes it easy for the business to enjoy the AI as a Service without investing a lot of money in it. It is predicted by the International Data Corporation that by 2021, the reach of the AI as a Service will cross the bar of 75%.
The fact is that companies depend upon the latest technology to grow and adding AI is not a surprise. It not only helps in automating the production but targets the potential audience. As a result, it increases the productivity and profitability of the companies.
AIaaS follows the common approach of all “as a service” options including:
- Keeping transparency with costs
- Focusing on core businesses instead of becoming an expert in machine learning and data
- Using data to increase business efficiency and benefits
- Lover investment risks
- Working on the business flexibility and dynamic model with major strategies
Now let us understand it’s beginning to understand what looked like during the initial stage and how much it has evolved over time.
How AIaaS began?
Microsoft, Amazon, Google, IBM, and other technical giants along with Fataiku and BigML startups worked on AI. But its evolution was much earlier than that of the Blockchain.
Since the 1960s, AI is growing at a rapid rate in terms of networking and graphics processing units. As a result, big data has evolved to become a major part of the companies that started to find out the ways to make the process easier to understand.
The increase in data gives a kickstart to many other technological advancements such as IoT (Internet of Things) that depends widely on real-time decisions. Amidst this, AI is growing remarkably becoming a differentiator and prerequisite for the cloud providers.
Ai works on several algorithms to complete a task and facilitate the tasks using the datasets. However, as mentioned above, it was costly to build technical aspects and infrastructure for the AI.
This gives rise to the AI as a Service that has minimized the cost and development time for the companies. AIaaS or AI off the shelf product algorithm is written in a manner that analyzes the data and works on the tasks accordingly.
Vendors of AI as a Service
AIaaS may be a new concept for many but it is thriving in the market for a longer period of time. This basically depends upon the AI as a Service business model that is followed by the giants that offer this service. The major advancement in the AIaaS field is done in the form of:
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#1 Amazon Web Services
AWS is the on-demand cloud computing platform that has been in the market since 2006. It helps in personalizing the customer experience, perform video and image analysis, create a precise forecasting model, use natural language processing for text analysis, and so on.
In addition to this, there is Amazon SageMaker that is efficiently used by data scientists and developers to train, build, and deploy the models based on machine learning. The best thing about this product is that it doesn’t require any heavy lifting that is necessary when models are developed from the initial phase.
#2 Google Cloud
It is an AI hub solution that offers a number of features to companies such as end-to-end AI pipelines with the sharing capabilities. The companies can use the Google Cloud AI building blocks can help in including several technologies such as Speech recognition, Natural Language Processing, Computer Vision, and Translation.
In addition to this, developers with limited experience in machine leading can also use Cloud AutoML easily that have trained ML models as per the requirements of the company.
#3 Microsoft Azure
The Microsoft-owned tool is used to train, build, and deploy the ML models with the help of ONNX, Azure Databricks, and Azure Machine Learning. The companies also use Azure Cognitive Search to discover the relationships and patterns in the datasets. They work on the AI capabilities built-in the tool that is used in the cloud search service.
With the help of Azure Cognitive Services, it is possible for the companies to speech, make decisions, and embed vision in the application. The best thing is that the developers don’t have to be an expert in Machine Learning. s
#4 IBM Watson Cloud
This software allows the companies to make precise predictions, obtain optimized solutions, and automate the business processes & decisions. In addition to this, there are other types of AI as a Service under Watson application including Watson Natural Language Understandings, Watson Speech to Text, and Watson Assistant.
In addition to this, this tool gives an insight into data extraction, the use of AI in Fintech services, and enhances the customer experience. It also includes AI for cybersecurity to manage the response time and analyze the risk data.
Types of AI as a Service
To understand the whole concert of Ai off the shelf, we need to understand the different types of AI as a Service. These includes:
#1 Cognitive Computing APIs
It is commonly known in the world of mobile app development that API or Application Programming Interface is used to add a specific service or technology to the code instead of writing the code from the initial stage. When it comes to AI as a Service, APIs are used with different options such as translation, computer vision, computer speech, NLP, emotion detection, search, and knowledge mapping.
#2 Digital Assistance & Bots
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Chatbots are the most common form of Artificial Intelligence that we all are very much aware of. It uses the NLP or Natural Language Processing algorithms that use the datasets to interact with humans and answer them to solve the queries.
The digital assistant or Bot is mainly used to solve the issues of customers using language patterns and eliminating the human workload. The human force can be used for the much-complicated tasks that are not solved by the AIaaS.
#3 Machine Learning Services
This is the best option that developers opt for when it comes to built machine learning frameworks. It helps in reaching the capabilities of machine learning using pre-built models, templates, and drag-drop tools. These are used to develop customized machine learning frameworks for assisting developers.
#4 Machine Learning Framework
The mobile app developers use the framework to easily build models with the help of datasets that are already available. Big Data and Machine Learning are connected with each other that uses the framework easily. The best thing about the ML framework is that it doesn’t use the big data environment.
Major Factors Added In AI as a Service
AI as a Service model is extremely popular in the market due to its potential for driving business. The unmatched efficiency of this type of ‘as a Service” is extremely remarkable that drives the owners to increase the business value.
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In 2018, the AI-based software revenue was around $9.5 billion whereas, by 2025, it is expected to reach $118.6 billion. The businesses are now opting for the AI as a Service business model that gives a competitive edge. Every business knows the value of data but doesn’t know how and where to use it.
However, what business owners know is that machines are more than capable to cover the aspects that researchers might miss out on. But the machines included complex algorithms to build machine learning and AI solutions as per the business requirements.
It is essential to work with expert data scientists to develop the solution that compiles the data to deliver promising results. What makes this approach not ideal? The total money required to develop the model makes it difficult for the organization to built-in-house solutions.
With this out, let us give you an insight into how AI-based solutions have gained momentum. As a result, the companies started to depend upon the AIaaS solutions as per their sophisticated models and vertical industries.
These solutions are used to refine, access, and expand the data as per the business model that was indecipherable manually. The major opportunity of the AIaaS has increased with its funding rate increased up to $7.4 billion in the 2019 second quarter. And after that, the number is only increasing.
AI as a Service platform is not limited to a single platform and is growing at a fast pace. It is fostering the AI-based solution covering all the major concerns and regulation of the industries. In addition to this, it also covers up the digitization journey of the businesses to enhance user experience.
The business processes now depend widely upon the AI-based solution to decipher the data with the integration of machine learning. It also includes the adoption of machine learning in the whole work process. The technologies depend widely on the requirements of the process that businesses work upon and day-to-day operations.
Why it’s Time for AI as a Service?
Read More – Role of chatbots in enterprise
Enough explained about AI as a Service now let’s get to the point why businesses should go for AIaaS now. As it is explained above, AIaaS is the artificial intelligence software that is offered to the companies by the third-party that can be used to cover all the major requirements.
The business owners can invest a minimum amount to use the AIaaS platform for the business. The companies can get better customer insights and great efficiencies with the help of AIaaS. AI as a Service is a digital transformation for the business world that has become a necessity and not some nice-to-have technology.
AI is already a well-known technology similar to blockchain or 5G and is rapidly growing with the Internet of Things innovations. Out of all these innovations, AI is making an impact on the companies be it small-scale, medium-sized, or enterprise.
The major factor that is making companies jump into the AI as a Service is explained below in detail for you to understand.
#1 Market Demand
The surge in market demand is not hidden from anyone. Customers are expecting something new from companies every now and then. As a result, churning up the data rapidly is becoming a major part of the business world. Customer expectation is essential for companies to maintain their competitiveness. The companies are headed towards the AI-powered services to ensure that customer requirements are maintained.
The economy is slowly expanding with the optimization of the AI software in the businesses. In addition to this, the business world understands that they have to depend upon the vendors for the topnotch AI solutions that they can implement in their processes.
The dependencies upon the technologies have made businesses to partner up with the third party to move towards their common goals. More businesses are now adopting the AIaaS platform with the increase in solution providers’ number that is encouraging the economy in a drastic way.
#3 Business Users
The companies are embracing AI with its demand in society with modern technological advancement. Businesses are taking advantage of AI solutions that are becoming the next-gen tech trends. In addition to this, the AIaaS platform is setting new standards of business that potential to take the business to the next level with the help of cloud-based integration.
#4 Small & Medium Companies
The infrastructure of the AI as a Service is developed at minimal cost that is what midsize and small businesses depend upon. These businesses can’t directly compete with the tech giants that already have a well-established reputation. But with AIaaS, it is possible for them to power up to be in the market with no need for expertise.
Difference Between MLaaS and AIaaS
With this said, let us give you an overview of the AI as a Service platform and how it is different from Machine Learning as a Service. The MLaaS work on Natural Language Processing, Computer Vision, Speech Recognition, and Image & Video Analysis.
The machine learns on itself in the machine learning concept whereas Artificial Intelligence works on both application and acquisition data. The simulated data is proceeding in AI in order to solve complex problems with natural intelligence.
AIaaS broadens the whole setup of the MLaaS with the cognitive capabilities by enabling machines in a certain flow.
AI as a Service platform is influencing the whole AI conceptualization that has the ability to change the phase of the digital products. Cloud is now changing the way we worked and with AI apps that are running developers are ready for the bigger revolution.
There are a number of benefits of AIaaS that are rounding up in the market. For the early-adapters, it can change the offerings of “as a Service” while collecting the right and clean data. The random data is sorted to ensure that the right set is taken for the considerations.
AI as a Service has the potential to transform the business world and make it better as per the business pipeline.
Co-Founder and AVP Technology at Appventurez. An accomplished Android and React Native developer who is a fan of clean and optimized code, he is a passionate team builder having smart project managerial skills and has a deep love to provide end to end solution.
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