AI Platform as a Service and AI as a Service
With the global AI, industry expected to reach $309.6 billion by 2026, artificial intelligence (AI) technologies continue to be a popular trend in software development. It makes sense for cloud service providers (CSPs) to offer AI-powered services for text-to-speech conversion, object detection in video, and celebrity facial recognition.
Some of these service providers have already started to offer more sophisticated AIPaaS solutions. These tools are designed to make it easier and faster for developers to create machine learning (ML) and deep learning (DL) based products. AI can be applied to business in a variety of ways, two of which are AI platform as a service and AI as a service. While there isn't much of a name difference between the two, distinguishing them is still necessary. In this article, we'll take a closer look at AI Platform as a Service and AI as a Service and how they differ.
What is AI Platform as a Service?
For the purpose of creating, testing, and deploying AI-powered capabilities, AI PaaS is a combination of AI and ML platform services. By definition, PaaS services aid users in creating, deploying, and managing applications, so AI PaaS can assist businesses in developing AI-based solutions without having to invest in and improve infrastructure.
When we use the word "end-to-end solution", we typically mean cloud platforms that enable enterprises to use the AI-based services they require on a pay-per-use or pay-per-service basis. Such platforms frequently incorporate managed sub-services and third-party APIs to give comprehensive intelligent solutions that can function right out of the box.
For developers who are creating, improving, and launching AI-focused programs, AIPaaS is useful. Platform as a Service (PaaS) and Artificial Intelligence (AI) are combined to operate cloud platforms and deliver more user-friendly, pre-built, comprehensive, and automated AI solutions.
What is AI as a Service?
A cloud-based service that enables the outsourcing of artificial intelligence (AI) is known as Artificial Intelligence as a Service (AIaaS). AIaaS enables people and organizations to experiment with AI and even put it into production for large-scale use cases with low risk and low up-front costs. It makes trying out different public cloud platforms, services, and machine learning algorithms possible because it's easy to get started.
The ability to bundle specialized hardware and software with the service is another key feature of AIaaS that cloud providers can offer. For example, computationally intensive computer vision applications require hardware such as graphics processing units (GPUs) or field-programmable gate arrays (FPGAs). For many organizations, the cost of purchasing and maintaining the technology and software required to get started with AI can be prohibitive. With AIaaS, a business can purchase AI services along with all the infrastructure required to run them.
AI PaaS key components
AI platform as a service and AI as a service are all about processing massive amounts of data, which requires a lot of computing power. For this reason, some AI service providers include infrastructure resources, compute resources, and virtualization capabilities, similar to the normal PaaS approach. There needs to be a place to store all the large amounts of data needed to develop AI systems and train ML models. For this reason, data storage resources are a common component of both AI Platform as a Service and AI as a Service offerings.
There are two elements that differentiate AI PaaS from a standard PaaS model: AI APIs and pre-trained ML models.
Pre-trained Machine Learning models
Machine learning models can take many years to build and perfect. They require a lot of technical expertise, time, and resources. What if you can't build a machine learning model from scratch but still need one? In this situation, you use models that have already been trained.
To ensure ease of use and functionality, these solutions often include pre-trained machine learning models and connectors. They provide access to sophisticated machine learning algorithms and at least some no-code or low-code capabilities, but a developer is often required for AIPaaS solutions. These models often provide capabilities such as:
Predictive modeling
Image processing
Text and speech analysis
Simplified computation
Accurate object recognition
Motion detection
Data extraction
AI APIs
Application Programming Interfaces (APIs) are the foundation of AIPaaS, and much of the usage of AIPaaS is through APIs. Cloud computing platforms use APIs to facilitate communication with AI models. There are many different types and sizes of APIs, and certain platforms, such as Microsoft Azure, Vertex AI, and Amazon Web Services (AWS), offer best-in-class APIs for the largest AI solutions. Here are some typical AI API features:
Analyzing large amounts of data
Text analysis and natural language processing (NLP)
Prediction
Semantic search
Computer vision
Text-to-Speech Conversation
Chatbots and virtual assistants
Language Translation
Fraud detection
Benefits of AI Platform as a Service
Using a complete platform to build AI solutions benefits organizations in a number of ways.
Faster and cheaper development
AIPaaS packages provide a wide range of practical tools and services that streamline every stage of application development. As a result, developers have more time to focus on other application components, while ML models can be built, trained, and tested more quickly. Organizations do not need to invest time and resources in purchasing and maintaining expensive equipment. In addition, you can manage your budget efficiently thanks to the standard pay-as-you-go strategy used by the majority of CSPs.
Ready-to-use algorithms
Another key benefit of using AIPaaS solutions is that you have access to a secure, pre-built infrastructure and environment. You don't need to build a sophisticated infrastructure because, as mentioned, all your tasks are performed in the cloud. This is less risky and costs less time and money. Developers can use pre-built AI, ML, and DL algorithms to build, release and maintain applications. With the time they save, they can then work on improving or creating new products.
Excellent scalability
Most companies use AIPaaS solutions to learn how they work and how they'll benefit. AIPaaS is a highly scalable technology, regardless of where you start. You can grow your business quickly without the need for infrastructure resources or large technical staff.
Comprehensive analytics and insights
Analyzing your data and processes and using the resulting insights to improve your judgments is the first step in making intelligent business decisions. With AIPaaS, you can do this. To gather key insights, identify trends, and develop connections between different data sets, AI analytics in business intelligence use machine learning algorithms and methods. This is very helpful for business analysts who want to make in-depth and sophisticated assessments.
Challenges of using AIPaaS
While AIPaaS has many benefits, it also has some drawbacks. Business users may face a number of challenges in the areas of customization, security, and data quality. Let's take a closer look at these obstacles.
Compliance issues with security
The cloud hosts 60% of all business data, but is it safe? AIPaaS raises significant data governance, privacy, and security issues that shouldn't be ignored. An already established workspace can be an occasional temptation for malicious intent. Simple data breaches can leave you vulnerable and damage your business. AIPaaS also forces you to rely solely on your provider's security architecture.
Ensure your service provider has the essential security rules and standards in place and they adhere to them before you choose them.
Locating reliable data sources
For reliable results, most AIPaaS solutions rely on high-quality data. Your data can now be in any format and size. It is likely to have a very hollow structure. It's challenging to handle and manage unstructured data, which is growing at an astonishing 55-65% per year.
Identifying reliable data sources and ensuring that the data you provide to AIPaaS systems are clean and usable are both difficult tasks. However, smart decisions can overcome this barrier.
Top AI Platform as a Service Providers
Amazon, Google, IBM, and Microsoft are among the leading cloud computing service providers for AI. They provide high-quality, out-of-the-box AI tools for programmers and data scientists to build world-class ML models.
Amazon
With Amazon Web Services (AWS), organizations can focus on building and maintaining their applications because AWS provides a highly flexible PaaS architecture that takes care of maintaining the underlying infrastructure.
Amazon also provides access to pre-built tools and services, so developers do not have to worry about finding resources, maintaining software, capacity planning, patching, or other difficulties associated with running applications. An AWS AI service focused on machine learning and deep learning is Amazon SageMaker.
IBM
AI Platform as a Service and AI as a Service Platform IBM Watson is known for providing practical tools and services to facilitate the adoption of AI. To maximize business benefits and encourage the correct use of AI, the company as a whole focus on affordable and widely available solutions. IBM Watson Studio is one of the most popular AIPaaS systems.
Google is well known to the general public and to artificial intelligence researchers and developers. A variety of cloud computing services are available through Google Cloud Platform (GCP), which is hosted on Google's infrastructure. GCP outperforms many competing platforms in terms of speed, scalability, and quality of infrastructure.
The pinnacle of what GCP has to offer is Vertex AI. It is a comprehensive ML platform for developing, deploying, and scaling useful AI models.
Microsoft
Microsoft never hesitates to develop innovative solutions. Azure AI's AIPaaS platform is a perfect example of its inventive approach. Like other AI Platform as a Service and AI as a Service solutions, Azure AI provides a single platform for deploying, operating, and managing AI products and services. However, building your own AI solutions gives developers and data scientists more flexibility and power.
Through simple API calls, Azure AI gives customers access to superior AI models for speech, language, decision making, and vision. It's an all-inclusive platform for building unique machine learning as a service model using popular third-party applications such as PyTorch, Visual Studio Code, and Jupyter Notebooks.
Deep Cognition
It is a platform dedicated to automating the deep learning process with almost no coding. The platform provides a graphical workflow designer to feed the data, define the flow and continuously train the model to improve its predictive capability. With a focus on deep learning, the platforms are pre-configured to perform the desired tasks and have the right tools to quickly move the model from study to production.
Advantages:
Visual design tools help you to have more clarity in your workflow
AutoML features help train models automatically with minimal effort
Ready to deploy the server for your trained AI model
Dataiku
It is an enterprise-ready back-platform that provides all the tools that allow business analysts, data scientists, data analysts, and AI developers to work together. It provides a sophisticated platform to allow tasks through a defined pipeline, giving each user the ability to perform their respective tasks.
Advantages:
The platform supports most popular programming languages for data science
Provides built-in data visualization tools for easy data visualization
Famous machine learning libraries including Scikit-learn, MLLib, XgBoost
DataRobot
It is a platform that focuses on providing large-scale data for automatic model tuning. It is a premium platform with over a hundred open-source libraries pre-configured for use. This platform has a self-learning and analytical data modeling algorithm and can receive your data, communicate based on desired predictions and build a model ready for prediction.
Advantages:
Intelligent data assimilation engine that can learn and build models
It helps you to compare and visualize the result of each model
After comparing, you can easily model through the platform itself
What points to consider when choosing an AI PaaS service?
As you know, AI service providers offer similar sets of AI capabilities but still have differences and limitations. Therefore, choosing the right AI PaaS provider should be tailored to your product needs. In this section, we have mentioned some points that you should pay attention to:
Data quality: It doesn't matter which AI service provider you choose, because the performance of AI features is completely related to the quality of the processed data. Make sure you do your due diligence in data preparation and use reliable databases with quality content, but make sure you check the results later as these tools vary depending on the type and quality of input data.
Compatibility of technologies: Consider the set of tools, services, frameworks, and programming languages supported by a particular AI PaaS. The more frameworks across tools match the experience and needs of the project, the easier it will be for your development team to work with the platform.
API Availability: Most AI service providers offer APIs to integrate AI capabilities into your app. With the help of APIs, you can introduce new artificial intelligence functions into your software faster and with less effort. Before you start developing custom APIs, be sure to check out your AI PaaS provider's offerings for these solutions.
Differences Between AI Platform as a Service and AI as a Service
AI Platform as a Service and AI as a Service are both cloud-based solutions for developing and deploying artificial intelligence (AI) technology, but they differ in scope and functionality.
AI Platform as a Service (AIPaaS) is a cloud-based platform that provides developers and data scientists with the tools and resources to design, train, and deploy their AI models and applications. AIPaaS often includes:
Software development kits (SDKs).
Machine learning frameworks.
APIs.
Other development tools allow users to customize and optimize AI models.
AIPaaS platforms are designed to be customizable and adaptable, allowing developers to deploy AI models using their preferred programming languages and tools.
On the other hand, AI as a Service (AIaaS) is a cloud-based service that provides pre-built AI models and applications that can be easily integrated into existing business processes and applications. AIaaS often includes pre-built models for operations such as natural language processing, image recognition, and predictive analytics. These models are accessible through APIs, so developers can easily integrate them into their applications.
In general, the main difference between AI Platform as a Service and AI as a Service are that AIPaaS provides a platform for developers to build and deploy new AI models. In contrast, AIaaS provides pre-built AI models that can be readily incorporated into existing systems.
Is AIPaaS better than AIaaS?
AIPaaS and AIaaS are not in direct competition with each other. Both are used in different conditions and have many differences in terms of performance.
AIPaaS is a much more technical solution, so if your business is building a complete infrastructure, it makes more sense to use this solution. This solution and its providers in most cases provide at least some code-free functionality, but it is misleading to position AIPaaS as a purely no-code or low-code solution. Low-capacity developer resources are required to bring this solution to its full potential.
AIaaS, on the other hand, can be completely code-free. AIaaS solutions are more suitable for small and medium-sized businesses without a team of developers but still want to use the wonders of artificial intelligence. You can consider AIaaS as an off-the-shelf solution for you and your teams.
Why do companies choose AIPaaS?
AIPaaS brings together middleware, operating systems, development, and deployment in an abstracted environment, separate from an organization's infrastructure. This enables companies to more easily migrate existing on-premises applications to the cloud, making it faster and easier to scale resources as their user base grows. It also provides the ability for the organization to efficiently develop and deploy customized applications.