Artificial Intelligence as a Service (AIaaS) is a cloud-based service offering artificial intelligence (AI) outsourcing. AIaaS enables individuals and businesses to experiment with AI, and even take AI to production for large-scale use cases, with low risk and without a large up-front investment. Because it is easy to start with, it makes it possible to experiment with different public cloud platforms, services, and machine learning algorithms.
Another important aspect of AIaaS is that a cloud provider can offer specialized hardware and software, packaged together with the service. For example, computer vision applications are computationally intensive and rely on hardware such as graphical processing units (GPUs) or field-programmable gateway arrays (FPGA). Buying and operating the hardware and software needed to get started with AI can be prohibitive for many organizations. With AIaaS, a company can get the AI services together with the complete infrastructure needed to run them.
This is part of our series of articles about machine learning in the cloud.
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Digital assistants are a popular type of AIaaS. They allow companies to implement functionality like virtual assistants, chatbots, and automated email response services. These solutions use natural language processing (NLP) to learn from human conversations. They are widely used in customer service and marketing applications.
AIaaS solutions provide APIs that allow software programs to gain access to AI functionality. Developers can integrate their applications with AIaaS APIs with only a few lines of code and gain access to powerful functionality.
Many AIaaS APIs offer natural language processing capabilities. For example, they allow a software program to provide text via the API and perform sentiment analysis, entity extraction, knowledge mapping, and translation.
Other APIs provide computer vision capabilities—for example, they allow an application to provide a user image and perform complex operations such as face detection and recognition, object detection, or in-video search.
Machine learning frameworks are tools that developers can use to build their own AI models. However, they can be complex to deploy, and do not provide a full machine learning operations (MLOps) pipeline. In other words, these frameworks make it possible to build an ML model, but require additional tools and manual steps to test that model and deploy it to production.
AIaaS solutions offered in a platform as a service (PaaS) model provide fully managed machine learning and deep learning frameworks, which provide an end-to-end MLOps process. Developers can assemble a dataset, build a model, train and test it, and seamlessly deploy it to production on the service provider’s cloud servers.
Fully managed machine learning services provide the same features as machine learning frameworks, but without the need for developers to build their own AI models. Instead, these types of AIaaS solutions include pre-built models, custom templates, and no-code interfaces. This is ideal for companies that do not want to invest in development tools and do not have data science expertise in-house.
Here are popular Azure AI services:
Amazon Web Services (AWS) offers various AI and ML services, including:
Google Cloud offers various cloud AI services, including:
The AIaaS delivery model offers an affordable way for organizations to run AI solutions without building or maintaining an AI project. AIaaS solutions are flexible, scalable, and easy to use, enabling companies to implement customized AI services.
Some real-world benefits of AIaaS include:
Some challenges of AIaaS include:
Run:ai automates resource management and orchestration for AI infrastructure. With Run:ai, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:ai:
Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:ai GPU virtualization platform.