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.
In this article:
- ~Bots and Digital Assistants
- ~Application Programming Interface (APIs)
- ~Machine Learning (ML) Frameworks
- ~No-Code or Low-Code ML Services
Types of AIaaS
Bots and Digital Assistants
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.
Application Programming Interface (APIs)
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 (ML) Frameworks
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.
No-Code or Low-Code ML Services
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.
Top AI as a Service Companies
Here are popular Azure AI services:
- Cognitive Services—provides APIs for content moderation, anomaly detection, and more.
- Cognitive Search—lets you add AI-powered cloud search into mobile and web applications.
- Azure Machine Learning (AML)—enables you to build, train, and deploy ML models from cloud to edge, supporting the service’s models and custom AI development.
- Bot Services—offers a serverless chatbot service that can scale on demand.
Amazon Web Services (AWS) offers various AI and ML services, including:
- Sagemaker—a fully-managed service for machine learning in the cloud. It enables building and training machine learning models and deploying them to a production-ready, hosted environment.
- Lex—provides features for building chatbots and virtual agents and integrating with new and existing applications. Lex includes natural language capabilities, such as speech recognition, natural language processing (NLP), and speech-to-text conversion.
- Polly—includes features for creating speech-enabled applications and products. Polly helps convert text into spoken audio.
- Rekognition—offers computer vision capabilities services, including algorithms pre-trained on datasets curated by Amazon or its partners. You can also use algorithms that you have trained on a custom dataset.
Google Cloud offers various cloud AI services, including:
- AI Platform—provides capabilities to help you build, deploy, and manage ML models at scale.
- AI Hub—this hosted repository offers plug-and-play AI components, including out-of-the-box algorithms and end-to-end AI pipelines.
- Conversational AI services—include various services, such as Text-to-Speech, Speech-to-Text, virtual agents, and the Dialogflow platform to help create conversational actions across applications devices.
AI as a Service: Benefits and Challenges
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:
- Speed—AIaaS is the fastest way to deploy AI-based technologies. AI use cases differ significantly, and it’s not always practical for a company to build and maintain an AI tool for each use case. Customizable solutions are especially useful, as organizations can tweak the service according to their business constraints and needs.
- Stability—AI solutions often must handle extreme data conditions in the production environment, including unstructured and noisy data. Integrated AI technologies and expertise allow organizations to achieve stability and robustness.
- Long-term value—achieving production is often difficult, but maintaining production is also important to ensure the model stays on track in changing data conditions. Maintaining an AI model in production is expensive and includes version control, monitoring, noise detection, and updates. AIaaS eliminates the need to maintain the AI model in-house.
Some challenges of AIaaS include:
- Security—AI solutions require large amounts of data, which the AIaaS vendor must access. If this data includes sensitive or personal information, it could expose companies to third-party risks. Likewise, it is important to secure data access, transit, and storage.
- Third-party reliance—working with a third-party vendor entails reliance on that vendor to maintain security and provide relevant information. It can result in lags when resolving issues.
- Transparency—AIaaS provides a service, not direct access to an AI system, so the customer has no visibility into the system’s inner workings (i.e., the algorithms).
- Data sovereignty and governance—some industries restrict data storage in the cloud, precluding the use of certain AIaaS offerings.
- Unforeseen costs—long-term and unexpected costs often spiral out of control, especially when companies purchase services requiring training or hiring new staff.
AI Infrastructure Automation with Run:ai
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:
- Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
- No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
- A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
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.