Today, almost all companies are using at least one type of ‘as a service’ offering as a way to focus on their core business and outsource other needs to third-party experts and vendors, and AI is no exception.
Artificial Intelligence as a Service (AIaaS) is the third-party offering of artificial intelligence (AI) outsourcing. It enables individuals and companies to experiment with AI for various purposes without a large initial investment and with lower risk.
AIaaS provides out-of-the-box platforms and is easy to set up, making it simple to test out various public cloud platforms, services and machine learning (ML) algorithms.
Understanding AI as a Service
Artificial intelligence as a service (AIaaS) is defined as a service that outsources AI to enable individuals and companies to explore and scale AI techniques at a minimal cost. It refers to out-of-box AI services rendered by companies to potential subscribers.
AI refers to a paradigm where computer systems perform human-like tasks by reasoning, picking up cues from past experiences, learning, and solving problems. Broadly, disparate technologies such as machine learning (ML), natural language processing (NLP), computer vision, and robotics come under the AI roof.
Artificial intelligence benefits businesses in numerous ways, right from improving customer experiences to automating redundant tasks. However, developing in-house AI-based solutions is a complex process that requires huge capital investment. That’s why businesses are openly embracing AIaaS, where third-party providers offer ready-to-use AI services.
Like software as a service (SaaS) and infrastructure as a service (IaaS), AIaaS provides an ‘as a service’ package that a third-party provider hosts. This is a cost-effective and reliable alternative to software developed by an in-house team. As such, AI becomes accessible to everyone in the corporate ecosystem.
With AIaaS, end users can harness the capabilities of AI through application programming interfaces (APIs) and tools without having to write any complex codes.
How does AI as a Service work?
AI encompasses a variety of technologies, including robots, computer vision, cognitive computing, ML models, and natural language processing (NLP).
AI algorithms are frequently divided into two categories: deep learning algorithms that use deep neural networks and machine learning algorithms such as regression and classification.
Machine learning algorithms — the primary tool used in AI — are a collection of guidelines or methods that are applied, generally by a computer, to compute or solve a problem. The typical methods computers use to solve problems or provide decision-making capabilities include either extensive data analysis or the creation of generalizations and statistical forecasts.
Major architectural components of AI as a service
The AIaaS architecture has three basic components: AI infrastructure, AI services, and AI tools.
AI infrastructure
AI infrastructure supports underlying AI and ML models. Data and compute are the two fundamental pillars of these models.
- AI data. When you apply large volumes of data to statistical algorithms, it is regarded as a functional ML model. These models are built to learn from patterns in the existing data. The sheer volume of data decides the accuracy percentage of the predictions. For example, numerous medical reports train deep learning networks, which further evolve and detect medical emergencies, cancer, or tumors.
ML relies heavily on input data that can be sourced from multiple sources. Data can come from relational databases, unstructured data (binary objects), stored annotation in NoSQL databases and a pool of raw data in a data lake. All these are used as inputs to the ML models.
Advanced ML techniques, including neural networks, perform complex computations that require a blend of central processing units (CPUs) and graphic processing units (GPUs). Both these components complement each other and enable faster processing. Cloud providers offer clusters of GPU-CPU combination-backed virtual machines (VMs) and containers in an AIaaS setup. Clients can use this infrastructural arrangement to train ML models and choose to pay on a use-per-use basis.
- AI compute. AI compute services include VMs, serverless computing, and batch processing. These computing methods are used to enhance parallel processing and automate ML tasks. For example, Apache Spark is a real-time data processing engine that has a scalable ML library. In training the ML models, they are used in VMs and containers to perform computations.
AI services
Public cloud vendors provide APIs and services that are readily available and do not need custom ML models for their consumption. These APIs and services extract benefits from the underlying infrastructure, which the cloud provider owns.
- Cognitive computing. Cognitive computing APIs include speech, text analytics, voice translation, and search. These services are accessed as REST endpoints by developers and integrated with applications with a single API call.
- Custom computing. Although APIs serve the purpose in generic cases, cloud providers are shifting toward custom computing, enabling users to experience cognitive computing using custom datasets. Here, users employ their data to train cognitive services. The custom approach reduces the overhead of selecting the right kind of algorithms and training the custom models.
- Conversational AI. Today, the world is becoming increasingly familiar with virtual assistants as end-users continue to accept AI readily. Thus, cloud providers are helping developers to integrate bots (voice, text) across platforms by leveraging bot services. Using this service, web and mobile developers can add digital assistants to their applications.
AI tools
In addition to APIs and infrastructure, cloud vendors provide tools that can help data scientists and developers. These tools promote the usage of VMs, storage, and databases as they are in sync with the data and compute platforms.
- Wizards. Amateur data scientists are served with wizards to reduce the complexity of training ML models. At the backend, these tools, in totality, act as a multi-tenant development environment.
- Integrated development environment (IDE). Experienced cloud vendors are making substantial investments in IDEs and notebooks (browser-based) that help in easy ML model testing and management. Such tools enable developers and data scientists to build smart applications with ease.
- Data preparation tools. The performance of ML models heavily depends on the quality of data. To ensure the top-notch efficiency of ML models, public cloud vendors are providing data preparation tools that can perform the extract, transform, load (ETL) job. The output of these ETL jobs is then fed into the ML pipeline for training and evaluation purposes.
- Frameworks. Cloud providers offer ready-to-go VM templates with frameworks such as TensorFlow, Apache MXNet, and Torch, as setting up, installing, and configuring the required data-science environment has become complicated. Such VMs train complex neural networks and ML models as they are GPU-supported entities. Public cloud providers are adopting AI on a large scale as they are looking to attract more customers to their platforms.
Although AIaaS is still evolving, it can be a game-changer in the context of data and compute services over the coming years.
Types of AI as a Service
Common types of AIaaS include:
Chatbots & digital assistance
These can include chatbots that use natural language processing (NLP) algorithms to learn from conversations with human beings and imitate the language patterns while providing answers. This frees up customer service employees to focus on more complicated tasks.
These are the most widely used types of AIaaS today.
Cognitive computing APIs
Short for application programming interface, APIs are a way for services to communicate with each other. APIs allow developers to add a specific technology or service into the application they are building without writing the code from scratch. Common options for APIs include:
- NLP
- Computer speech and computer vision
- Translation
- Knowledge mapping
- Search
- Emotion detection
Machine learning frameworks
ML and AI frameworks are tools that developers can use to build their own model that learns over time from existing company data.
Machine learning is often associated with big data but can have other uses—and these frameworks provide a way to build in machine learning tasks without needing the big data environment.
Fully managed machine learning services
If machine learning frameworks are the first step towards machine learning. This option is a way to add in richer machine-learning capabilities using templates, pre-built models, and drag-and-drop tools to assist developers in building a more customized machine-learning framework.
Benefits & drawbacks of AI as a Service
Like any other “as a service” offering, AIaaS brings value to companies without costing huge amounts. But there are also distinct drawbacks to using a cloud-based AI system that no business should ignore.
Benefits
- Flexibility. Hand in hand with lower costs, there’s a lot of transparency within AIaaS: pay for what you use. Though machine learning requires a lot of compute power to run, you may only need that power in short amounts of time—you don’t have to be running AI non-stop.
- Advanced infrastructure at a fraction of the cost. Successful AI and machine learning require many parallel machines and speedy GPUs. Prior to AIaaS, a company may decide the initial investment and ongoing upkeep too much. Now, AIaaS means companies can harness the power of machine learning at significantly lower costs. This means you can continue working on your core business, not training and spending on areas that only partially support decision-making.
- Scalability. AIaaS allows you to start with smaller projects to learn if it’s the right fit for your needs. As you gain experience with your own data, you can tweak your service and scale up or down as project demands change.
- Usability. While many AI options are open source, they aren’t always user-friendly. This means your developers are spending time installing and developing the ML technology. Instead, AIaaS is ready out of the box—so you can harness the power of AI without becoming a technical expert first.
Challenges
- Long-term costs. Costs can quickly spiral with all “as a service” offerings and AIaaS is no exception. As you wade deeper into AI and machine learning, you may be seeking out more complex offerings, which can cost more and require that you hire and train staff with more specific experience. As with anything, though, the costs may be a wise investment for your company.
- Reduced security. AI and machine learning depend on significant amounts of data, which means your company must share that data with third-party vendors. Data storage, access, and transit to servers must be secured to ensure the data isn’t improperly accessed, shared, or tampered with.
- Reduced transparency. In AIaaS, you’re buying the service, but not the access. Some consider as a service offerings, particularly those in ML, like a black box—you know the input and the output, but you don’t understand the inner workings, like which algorithms are being used, whether the algorithms are updated, and which versions apply to which data. This may lead to confusion or miscommunication regarding the stability of your data or the output.
- Data governance. Particular industries may limit whether or how data can be stored in a cloud, which may prohibit your company from taking advantage of certain types of AIaaS.
- Reliance. Because you’re working with one or more third parties, you’re relying on them to provide the information you need. This isn’t inherently a problem, but it can lead to lag time or other issues if any problems arise.
Top providers of AI as a Service
AI platforms, including Amazon Machine Learning, Microsoft Azure Cognitive Services and Google Cloud Machine Learning, can help organizations determine what might be possible with their data. Before committing, organizations can learn what works and enable scaling by testing the algorithms and services of different providers. When a platform is found that scales to requirements, the resources of these large providers can back up the needed scaling with compute capacity.
The following are some popular vendor platforms that offer AIaaS services:
Amazon Web Services (AWS)
AWS is a platform that offers multiple cloud services and more than 200 services across the globe. AWS provides several products for common use cases for machine learning and AI, including Amazon SageMaker and Amazon Alexa. Customers, companies and individuals with impairments all benefit from these Amazon AI services.
Anolytics
Anolytics is an AIaaS platform for data annotation that offers outsourcing services for ML and AI models.
IBM Watson
Businesses can select from a variety of prebuilt apps from IBM Watson, including Watson Assistant for creating virtual assistants and Watson Natural Language Understanding for performing complex text analysis tasks. No prior knowledge of data science or machine learning is required and developers can also create, train and deploy ML models across any cloud using IBM Watson Studio.
Google AI
Google Cloud provides many AI and machine learning tools, such as the Tensor Processing Unit (TPU), which accelerates AI model training. To expedite the development process, Google also offers several other AI technologies, including Google Lending DocAI, which automates the processing of mortgage documents.
LivePerson
LivePerson is a SaaS startup that uses the LivePerson Conversational Cloud. It enables the integration of systems for voice, email and messaging customer experiences and aims to use intent discovery to inform brands about what their customers want.
Microsoft Azure AI
Data scientists, engineers and machine learning experts frequently use Microsoft Azure machine learning and AI platforms. One such platform is the cloud-based service called Azure NLP, which aids in interpreting and analyzing texts. Python and R language support are also available through Azure.
Microsoft Azure offers prebuilt libraries, specialized code packages and other AIaaS offerings, including conversational AI and Azure Cognitive Services.
SAS
SAS is an AI-driven analytics platform that uses AI to handle big data and manage and retrieve data from various sources. The company also offers services in NLP and visual data mining and provides an easy GUI through the SAS language.
ServiceNow
One of the most popular services offered by ServiceNow is AIOps, which is an artificial intelligence platform that’s designed to help simplify IT operations. With products such as AI Contact Center and AI Customer Care, ServiceNow also offers choices for digital security.
The future of AI as a Service
As a rapidly growing field, AIaaS has plenty of benefits that bring early chapters to the table. But, its drawbacks mean there’s plenty of room for improvement.
While there may be bumps in the road while developing AIaaS, it’s likely to be as important as other “as a service” offerings. Taking these valuable services out of the hands of the few means that many more organizations can harness the power of AI and ML.
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