AI as a Service in 2024: Types and Challenges

Cloud services such as SaaS, IaaS, PaaS, AaaS (Analytics as a Service) have been used by companies for a while. With As-a-Service models, costly and time consuming implementations are shifted to subscription based technology that reduces IT spending while enhancing client flexibility. Artificial intelligence as a service have the same principle. AIaaS companies provide a cost-effective solution for businesses who are willing to invest in AI because AI-as-a-Service providers maintain their infrastructure while companies leverages services..

What is AIaaS?

AI-as-a-service is AI tools (most of the time APIs) that are offered by third party vendors through off-the-shelf solutions. AIaaS enable companies to implement AI solutions with minimal investment.

Why is it important now?

Due to increasing competition within industries, businesses are more willing to invest in digital technologies including artificial intelligence to gain a competitive edge. Yet, AI development and implementation is not feasible for every business. Though 79% of executives worldwide say artificial intelligence will make their job easier and more efficient, there are still obstacles that slows down AI implementation. In fact, 40% of executives believes that technologies and expertise are too expensive.

AIaaS enables businesses to

• focus on the core business,
• reduce costs through pay as you use so that organization have a more transparent view on AI pricing,
• reduce development time and investment risk
• increase strategic flexibility through dynamic availability

What are the main types of AIaaS?

Tools that enable companies to build their own custom AI models

• End-to-end ML services: Vendors deliver the service through pre-built models, custom-created data model templates, and drag and drop interfaces that aims to minimize the complexity of machine learning workflow. Pre-trained and customizable models enable businesses build AI-powered applications without AI/ML expertise. For example, Azure Pipelines and Azure Machine Learning can be combined to create end-to-end ML services. Other examples include Algorithmia.
• Machine learning components: These enable developers and data scientists to build their own AI models. They are not end to end solutions but aim to help technical personnel build better models. Examples include AWS Sagemaker.

Pre-trained machine learning models

• Third party APIs: APIs enable businesses to add new AI capabilities into existing/new applications. Common APIs include
  • NLP (natural Language Processing) including APIs such as intent detection, translation, transliteration, voice-to-text etc.
  • Vendors also combine several NLP services like intent detection to provide end-to-end conversational AI services
  • knowledge mapping
  • computer vision
  • intelligent searching
  • emotion detection

What is the AIaaS ecosystem?

AIaaS ecosystem contains both tech giants & startups. Though tech giants contain the major share of the market, there are startups that offer unique value propositions to businesses.

Tech giants

Amazon Web Services (AWS)

Amazon is one of the first companies to offer services in the cloud AI/ML service market. It offers a wide range of services and APIs including:

• Lex is a service that enables developers to build natural language chatbots into new and existing applications. It also performs speech recognition, converts speech to text, and analyzes content via NLP.
• Polly converts text into spoken audio. It allows developers to create speech-enabled applications and products.
• Rekognition provides computer vision capabilities services through
  • algorithms that are pre-trained on data collected by Amazon or its partners
  • algorithms that a user can train on a custom dataset.

Microsoft Azure

Azure is a cloud computing service for building, testing, deploying, and managing applications and services through cloud. AI services Microsoft Azure offers are:

• Cognitive Services includes APIs like anomaly detection, content moderation etc.
• Cognitive Search enables AI-powered cloud search mobile and web app development.
• Machine Learning (AML) builds, trains, deploys ML models from cloud to edge. It also supports custom AI development.
• Bot Services include intelligent, serverless chatbot service that scales on demand.
• Databricks is a easy-to-use, collaborative Apache-Spark based platform for analytics.

IBM Developer Cloud

IBM Developer Cloud helps developers insert Watson intelligence into apps and it also helps train and manage data in a cloud. Check out this Github link to see all open source Watson APIs , there are currently 108 different libraries as of February 2022.

Google Cloud

Google Cloud is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products. Google offers variety of cloud AI services to help developers in each step of machine learning development. Some services include

• AI Platform help businesses build, deploy, and manage machine learning models.
• AI Hub is a hosted repository of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms
• Conversational AI services include Speech-to-Text, Text-to-Speech, virtual agents and Dialogflow to create conversational actions across devices and platforms.

Startups

H2O.ai

H2O.ai is an open source machine learning platform that enables AI applications via services in cloud, as well as on-premises.

Prevision.io

Prevision.io offers AutoML focused set of cloud AI development services. Prevision.io automated machine learning platform generates and deploys predictive models on cloud or on premises. Their platform enables citizen data scientists to build standalone models using enterprise data.

What are the challenges of AIaaS?

Some challenges of AIaaS are:

• Data Privacy/ Security: Due to data privacy legislations such as GDPR and CCPA and due to the expiration of US/EU data privacy shield, business should be careful with their data. Using privacy-enhancing technologies such as data masking could help protect your enterprise data.
• Vendor lock-in: It may seem easy to switch to a different API however each API uses different response formats and changing APIs requires some effort. End-to-end ML services or ML components are harder to switch tools since your teams need to get familiar with them to be effective. All of these contribute to vendor lock-in an companies should understand how easy or difficult it is to switch between competing products.
• Data governance: Some companies in highly regulated industries such as banking or healthcare may limit the storage of data in the cloud. Such companies may not be able to leverage AIaaS.
• Long run costs: AIaaS allows companies to get started fast. However, long run costs may be significant and companies need to understand both short and long term costs before making significant AIaaS investments.

How to choose your AIaaS solution?

Before selection an AIaaS solution, you may wonder if you really need to outsource AI services. If you still didn’t decide whether you prefer outsourcing solution or inhouse development, you can take a look at our article on the topic.

According to 2019 State of Cloud Report, 35% of the funds cloud users are spending on the cloud is going to waste. Therefore selecting the right AIaaS is crucial for companies. Some questions that you should answer before selecting a vendor are:

• Does vendor provide an option to test the API? You need to test APIs with your own data to ensure that AI implementation is working at an acceptable level of security. Since data masking also takes effort, if the data is not very valuable, you may want to sign an NDA and Data Processing Agreement (DPA) and then use real data to test the system.
• Is the API the best in the market? It is best to compare results of competing APIs on your data. Services such as RapidAPI allow companies to use multiple services following openAPI specification making it easier to try multiple services. There are also AI startups that combine multiple API services and allowing you to try multiple APIs using a single endpoint.
• Is this a secure API? A typical data security checklist for a cloud service provider begins with checking their SOC 2 and ISO 27001 credentials. In addition to these, your company may have its additional security requirements.
• Typical procurement best practices also make sense. For example, you need to make sure that the vendor has the financial means to be a good partner in the long run.

If you still have question about AI-as-a-service vendors, don’t hesitate to ask us: