We are aware of technical terms used in the cloud industry as Software as a Service (SaaS), Platform as a service (PaaS), and Infrastructure as a Service (IaaS). Usually, with “As-a-Service” models, costly and time-consuming solution implementations get shifted towards subscription-based technology that reduces IT spending while enhancing flexibility for clients.
Some major events have taken place in the Information Technology space in the last 5 years:
- Cloud platforms are booming with a variety of affordable choices for enterprise data management, moving from self-owned platforms to infrastructure to services that data-centric.
- Storage technologies have become more reliable, secure, and cheap
- AI technologies like Machine Learning, Deep Learning, and Natural Language Processing grew by leaps and bounds and it is still growing at an exponential pace
From 2018, the Software-as-a-Service (SaaS) market spiked from $5.6 billion to $133 billion. The four major players that are highly engaged in this domain — Google, Amazon, Microsoft, and IBM — have been engaged in a race for AI stardom for a while now.
Advantages obtained by SaaS, PaaS, IaaS models is now being replicated in this emerging technology, and that, we call it AIaaS(Artificial Intelligence as-a-Service)
So, what’s this AI as a Service?
For a long time, AI technology was around for a while but was cost-prohibitive to most companies. Some of these concerns were:
- Machines had storage constraints
- Insufficient Data
- Inadequate Infrastructures
The emergence of cloud services has made AI accessible. This is where AI-as-a-service comes into the picture.
AI as a service refers to off-the-shelf AI tools that enable companies to implement as well as scale their AI solutions at a fraction of the cost of full in-house AI.
Different types of AIaaS
Chatbots & digital assistance
Chatbots that use Natural Language Processing (NLP) algorithms to learn from conversations imitate the language patterns while providing answers and also provides language translation services. This frees up customer service employees to focus on more complicated tasks.
Cognitive computing APIs
APIs are like a communication channel for services to communicate with each other. APIs allow developers to add a specific technology or service into the application or use existing functionality without writing the code from scratch.
Machine learning frameworks
ML and AI frameworks are tools that Data Scientists and AI Engineers use to build their model that learns over time from existing data. These frameworks provide a way in which one can build machine learning tasks without needing the big data environment.
Example: Tensorflow, Pytorch
Fully managed machine learning services
If machine learning frameworks are the starting steps towards machine learning, fully managed ML services are a way to add in richer machine learning capabilities using templates, pre-built models, and drag-and-drop tools to assist in building customized machine learning frameworks.
Example: Azure Pipelines and Azure ML can be combined to create end-to-end ML services. Other examples include Algorithmia.
Machine learning components:
Machine Learning components enables developers and data scientists to build their own AI models. These components are not end-to-end solutions but aim to help technical personnel build better models.
Example: AWS Sagemaker.
Why should you prefer AIaaS?
Advanced infrastructure available at low cost
Successful AI and machine learning require a good infrastructure (parallel machines and speedy GPUs). Through AI as a service companies can harness the power of machine learning at significantly lower costs. This means companies can continue working on their core business, not training and spending on areas that only partially support decision-making.
Flexibility to companies/clients
In addition to lower costs, there’s a lot of transparency within AIaaS: only pay for what you use. Since machine learning requires a lot of computational power, you may only require power in short amounts of time (during model training).
Usability of resources
There are many open-source AI options, but they aren’t always user-friendly. This means developers are spending time installing and developing ML technology. Instead, AIaaS is ready out of the box—so you can harness the capabilities of AI even without being a technical expert.
One of the most important aspects of AIaaS is that it allows starting with smaller projects to learn if it’s the right fit for the requirement of the project. If it’s a right fit for the solution, it can be tweaked scale up or down as project demands change.
What are the challenges of AIaaS?
Like every coin has two sides, AIaaS of course has many benefits, but it also has some challenges. Some of the most common challenges are:
AI and machine learning models depend on significant amounts of data, which means one needs to share that data with third-party vendors. Data storage, access, and transit to servers must be secured enough to ensure the data isn’t improperly shared, or tampered with.
Reliance on the third party
Using AIaaS may involve one or more third parties. This isn’t inherently a problem as such, but it can lead to lag time.
Particular industries may limit how data can be stored in a cloud, which altogether might prohibit companies/developers from taking advantage of certain types of AIaaS.
Costs can quickly spiral with “everything as a service” offerings and AIaaS is no exception here. As we dive deeper into AI and machine learning, we may be seeking more complex offerings, which can cost more.
Current AIaaS ecosystem
The current AIaaS ecosystem includes renowned tech giants & startups. Though tech giants have the major share of the market, recently some startups offer unique value propositions to businesses.
Let’s look at tech giants in the AIaaS ecosystem.
Amazon Web Services (AWS)
Amazon is one of the first companies to offer a wide range of services in the cloud AI/ML service market. Some of the services and APIs include:
- Amazon Lex: It is a service that enables developers to build chatbots into new and existing applications. Besides this task of building chatbots it also performs speech recognition, converts speech to text, and analyses content via NLP.
- Amazon Polly: A fantastic tool that converts text into spoken audio. Moreover, it allows developers to create speech-enabled applications and products.
- Amazon Rekognition: This tool adds computer vision capabilities services through algorithms that are pre-trained on data collected by Amazon or its partners’ algorithms.
The next big tech giant is without a doubt
Azure provides cloud computing services for building, testing, deploying, and managing applications and services through the cloud. AI services Microsoft Azure offers are:
- Azure Cognitive Services: This tool includes APIs like anomaly detection, content moderation, etc.
- Azure Bot Services: It includes an intelligent, serverless chatbot service that can be scaled on demand.
- Azure Databricks: An easy-to-use, collaborative Apache-Spark-based platform for analytics purposes.
The next big tech giant as expected is IBM
IBM Developer Cloud
IBM Developer Cloud helps you insert Watson’s intelligence into apps and additionally it also helps train and manage data in a cloud. There are currently 102 different open-source libraries as of October 2020.
And then comes Google with its
Google Cloud is itself a suite of cloud computing services that runs on the same infrastructure that Google uses internally. Google offers a wide range of cloud AI services to help developers at each step of machine learning development. Some services include
- AI Platform: This platform helps businesses build, deploy, and manage machine learning models.
- AI Hub: A hosted repository of plug-and-play ready to use AI components, including end-to-end AI pipelines and out-of-the-box algorithms
- Conversational AI: These services include Speech-to-Text, Text-to-Speech, virtual agents, and Dialogflow to create conversational actions.
H2O.ai is an open-source ML platform that enables AI applications through services in the cloud, as well as on-premises.
Prevision is an end-to-end enterprise AI as a service platform, designed to enable business users, data scientists, and developers to deliver AI projects with better Returns on Investments, and faster. Prevision.io automated machine learning platform generates and deploys predictive models on the cloud or premises.
How to choose your AIaaS solution?
Before selecting an AIaaS solution, you may need to analyze if you need to outsource AI services.
As per the State of Cloud Report in 2019, 35% of the funds cloud users are spending on the cloud are going to waste. Here are some of the questions that you can consider as a checklist but not mandatory questions before investing in an AIaaS solution:
Does the vendor you are opting for provide you an option to test the API?
You may need to test APIs from time to time with your data just to ensure that AI implementation is working at an acceptable level of security.
Is the API best in the market?
It is always recommended to compare the results of competing APIs on your data. Some of the Services like RapidAPI allow companies to use multiple services following open API specifications making it easier to try multiple services.
Is this a secure API?
You must go through 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 might also have additional security requirements.
In short, AIaaS gives companies the power of using AI without the need for in-house expertise to manage it. It appears to be for sure a game-changer. It may indeed be set to become one of the major leaps forward for businesses.