Ai as a Service Guide – Advantages, Limitations, Types, Models, Full Managed Platforms

Artificial intelligence as a service (Aiaas). Browse our detailed guide on advantages, limitations, types, models, fully managed platforms.
Artificial intelligence as a service Aiaas

What is Ai as a Service?

Artificial Intelligence as a Service (AIaaS) is artificial intelligence (AI) outsourcing service provided by a third party. Individuals and businesses can experiment with AI for a variety of objectives without huge upfront investment and with a lower risk. Experimentation can help you test different Machine learning algorithms by sampling numerous public cloud platforms.

Various AI provider platforms provide a variety of machine learning and AI styles. Because enterprises must examine features and pricing to see what works for them, these variants can be more or less suitable to their AI needs. Some AI jobs require specialized hardware, such as GPU-based processing for intense workloads, which cloud AI service providers can deliver. Buying the necessary gear and software to get started with on-premise cloud AI is expensive. AIaaS is prohibitively expensive for many enterprises due to manpower and maintenance costs, as well as hardware upgrades for different jobs.

How Does Artificial Intelligence As a Service Work?

Advanced AI necessitates enormous volumes of data, the quantity and quality of which is crucial to AI effectiveness. Its capability is to extract specific features from this data and classify them in order to generate an output. Human intervention is required in machine learning to instruct the machine on how to extract features. The system can educate itself to extract and classify data using deep learning, which is a much more advanced level of AI.

Consider the case of self-driving cars. To recognise what is happening in its environment, the vehicle gets data both visually through cameras and by radar or other sensing technologies. At the same time, it is constantly receiving and monitoring data about the vehicle’s performance. AI analyses and classifies the data to determine whether the situation the car is in demands intervention, and then sends a command to the vehicle to safely drive through the scenario.

What are the Benefits/Advantages of Ai Services for startups, SMEs and Enterprise?

Many businesses are keen to take advantage of AI’s fast-increasing technology, which offers numerous development chances.

1. There is no requirement for advanced engineering skills.

Even if you don’t have an AI programmer on staff, AIaaS can be used if you add a layer of no-code infrastructure to the game. At any point throughout the setup process, companies that actually deliver AIaaS generally do not require any coding or technical knowledge. “In a time when there’s a shortage of AI experts and ever-increasing rivalry in the industry, that’s a major issue,” writes Daniel Newman of Broadsuite Media Group in a Forbes column.

On that note, it’s worth noting that, while some AIaaS solutions don’t require any coding knowledge, the level of implementation difficulty varies greatly when dealing with legacy software.

2. Infrastructure that is both advanced and quick

Before AIaaS, successful AI and machine learning models required powerful and fast GPUs. The majority of SMEs lack the resources and time to create software internally.

There are a few rules of thumb in the area of AI, one of which is that your model will only function well if the data it is fed is of high quality. Because AIaaS is customisable, it will allow enterprises to design a specific task-oriented model on top of the vast amounts of data they already have.

3. Transparency

AIaaS not only gives you access to AI while reducing non-value-added labour, but it also provides a high level of transparency. Machine learning demands a lot of computational power, yet most pricing models focus on utilisation. AIaaS allows you to pay per usage.

Furthermore, certain platforms give the user additional control over AI automation.

The solution is to include a human in the loop as an option. In edge circumstances, HITL is a continuous feedback loop in which process owners provide AI input. The feature seeks to accomplish something that neither a human nor a machine can do on their own.

4. Usability

Let’s be honest: most as-a-service platforms aren’t quite as user-friendly as they claim. Although many AI alternatives are open-source, which means they can be freely downloaded, modified, and utilised, they can be difficult to set up and develop. In most circumstances, AIaaS, on the other hand, is totally ready to use. Without any formal training, process owners can use AI software.

Pre-built and custom-created models, as well as drag-and-drop interfaces for decreased complexity, are all part of end-to-end ML services. The best part about it? Without engineers, you can start your machine learning project in hours.

5. Scalability

Have you ever heard of a company that gets less emails as it grows? We haven’t, either.

AIaaS is designed to scale. You’re already ahead of the game if you’ve trained your model to identify your info@ mailbox based on email urgency or emotion and send the right emails to the right recipient.

AIaaS is ideal for doing activities that involve some level of cognitive judgement but are not value-adding in nature.

What are the Types/Models of Ai As a Service?

The following are examples of AIaaS:

Digital support and chatbots

Chatbots that employ natural language processing (NLP) algorithms to learn from human interactions and emulate language patterns while offering answers are one example. This allows customer service representatives to focus on more difficult jobs.

These are the most popular AIaaS services nowadays.

Computer cognitive  APIs

APIs, which stand for application programming interface, allow services to connect with one another. APIs enable developers to include a certain technology or service into their application without having to start from scratch. APIs come in a variety of options:

  1. NLP
  2. Computer vision and computer speech
  3. Translation
  4. Knowledge mapping
  5. Search
  6. Emotion detection

Frameworks for machine learning

Developers can utilise ML and AI frameworks to create their own model that learns over time from existing company data.

Machine learning is frequently linked with big data, but it may also be used for other purposes, and these frameworks make it possible to incorporate machine learning activities without requiring a big data environment.

Machine learning services that are fully managed

If frameworks for machine learning are the initial step toward machine learning. This option allows developers to design a more customised machine learning framework by using templates, pre-built models, and drag-and-drop tools to add broader machine learning capabilities.

Labeling of Data

Data labelling is the process of marking vast amounts of data to make it easier to organise. It can be used for a variety of purposes, including ensuring data quality, categorising data by size, and further training AI, to mention a few. In the latter situation, human-in-the-loop (which we discussed before in this piece) is used to classify data so that AI can analyse it simply in the future.

Classification of Information

When data is classified, it is assigned to one or more categories. Content-based, context-based, and user-based categorization are common. Data can be categorized on a bigger scale using Artificial Intelligence if a data categorization outline and criteria are explicitly stated.

Folio3 is Your Top Ai As a Service Company Choice

Folio3 assists organisations in automating procedures and regular decision-making through powerful algorithms. Machine Learning is bringing dramatic change to a variety of areas, including healthcare, education, transportation, and travel.

Our solutions make our clients’ lives easier and more efficient, allowing them to move away from traditional rule-based procedures and toward more intelligent ones, allowing them to uncover new unstructured data sets and patterns.

Below are few of the services provided by Folio3:

License Plate Recognition Assisted by Artificial Intelligence

Our program will make license plate detection and identification more efficient and seamless. The solution creates automation that maximises efficiency across verticals and sectors such as law enforcement and public administration.

The solution is built to work in a variety of situations and scenarios. Organizations and governments may improve their existing systems and increase operational efficiency by leveraging AI.

Transform road traffic analysis with Folio3’s Vehicle Detection Solution.

Our traffic analysis system automates vehicle recognition and counting while also identifying the type/category of the vehicle, making it a must-have for road and safety. This allows you to easily monitor traffic and congestion and make informed decisions to reduce inefficiencies.

Service for Facial Recognition

For a seamless and secure user experience, include low-friction and powerful facial recognition features in your apps.

Use Folio3’s facial recognition technology to find and recognise people in photos and videos. We can assist you in developing complex business scenarios involving facial recognition, such as gaining entry to restricted areas, counting individuals in venues, and obtaining crowd intelligence.

Person Recognition Assisted by Artificial Intelligence

To automate a variety of use cases such as restricting access, staff safety, and rescue, accurately detect individuals and persons in an image and/or video feed. Our system allows you to easily recognize people and flag access to vital areas, supplement search and rescue efforts, and enforce health and safety regulations such as social distancing.

Food Detection Using Advanced Deep Learning

Utilize AI and Deep Learning to recognize, identify, and classify various types of food and dishes. Our technology enables you to recognize food items in your image/video foods, whether you work in the food sector or on health and wellness apps.

Are There Any Artificial Intelligence Services Frameworks Available for Free?

Artificial intelligence software that is free and open-source:

1 TensorFlow

TensorFlow is an open-source artificial intelligence programme that aids in the creation and training of machine learning models. It introduces a library for high-speed numerical computing. Due to its modular architecture, this free AI software enables for straightforward compute deployment across a number of platforms (CPUs, GPUs, and TPUs).

2 IBM Watson

IBM Watson is a free, open-source AI programme that allows businesses to accelerate research and development, predict disruptions, and improve interactions. Several companies are using this software to analyse their data, collect intellectual property, gain insights, and forecast their future performance. Organizations may make better decisions by utilising IBM’s cloud-based platform.

3 Apache Mahout

Apache Mahout is a distributed framework for handling data processing efficiently. This free AI tool can be used effectively for data mining in conjunction with Hadoop. Facebook, Foursquare, Twitter, LinkedIn, and Yahoo are just a few of the large companies that use this data mining software.

4 OpenNN

OpenNN is an open-source artificial intelligence programme created in the C++ programming language. It gives you a faster processing speed. This programme serves as a free neural network library for sophisticated analytics. This app gets to the bottom of numerous apps in energy, health, and marketing.

5 Scikit-learn

Scikit-learn is a free artificial intelligence application that provides a uniform interface for a number of supervised and unsupervised learning techniques. It is regarded as a simple and effective data mining and data analysis tool. This open source AI programme is reusable and accessible in a variety of scenarios. If you want to include machine learning into a production system, this tool is worth considering.

6 Accord.NET

If you’re looking for a free artificial intelligence tool, Accord.NET is an excellent choice. This software combines audio and picture processing components with a.NET machine learning framework. Statistical analysis, image processing, machine learning, mathematics, and computer vision are all included in this open-source AI development platform.

7 Torch

This open-source artificial intelligence tool is a scientific computing framework that prioritises GPUs and has a lot of support for machine learning methods. Torch thinks that while creating scientific algorithms, complete flexibility and speed are essential. It greatly simplifies the procedure.

What Are Fully Managed Artificial Intelligence Platform as a Services?  

To preserve the system’s integrity, every cloud system requires someone to manage the network. Cloud managed services allow businesses to take use of the benefits of cloud computing without devoting so much time and attention to it that it interferes with their regular operations. Companies can now perform cloud managed services in one of two ways: by using in-house workers to operate the cloud network or by contracting with a third-party supplier.

Some of the benefits that Managaed Ai Cloud offers are:

Delivering Value at Scale and Speed

Get up and running in minutes, with no lag time between building models and providing value to the business. Plus, with a DataRobot University curriculum and our world-class AI Success team on your side, you’ll realise bottom-line value from enterprise AI initiatives in no time!

The total cost of ownership is low.

Let us handle all of the hardware installation, infrastructure setup, and computing costs while you focus on bringing your domain expertise to your AI initiatives. With the DataRobot AI Cloud Platform, you can get enterprise-level AI capabilities at a low total cost of ownership.

Security and Governance at the Enterprise Level

Other cloud machine learning services require you to forego enterprise-level security in exchange for convenience. You get the best of both worlds with DataRobot. Our Managed AI Cloud is SOC 2 Type II certified for information security, corporate controls, and software development, ensuring compliance with industry standards and best practises. Furthermore, our EU-based cloud solution is GDPR-compliant to meet our European customers’ specific data protection needs.

No upkeep required. Concentrate on the Most Important Issues

So you don’t have to, we’ll take care of the infrastructure. You’ll enjoy great security and availability, as well as frequent backups to safeguard your investment. We’ll also handle any upgrades, ensuring that you always have the most up-to-date DataRobot software.

What are the limitations and drawbacks of ai algorithm as a service?

Artificial intelligence has proven transformative for humanity, helping businesses to achieve more efficiency, lower costs, and boost their businesses in a variety of ways. But it isn’t perfect. There are certain drawbacks (mentioned below) that need to be taken into consideration.

Inaccuracy in Data Analysis:

AI programmes can only learn from the information we present them with. Your results may be wrong or distorted if the data provided to the programme is incomplete or unreliable. As a result, AI can only be as smart or successful as the data you feed it with.

Algorithmic Bias: 

Algorithms are a collection of instructions that a machine follows to achieve a task, which may or may not have been authored by a human programmer. However, if the algorithms are flawed or biassed, they will just provide you with unfavourable results, and we cannot trust them. Biases arise mostly as a result of programmers’ partial design of the algorithm, which favours some desirable or self-serving criterion. Algorithmic bias can be found on a variety of significant platforms, including social networking sites and search engines.

“Black Box” Nature of Ai: 

Artificial intelligence is recognised for its capacity to learn from enormous amounts of data, uncover underlying patterns, and make data-driven judgments. However, while the AI system consistently returns accurate findings, it has one major flaw: it cannot communicate or explain how it arrived at this conclusion. As a result, the issue arises: how can we trust the system in highly sensitive areas like national security, governance, or high-stakes corporate ventures?

What Are the Ai Platform As a Service Challenges?

Some of the major challenges that Ai platform as a service faces are as follows:

Reduced Security

Because AI and machine learning require large volumes of data, your organisation will have to share that data with third-party providers.


You’re relying on one or more third parties to deliver the information you require since you’re working with them. This isn’t a problem in and of itself, but it can cause lag time or other concerns if any complications develop.

Less Transparency

You buy the service but not the access in AIaaS. Some see service offerings, particularly those in machine learning, as a black box—you know the input and output, but not the inner workings. This could lead to misunderstandings or miscommunications about the data’s or output’s stability.

Data management

Certain industries may place restrictions on whether or how data can be stored in the cloud, making it impossible for your firm to use AIaaS.

Long-term expenses

All “as a service” solutions, including AIaaS, can soon spin out of control. As you dive deeper into AI and machine learning, you may find yourself looking for more complex solutions, which can be more expensive and necessitate hiring and training more specialised personnel.

AIaaS Providers in 2022

You can probably guess who the big AIaaS vendors are.

Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)  and IBM Watson are all industry leaders that have provided AIaaS solutions to a wide range of businesses throughout the world. In addition to fully managed machine learning options, each provider offers a variety of bots, APIs, and machine learning frameworks.

Other well-known IT companies, such as Salesforce, Oracle, and SAP, are creeping into the Big 3’s turf.

Source: Google

Future of AIaas and Upcoming Trends

AIaaS has a lot of advantages that attract early adopters because it is a quickly growing sector. However, its flaws indicate that there is still space for growth.

While there may be setbacks in the development of AIaaS, it is expected to be just as essential as other “as a service” products. By removing these essential services from the hands of a few, many more businesses will be able to benefit from AI and machine learning.

Final Thoughts

AIaaS (Artificial Intelligence as a Service) makes AI technology available to everyone. With minimal work, it gives quick, cost-effective, and ready-to-use solutions.

Developing AI software in-house is usually not a possibility. You may pay for the tools you need with an AIaaS and upgrade to a higher plan as your business and data grow.

Whether you need a chatbot to assist customers on the front lines, detect sentiment in Twitter data, or automate ticket labelling and routing, an AIaaS solution can help.

Many AI activities can be accomplished using Folio3’s no-code tools. To learn how AI can help you with your specific use case, sign up for a free demo.

About Muhammad Imran

Muhammad Imran is a regular content contributor at Folio3.Ai, In this growing technological era, I love to be updated as a techy person. Writing on different technologies is my passion and understanding of new things that I can grow with the world.

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