This article was originally published in February 2023. It has been refreshed with new information.
Machine learning is taking almost every industry by storm.
If a process can be digitally executed, machine learning (ML) will eventually become part of it. As a branch of artificial intelligence, it uses algorithms to analyze massive amounts of data to derive relevant information and automatically improve from experience.
Healthcare, manufacturing, finance, and e-commerce are some of the many industries that use ML extensively. ML can automate monotonous tasks and find newer, more efficient ways to execute business processes.
Given the demand for machine learning, a new kind of service called machine learning as a service has sprouted in recent years. It's a full-stack AI platform that helps to automate several business processes.
MLaaS empowers small and medium-sized companies to use machine learning for their specific business needs without investing in any specialized infrastructure.
Machine learning as a service (MLaaS) is a collection of cloud-based machine learning tools offered by cloud service providers. Such tools provide frameworks for artificial intelligence tasks such as machine learning model training and tuning, face recognition, speech recognition, chatbots, predictive analytics, natural language processing, data preprocessing, forecasting, and data visualization.
Amazon SageMaker (part of Amazon’s machine learning services), Microsoft Azure Machine Learning, and IBM Watson Machine Learning are some examples of MLaaS.
In other words, MLaaS is a software licensing and delivery model in which the service provider hosts the machine learning tools, making it easier for multiple users to access them from different devices.
With machine learning as a service, businesses can use the services offered by the provider or vendor without creating their own. You can use MLaaS to automate multiple tasks and increase the efficiency of workflows that involve humans.
Think of software as a service (SaaS) or platform as a service (PaaS), but machine learning tools instead of software or platform. With MLaaS, you don't have to worry about gathering the needed computational resources as the actual computation will be performed on the service provider's data centers.
As with any other cloud service, the best thing about MLaaS services is that you can get started with machine learning quickly without having to set up any specialized infrastructure. In most cases, MLaaS follows a pay-per-use model, which is like renting a car and paying only for the number of miles you drive.
MLaaS providers enable you to enjoy the benefits of machine learning without being concerned about the risks associated with designing ML models. They also empower you to use machine learning solutions without having an in-house team of data scientists and ML developers.
As mentioned above, businesses in almost every industry can benefit from machine learning services. Even a coffee shop can rely on the power of machine learning and data science to discover footfall trends or determine which new flavor of coffee would sell the most.
The following are some of the common use cases of MLaaS:
MLaaS encourages small and medium-sized businesses (SMBs) to use machine learning and gather actionable insights from their data. MLaaS platforms eliminate the need to have a specialized, expensive infrastructure in place and make deploying the machine learning technology more approachable, scalable, and affordable.
The following are some of the notable benefits of using MLaaS.
SMBs don't have to worry about their in-house capabilities as the machine learning software is hosted by the vendor, just like cloud providers. With MLaaS, businesses can get started with machine learning without going through the software installation process or setting up their own servers.
More specifically, ML services streamline the processes associated with the machine learning lifecycle, including data cleaning and preparation, data transformation, model training and tuning, and model version control.
MLaaS platforms can help you with data management. Since MLaaS providers are essentially cloud providers, they also offer cloud storage and proper ways to manage data for machine learning projects. This makes it easier for data scientists to access and process data as many of them may not have engineering expertise.
Another advantage of using MLaaS services is cost efficiency. Setting up an ML workstation is expensive. You require top-tier hardware like high-end graphic processing units (GPUs), which are costly and consume large amounts of electricity. With MLaaS, you pay for hardware only when you use it.
MLaaS providers also offer tools for data visualization and predictive analytics and APIs for business intelligence and sentiment analysis. Interestingly, some MLaaS providers offer interfaces with drag-and-drop functionality, making it easier to perform machine learning experiments without coding.
MLaaS solutions can be differentiated based on the kind of services they offer. In essence, these solutions analyze large volumes of data to discover hidden patterns. The difference in the type of input data, the algorithms used, and how the output is used give rise to different kinds of MLaaS.
Data labeling, also known as data annotation or data tagging, is the process of labeling unlabeled data. Labeled data is used to train supervised machine learning algorithms. Data labeling software differs based on the type of data they support.
*These are the five leading data labeling software solutions based on G2 data collected on June 29, 2026.
Natural language processing (NLP) is a subfield of artificial intelligence and computer science that offers computers the ability to understand written and spoken language. NLP has made significant strides in recent years due to rapid advances in deep learning, more specifically in deep neural networks.
Sentiment analysis or opinion mining is a popular application of NLP that helps determine the social sentiment of products, services, or brands by analyzing customer feedback, reviews, and social media posts.
Text mining is another application of natural language processing that enables users to gain valuable information from structured and unstructured text. Text analysis software can consume data from multiple sources, including emails, surveys, and customer reviews, and offer visualizations and actionable insights.
*These are the five leading text analysis software solutions based on G2 data collected on June 29, 2026.
Image recognition, a computer vision task, attempts to understand the content of images and videos. Image recognition software takes an image as an input and, with the help of computer vision algorithms, places a bounding box or label on the image.
With the advent of IoT devices, collecting image data is effortless, making it easier to train algorithms. Object recognition, image restoration, and facial recognition are all made possible by image recognition software.
*These are the five leading image recognition software based on G2 data collected on June 29, 2026.
Speech recognition converts spoken language into text. Voice recognition software helps convert audio and video files to text and process phone requests in customer service. Virtual assistants like Siri and Google Assistant use voice recognition to decode your speech into machine-understandable form.
*These are the five leading voice recognition software solutions based on G2 data collected on June 29, 2026.
MLaaS is built on cloud infrastructure and resembles many of the features of a SaaS solution. Instead of offering a buffet of tools, an MLaaS provider may offer only a single service, for example, a perfectly tuned machine learning model.
With MLaaS, all aspects of the machine learning process are handled by a single provider, ensuring maximum efficiency. The features of MLaaS platforms will vary depending on the provider you choose. Still, in most cases, you'll get a cloud environment on which you can prepare data, train, test, deploy, and monitor machine learning models.
In short, MLaaS platforms will have features for:
To better understand how MLaaS works, let's consider a simple example of a coffee shop.
The coffee shop owner aspires to increase revenue by using the power of machine learning. However, it's improbable that the coffee shop business will have the needed in-house talent to deploy machine learning models. Therefore it's better to rely on a third-party provider that offers machine learning as a service.
The MLaaS provider may install several IoT devices to collect data about footfall trends and also collect data from the POS machine. Doing so allows the service provider to better understand the peak timings, the flavors customers like the most, and frequently bought together items.
The MLaaS provider will employ data scientists and engineers to work on the collected data. They may also offer web-based applications with a drag-and-drop interface that the business owner can use without needing expertise in machine learning.
The MLaaS provider help transform the collected data into useful information, helping the business owner to make precise decisions about marketing and sales strategies. The data collected can also help predict what combos customers are more likely to purchase.
MLaaS can also enable businesses to run sentiment analysis and understand how customers perceive them by analyzing social mentions, posts, and reviews. In short, companies, regardless of their size, can apply machine learning with the help of MLaaS.
Suppose you're already familiar with the services of an MLaaS provider, for example, Amazon Web Services (AWS) or Google Cloud Machine Learning Engine. In that case, it’ll be easier to integrate their services with your existing system.
If your business runs a microservice-based architecture, then MLaaS can help with the proper management of those services. Suppose you want to use machine learning as a part of an application you're developing. In this case, MLaaS will be a good choice as you can integrate it, in most cases, using APIs.
MLaaS will also be beneficial if you've got a relatively smaller in-house team with less ML expertise. This service can augment their efforts and help employ machine learning, even if they don't have the necessary hardware. To choose the right MLaaS provider, consider factors including, the time available, budget, and your team's technical capabilities.
If the amount of training required is significantly high, building an in-house infrastructure may be a cheaper option. Likewise, if the amount of training data involved is gigantic, the development process with MLaaS solutions might be slower as data is stored and accessed from the cloud.
If you deal with highly sensitive data, you may have to heavily scrutinize your MLaaS provider. Of course, cloud platforms have remarkable end-to-end security features. But anytime data moves from one place to another, there's always a risk factor involved.
Furthermore, if you wish to perform several customizations on complex ML algorithms, it’d be better to opt for on-premise infrastructure.
Back in 2023, MLaaS primarily referred to platforms for building, training, and deploying traditional ML models. The emergence of large language models (LLMs) and generative AI has fundamentally expanded that definition.
Today, foundation models that are pre-trained on billions of parameters and delivered via API - are themselves a form of machine learning as a service. Rather than training a model from scratch, businesses can now access tools such as GPT-4, Claude, Gemini, and open-source models like Llama via simple API calls, paying only for what they use. This is the pay-per-use MLaaS model applied to the most powerful AI systems ever built.
The practical impact is significant: tasks that once required hours of engineering work - sentiment analysis, document extraction, code generation, customer intent classification - can now be completed with a few lines of API code. MLaaS has effectively lowered the barrier to enterprise AI to "you need an API key."
Machine learning software enables you to make predictions and data-driven decisions. They can provide automation and AI features to your applications and help solve classification and regression problems.
To qualify for inclusion in the machine learning category, a product must:
*Below are the five leading machine learning software based on G2 data collected on June 29, 2026.
Creating a machine learning model once required deep technical talent, expensive infrastructure, and months of development time. MLaaS has progressively collapsed each of those barriers - and with generative AI now delivered as a service, the gap between an SMB and an enterprise AI capability has never been smaller.
Artificial intelligence is yet to catch up with its portrayal in science fiction. However, a lot can be done with the level of artificial intelligence we currently have. It's called narrow AI, and you’re likely bearing the fruits of its hard work every day.