What Is Machine Learning as a Service (MLaaS)?

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 tools extensively. ML can automate monotonous tasks and find newer, 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.

Amazon Sagemaker (part of Amazon’s machine learning services), Microsoft Azure Machine Learning Studio, and IBM Watson Machine Learning are some examples of MLaaS.

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.

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 services without having an in-house team of data scientists and ML developers. 

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.

How MLaaS works

Machine learning as a service 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.

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.

Types of MLaaS

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

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.

Natural language processing

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.

Image recognition

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.

Speech recognition

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.

Applications of MLaaS

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.

Benefits of using ML as a service

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 ML as a service.

Hosted by the vendor

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.

Data management

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.

Cost-efficient

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.

Perform experiments without coding

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.

When to use 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.

When not to use MLaaS

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.

Top machine learning software

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:

• Ingest data inputs from different data sources
• Solve problems based on learned data
• Offer an algorithm or product that learns and improves by utilizing data
• Be the source of intelligent learning capabilities for applications

*Below are the five leading machine learning software from G2's Fall 2024 Grid® Report. Some reviews may be edited for clarity.

1. Vertex AI

Vertex AI is a Google Cloud platform for building, deploying, and managing ML models. It offers tools for data preparation, training, deployment, and monitoring. Key features include integrated workflows, AutoML, custom training, model monitoring, and MLOps integration.

What users like best:

"Vertex AI makes it easy to prepare data, train models, and deploy them. The tools and services work well together, which saves time and effort. AutoML is especially helpful for building models quickly without needing deep knowledge of machine learning."

- Vertex AI Review, Swati M.

What users dislike:

"The steep learning curve can be a little overwhelming for new users. The user interface feels complicated and not as intuitive as some competing platforms, especially for those with no previous experience in AI or data science."

- Vertex AI Review, Hariharan G.

2. Amazon Forecast

Amazon Forecast, a managed machine learning service on AWS, empowers you to generate accurate forecasts easily. It automates the complexities of machine learning, making it accessible to all. Using this simplified forecasting process, you can improve your planning and decision-making, and ultimately drive better business outcomes.

What users like best:

"With Amazon Forecast, users can benefit from a fully managed service that utilizes statistical and machine learning algorithms to provide exceptional accuracy in time-series forecasting."

- Amazon Forecast Review, Amy R.

What users dislike:

"The size and demands of this program are getting bigger and more annoying. The software takes up a lot of space on my system and makes communication with other programs complicated. It possibly affects the speed with which other programs of equal importance are executed in my system."

- Amazon Forecast Review, Choy N.

3. Google Cloud TPU

Google Cloud TPU helps businesses run machine learning models using Google's cloud computing services. Its custom network offers 100 petaflops of performance, which is enough computational power for transforming a business or making the next deep learning research breakthrough.

What users like best:

"I love the fact that we were able to build a state-of-the-art AI service geared towards network security thanks to the optimal running of the cutting-edge machine learning models. The power of Google Cloud TPU is of no match: up to 11.5 petaflops and 4 TB HBM. Best of all, the straightforward easy to use Google Cloud Platform interface."

- Google Cloud TPU Review, Isabelle F.

What users dislike:

"The price is too high, and some codes of TensorFlow need to be adapted to run it on a TPU system. Sometimes it's hard to track errors because of a hidden configuration."

- Google Cloud TPU Review, Obaib E.

4. Jarvis

Jarvis is an AI platform that helps create, launch, and scale conversational AI applications. It offers specialized modules for speech recognition and synthesis, natural language understanding (NLU), and computer vision integration. 

What users like best:

"I used to work with Chatgpt, Grammarly, Google Translate, and the search browser separately. But when I learned about Jarvis, I got all of these at the same place. GPT support, translator support, copywriting support, browser support-- all in one place! It is simple, effective, and time-saving."

- Jarvis Review, Athira N.

What users dislike:

"The least helpful aspect of Jarvis has been the difficulty in customing the commands specifically for our use case."

- Jarvis Review, Davina P.

5. Aerosolve

AeroSolve is a free machine learning tool built by Airbnb to help businesses in travel and hospitality solve complex problems like pricing and forecasting. It offers advanced features for location-based data, precise calculations, and combining different data points. You can also add your own knowledge to the models.

What users like best:

"It has advanced capabilities and is very easy to use. The implementation and integration are also quite smooth. The customer support is decent."

- Aerosolve Review, Rahul S.

What users dislike:

"Compared to some other popular machine learning libraries and platforms, Aerosolve’s user community may be small."

- Aerosolve Review, LV R.

Machine learning is the way forward

Creating a machine learning model requires the right talent and resources and ample time. Such demands may be unrealistic for SMBs, and so, machine learning as a service can help meet these requirements to make their goals a reality. In short, ML as a service enables you to effortlessly go from zero to hero in machine learning.

AI is yet to catch up with its portrayal in science fiction. However, there's still a lot that can be done with it. Read more about narrow AI here.

This article was originally published in 2023. It has been updated with new information.