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 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.

MLaaS empowers small and medium-sized companies to use machine learning for their specific business needs without investing in any specialized infrastructure.

What is machine learning as a service?

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 Studio, 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. 

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 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.

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.

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.

How MLaaS works

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.

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.

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 enable 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 improve by utilizing data

*Below are the five leading machine learning software from G2's Summer 2021 Grid® Report. Some reviews may be edited for clarity.

1. scikit-learn

scikit-learn is an open-source library used for implementing machine learning in Python. It consists of several efficient tools for machine learning and statistical modeling, such as classification, regression, clustering, and dimensionality reduction.

What users like:

"scikit-learn is simply wonderful. It abstracts away all the complexities of several machine learning frameworks. scikit-learn provides beautiful one-line function calls to really complex functions, and the documentation is beautiful. A complete noob can go through their documentation and understand since it's human-readable. In addition to top ML models ranging from random forest, decision trees, and linear regression, they also provide libraries for data preprocessing. You can do data preprocessing, one-hot encoding, and lots of other things with scikit-learn."

- scikit-learn Review, Izuchukwu U.

What users dislike:

"One issue that has persisted and troubled me for quite some time is the lack of categorical variables transformation capabilities (it is much easier in libraries like TensorFlow). It is comparatively slower than TensorFlow when it comes to enormous datasets, and this is something that should be adopted soon, especially in the era of big data technologies. However, with the frequency of updates, most issues get resolved quickly, making it a robust package for machine learning development."

- scikit-learn Review, Devwrat T

2. Personalizer

Personalizer is a cloud-based service from Microsoft Azure that helps deliver personalized experiences in your applications. It can help boost user satisfaction and usability by monitoring user reactions and choosing the best content to show to users.

What users like:

"We appreciate that the API tools offered by Personalizer are easy to use, and the system was organized clearly and distinctly. Two of us used the system the most, and we both felt very comfortable with it. I liked the use of JSON, which Microsoft used for the data exchange portion of the software. The option used for authentication was API Keys. This was also a good part of the software."

- Personalizer Review, Katie R.

What users dislike:

"Personalizer lags at times, and the results can be unexpected a few times."

- Personalizer Review, Ishaan R.

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:

"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. Amazon Personalize

Amazon Personalize helps developers create applications with real-time personalized recommendations. It doesn't require any machine learning expertise to use, making it ideal for small businesses or startups that don't have a data scientist or engineer on board.

What users like:

"It's effortless to make conversions when using Amazon Personalize. Whether you're there to sign up for a webinar or to download an ebook, the actions are swift and easy. Amazon Personalize is very welcoming to newbies. It's straightforward to attract newcomers to your website using this software."

- Amazon Personalize Review, Victor N.

What users dislike:

"At this point, the only issue with Amazon Personalizer is that we have to filter through too many options so that our consumers are not constantly receiving repetitive recommendations."

- Amazon Personalize Review, G2 User in Higher Education

5. machine-learning in Python

machine-learning in Python is a project that provides a programmatic-API and web interface for machine learning algorithms, including support vector regression (SVR) and support vector machine (SVM).

What users like:

"Several advanced models for machine learning are available in Python. This allows you to perform up-to-date experiments. There are many tutorials for using machine learning with Python, and the most modern systems use it.

If I have any problem with the output or any error, there are many internet forums showing any possible solution. That encourages me to use it because I can be sure of solving any problem I may have. If you don't find the solution, you can post a question and wait for an answer in the following days.

machine-learning in Python also allows using HW acceleration such as GPUs. You only need to set the proper HW. Another advantage is the fact that there are several libraries for doing machine learning with Python. In case you don't like any, you can choose among the others."

- machine-learning in Python Review, Alvaro R.

What users dislike:

"It has provided many implementation methods, which is quite good but raises too much confusion at the same time. So one needs to do some research as to which one to select among the available options."

- machine-learning in Python Review, Neha S.

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, MLaaS can help meet these requirements to make their goals a reality. In short, MLaaS enables you to effortlessly go from zero to hero in machine learning.

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.