Published on

January 8, 2024

ML as a Service

Machine Learning as a Service: What it is and How to Use It

MLaaS is revolutionizing AI. Learn about what Machine Learning as a Service is and how to use it.
Jon Reilly
Co-Founder, Co-CEO, Akkio
ML as a Service

MLaaS is revolutionizing AI. Learn about what Machine Learning as a Service is and how to use it. Machine learning has become a buzzword in recent years, with companies like Google, Facebook, and Amazon all embracing the technology. The idea is to learn from data to provide services that are better than what humans alone can offer.

But there are many challenges with traditional machine learning: You’d need to build a machine learning model from scratch, build and maintain powerful servers, manage data pipelines, and other technical infrastructure that can be quite tricky.  

That’s where Machine Learning as a Service (MLaaS) comes in. It automates many of the steps required to build a machine learning model and manage its deployment process. So instead of spending weeks or months building your own models and deploying them on your own servers, you can get started much more quickly by using an MLaaS platform.

There are several different kinds of MLaaS platforms out there: Some focus on specific tasks like image recognition or text-to-speech (for example) while others aim for more general applications that cover a variety of industries like sales and marketing. 

In this article, you’ll learn everything you need to know about MLaaS.

What is Machine Learning?

Machine learning is the hottest technology in Silicon Valley, and no wonder: It promises to revolutionize a huge swath of our daily lives. 

Google uses machine learning to personalize search results, Facebook uses it to suggest friends’ posts, and Amazon uses it to predict which products you might want based on what you buy and browse. 

The basic idea behind machine learning is that you can use a computer to try to teach itself how to perform some task. Machine learning is a subset of artificial intelligence that’s focused on learning from data, and itself includes subsets like deep learning, or using neural networks on big data. This is different from more conventional programming, where you instruct the computer exactly what to do. 

Machine learning starts with a set of data (say, photos) and then figures out how to extract useful information from that data. So for example, if we give the computer a bunch of photos of dogs, it might learn a description like this: There are four legs and fluffy fur. It has big eyes and a wet nose.

You can think about machine learning this way: The purpose of the program isn’t so much to produce predictions as it is to find patterns in training data by looking at examples and coming up with relationships between them.

Machine learning tools enable everything from more accurate regression to speech recognition, face recognition, predictive analytics, and computer vision. Machine learning services solve a wide range of business needs, including in customer experience (such as through better chatbots), healthcare automation, IoT, and more.

But it has traditionally been very hard to do it yourself - it requires phenomenal amounts of computing power, and used to require in-house data science teams that only the very largest of companies could afford. Traditionally, you’d need data scientists and cloud computing experts to work with cloud service providers, complex APIs, programming languages like Python, various IDEs, and libraries like TensorFlow, PyTorch, and SciKit just to deploy basic models.

What is ML as a service?

MLaaS is the use of technology to automatically train, test, and deploy machine learning models for you, typically by leveraging an AI platform with offsite server farms (a “cloud”) to run them on behalf of the customer.

In contrast to traditional SaaS firms, these companies don’t tend to sell an application to customers themselves; instead, they generally just provide access to algorithms and computational power so that customers can build and deploy their own models.

MLaaS is the next generation of machine learning, which has been around for decades but has only recently become mainstream. The goal of MLaaS is to make it easier and more affordable for companies to use machine learning, so they can get better insights from their data faster than ever before.

What is it used for?

MLaaS is used for a wide range of use-cases, from support ticket classification to churn prevention. The key about MLaaS is that it allows you to offload the heavy lifting of machine learning. This means you don’t have to worry about managing infrastructure, setting up servers, installing software, and so on. You just connect the relevant training data, select the column to predict, and let the service do its magic.

Let’s look at how MLaaS is used across a number of use-cases:

  • Natural Language Processing
  • Forecasting
  • Data exploration
  • Anomaly detection
  • Searching and understanding datasets
  • Regression
  • Image recognition
  • Recommendation engines

Natural Language Processing

Natural language processing can be used to better understand your customers, such as by analyzing social media posts and sentiment of customer feedback, allowing your company to make better decisions about how to market its products and services.

For example, if a user complains about an issue with the app, you can automatically send an email or message to the user offering support. Or if a user starts complaining about a competitor, you can automatically send an email or text message pointing out that there are better alternatives as well as other useful information about your products and services. 

Machine learning can also be used to classify incoming customer support tickets into different categories, such as whether it’s a billing issue or a technical issue. This allows you to prioritize your time and resources by automatically categorizing tickets so that you can focus on the most important ones first. 


MLaaS is also used for forecasting. For example, a company might want to forecast the number of orders that it will receive in the next month. With MLaaS, they can use historical data to predict future events. This is an example of supervised learning: The model is trained on historical data and then predictions are made based on that training set.

Cost modeling is another example of forecasting. Here, companies need a way to model the costs of products, services, or clients themselves. For example, for an insurer to predict the cost of a new patient, there are many things they must consider. They must think about the diagnosis of the patient, their age, symptoms, and any other associated data for this particular patient.

MLaaS offers the ability to easily build models based on past data to accurately predict cost.

Data exploration

Data exploration is the process by which you get a broad understanding of your data. That includes exploring variables, exploring relationships between variables, visualizing relationships, and so on—basic activities and the sort of things MLaaS can be helpful for as well. 

And it’s something analysts spend a lot of their time doing. At scale, it gets even more complex than that; to make smart recommendations about what patterns to look at next and how those should be interpreted in light of other patterns, and to put valuable information into context.

Anomaly detection

Anomaly detection is the process of detecting outlier events, such as fraudulent activity, from credit card fraud to insurance claims fraud. The technology works by looking at patterns of behavior that are similar to fraudulent activity in the past. 

This MLaaS use case is particularly useful for companies that don’t have the resources or expertise to build their own anomaly detection systems from scratch. It also allows companies to scale up their fraud prevention efforts without having to hire more staff or spend more money on new technology.

Searching and understanding datasets

Searching and understanding datasets is a very important task. It is the core of many applications, like recommender systems.

The most common approach to performing text search is to use a traditional database with SQL queries. However, these databases require technical expertise for searching or analyzing data, so you would still need to hire talent for that purpose.

Machine learning offers an alternative solution: you can tell the computer what to search for in natural language, and it will use machine learning algorithms trained on millions of examples to turn that into a SQL query. This is being done by companies like SeekWell, and could soon play a big role in the way non-technical users work with large datasets.


Unlike forecasting, which seeks to predict future events, regression is used to understand relationships between variables. For example, you might want to know how different advertising channels affect sales, or how different features of a product affect customer satisfaction.

With machine learning, you can build models that automatically identify these relationships. This is an example of unsupervised learning: The model is not given any labels or target values; it just looks at the data and tries to find patterns.

Image recognition

Image recognition is a process by which computers can “understand” and interpret digital images. It’s a form of machine learning that’s particularly well suited for mobile applications, since there are many use-cases where you want to be able to identify an object from a photo (e.g., identifying a flower from a photo taken with a phone).

Companies like Google and Pinterest have built very successful mobile apps that use image recognition. And there are many other potential applications, such as security (e.g., identifying faces in a crowd) and healthcare (e.g., diagnosing skin cancer from a photo).

Recommendation engines

Recommendation engines are a form of artificial intelligence that are used to predict what a user might want to buy or watch next. They work by looking at the user’s past behavior (e.g., what they’ve watched or bought before) and finding patterns in that data.

The most famous recommendation engine is probably Netflix’s, which recommends movies and TV shows to users based on their viewing history. Other companies, like Amazon and Spotify, also use recommendation engines to suggest products or songs to their users.

When should you use it?

The short answer is: when you need to solve a business problem that can be solved with ML. 

For example, if you have a finance team and want to use ML for fraud detection, then you should use MLaaS. If you have a marketing team and want to predict customer churn, then you should use MLaaS. And so on, from scoring leads to reducing employee attrition

That said, there’s one thing you can’t do without: data. Data is the fuel of machine learning. Without good data, your model won’t learn anything useful. So it’s crucial that your organization has access to quality data sets that are relevant for your business problems. 

But even if your organization has access to great data sets, there are still many ways in which they can fail or mislead machine learning models  —   especially if they are not used correctly. This is why it’s also crucial that your organization has access to skilled people who know how to clean and prepare the data for machine learning models. This is why it’s also crucial that your organization has access to skilled people who know how to clean and prepare the data for machine learning models.

Bias is one of the most common problems with data sets. It can be caused by a number of things, such as the way the data was collected, the demographics of the people who were sampled, and even personal biases of the people who prepared the data. If not carefully detected and corrected, bias can cause machine learning models to make inaccurate predictions.

Another common problem is missing data. This can happen for a number of reasons, such as hardware failures, software errors, or simply human error. Missing data can also be caused by deliberate attempts to hide or remove certain data points (such as in fraud cases). If not properly handled, missing data can cause machine learning models to produce inaccurate results.

Finally, impurity in data sets is another common issue that can lead to problems with machine learning models. Data impurity can be caused by a number of things, such as incorrect labeling of data points, contamination of data sets with outlier values, and so on. If not cleaned up properly, impurities in data sets can cause machine learning models to produce inaccurate results.

With that said, Akkio will automatically pre-process and clean your data sets for you, but while this automation improves the quality of data, it doesn’t mean you can completely avoid the problems mentioned above.

If you have a relevant business use case, and the data behind it, go ahead and get experimenting! You’ll be amazed at what MLaaS can do for you.

What are the best tools you can use?

The MLaaS market includes a wide range of ML services providers, including Google Cloud machine learning, Microsoft Azure machine learning, IBM Watson machine learning, and Amazon machine learning tools like Amazon Sagemaker, Amazon Rekognition, and Amazon Web Services. The functionality of these MLaaS providers’ machine learning solutions varies, but they generally cover the AI workflow from data visualization and data preprocessing to model training to real-time deployment.

For more complex functionality, like on-premises solutions or dedicated data centers, the pricing of any cloud provider will increase. Since cloud computing services have to manage massive GPU computation pipelines, and are priced accordingly, some startups opt for open source solutions, although that requires significant technical expertise. 

Google has a huge MLaaS offering, but it’s not as easy to use as you might think. You’ll need to know programming and software engineering concepts, particularly for deployment, which requires managing configuration files and running a series of commands.

Microsoft's Azure ML offerings are similarly complex, involving tools like the Azure CLI, which is a command-line interface for managing the Azure machine learning studio. If you’re not familiar with the CLI, it can take some time to get up and running.

Finally, Amazon’s MLaaS is built for technical experts as well. You’ll need to know a lot about programming, as well as how the various AWS tools work together, which can be difficult for less experienced users.

For the rest of us, there are other options. Akkio is a no-code solution made for all kinds of businesses. Akkio is an easier, cheaper, and faster solution than the competition, making the entire AI lifecycle effortless.

Akkio is an MLaaS tool designed to make ML accessible to anyone - even marketing professionals and business developers without a technical background can easily use Akkio to access powerful technology.

Unlike traditional AI software where you’re forced to pay to train ML models (that may never end up being used), Akkio doesn’t have any hourly training rate. With Akkio, any team can build models without hefty up-front costs to test processes. 

Akkio has a wide range of applications and use-cases - from lead optimization, financial fraud prediction, and sentiment analysis of free form text. 


Machine learning as a service is the future of AI. It’s already here, and it’s going to change everything. This is important for two reasons: First, because if you want to use AI in your business, you need to be able to access it on demand. Second, because the way we think about AI is going to change dramatically over the next decade or so.    

In the past, we have thought of AI as something that only big companies can afford. But now we have services like Akkio that make this technology accessible to everyone. And these services are getting better all the time.

Try an Akkio free trial to get started with MLaaS today.

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