Your Complete Guide to Machine Learning as a Service (MLaaS)

Machine Learning has been disrupting industries for over a decade. Although many people still find it confusing, it’s no longer unknown territory.

The Machine Learning industry is expected to grow at a Compound Annual Growth Rate (CAGR) of almost 39%, from $21.17 billion in 2022 to $209.91 billion in 2029. The rapid market growth is due to the increasing demand for automated data analysis solutions.

This brings us to today’s topic—Machine Learning as a Service (MLaaS). Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS) are examples of new cloud computing services that have emerged as a result of the evolution of software products into end-to-end solutions.

Now, Machine Learning as a Service is joining them as another concept that takes working on the cloud to the next level. Keeping data on the cloud and turning it into actionable insights has been a key consideration for businesses over the past few years, and MLaaS is the perfect solution for this need.

In this article, we’re introducing the concept of Machine Learning as a Service and showing you its most common use cases to give you an idea of how you can use it to improve your business.

Let’s start by taking a closer look at exactly what we mean by Machine Learning as a Service.

What is Machine Learning as a Service?

MLaaS as a product entails outsourcing the processes involved in integrating Machine Learning into your business to third-party experts and vendors, rather than creating your own.

MLaaS encompasses a number of services that involve Machine Learning algorithms as part of their cloud computing services. This includes:

• Pre-processing of data
• Model training
• Predicting future outcomes

Many cloud providers, such as Amazon, Google, and Microsoft have already included MLaaS as part of their portfolios.

The goal of MLaaS is to ease and automate actions like organizing and processing large amounts of data to turn it into valuable insights. At its core, Machine Learning attempts to make computers think as people do. It aims to make decisions based on previous data—much like a human makes decisions based on previous knowledge.

The use cases for MLaaS have increased greatly as technology has evolved, and Machine Learning models are able to achieve higher prediction accuracy when working with a wider variety of data.

We’ll take a closer look at exactly how your business can use Machine Learning, but first—let’s consider how this actually works.

How do Machine Learning models work?

A typical machine algorithm consists of the following three components:

1. A decision process: Machine Learning algorithms are used to produce predictions or classifications in general. The pre-trained algorithm will provide an assessment of a trend in the database on input data, which can be labeled or unlabeled.
2. An error function: the model's forecast is evaluated using an error function. If there are known instances, an error function may be used to assess the model's accuracy.
3. An updating or optimization process: the algorithm examines the errors it’s made and then modifies how the decision process gets to the final choice in order to improve the Machine Learning algorithm.

Netflix is one very popular example of putting Machine Learning and Artificial Intelligence algorithms into practice. They use historical data about the user—like the content they most frequently watched—and what they liked and didn’t like.

This data feeds the algorithm, which uses this information to return more accurate predictions the next time you log in.

A Machine Learning algorithm updates autonomously, and its accuracy increases with each run as it learns from the data it examines. As this recurrent learning occurs without human assistance, the algorithm is able to reveal hidden insights without being deliberately trained to do so—a key step in how machines learn.

Now, let’s see how MLaaS works.

How does MLaaS work?

MLaaS covers the value chain of Machine Learning in full, including:

• Data storage
• Data processing
• Model creation
• Model deployment
• Model training
• Quality control

All these segments are typically covered by one vendor, with different platforms containing different functionalities according to the specific services offered by each MLaaS platform.

MLaaS platforms should help you detect data patterns and create mathematical models that make predictions when fed new data. The idea is that the model analyzes data and produces predictions without the end users having to perform the actual calculation.

Common Machine Learning algorithms

Take a look at some of the algorithms MLaaS uses to develop unique workflows according to the client's demands:

• Convolutional Neural Networks. A convolutional Neural Network is a feed-forward Neural Network that processes data in a grid-like structure to evaluate visual pictures. Face recognition, picture classification, and other applications of CNN in Computer Vision are examples. It works in the same way as a basic Neural Network.
• Deep Learning is essentially a three or more-layer Neural Network. These Neural Networks aim to imitate the activity of the human brain by allowing it to ‘learn’ from enormous amounts of data.
• Probabilistic models are a statistical technique for predicting the likely occurrence of future outcomes by taking into consideration the impact of random events or actions. Predicting the weather and the traffic are two common examples.
• Bayesian inference is a statistical inference method that uses Bayes' theorem to modify a hypothesis' likelihood when more data or information becomes available. In statistics, and particularly in mathematical statistics, Bayesian inference is a crucial technique.

Some vendors provide solutions that go well beyond fundamental Machine Learning abilities like modeling, categorization, and clustering.

For example, some platforms enable you to discover abnormalities, develop a recommendation engine, and rate objects using Machine Learning as a Service. MLaaS companies also provide sophisticated APIs, which are services that have trained models that you can input your data and get results from.

What are the benefits of Machine Learning?

Organizations require help in sorting through and working with the massive amounts of data that our networks now generate on a regular basis. Businesses can employ Machine Learning technology to create automated systems that can handle large amounts of data fast and understand how to apply it to tackle problems.

In addition to the quick detection of patterns in data, Machine Learning models learn autonomously and don’t need a Human-in-the-Loop. The more data they get, the better their decisions become. This can automate a lot of manual processes, like labeling the products of an online e-commerce store with 1,000+ products for optimized product discovery.

In short, the major benefit of Machine Learning as a Service is that it saves you time—and lots of it. Sure, sometimes you’ll have to validate predictions to help the machine Learn—but overall it automates processes and tasks that you and your team waste time on every single day.

What are the limitations of Machine Learning?

Every coin has two sides—Machine Learning is no exception.

For an algorithm to function properly you need to invest some time upfront into training a Machine Learning model. ML algorithms need large amounts of data to produce accurate predictions—meaning that sometimes, you need to wait to get new data that will feed the algorithm.

Another drawback is that the algorithm can be prone to errors. If you feed the algorithm with a biased training set, you’ll get biased results. You need to ensure you’re using high-quality, bias-free training data—which can sometimes be difficult and time-consuming to source.

Machine Learning use cases for businesses

Now, let’s explain the Machine Learning use cases for various types of businesses.

Natural Language Processing

Natural Language Processing (NLP) is a branch of Machine Learning that refers to a computer's capacity to comprehend, interpret, modify, and perhaps synthesize human language.

NLP examines the language structures of sentences and the specific meanings of words. Then, it applies algorithms to extract knowledge and produce results. In other words, it understands human language so that it can accomplish various activities automatically—much like a human would when receiving certain information.

For example, when you’re searching for a specific term on Google, under the first result, Google shows you a list of questions related to this term. Google’s NLP algorithms understand the meaning of your query and return helpful results—even if your search term wasn’t comprehensive.

That being said, you don’t have to be Google to use NLP. For example, you can use NLP to analyze and categorize incoming emails based on content. This enables you to create your custom email rules and establish better control over your inbox.

Data exploration

Data scientists must first comprehend and establish a complete picture of the data before collecting pertinent data for further analysis, such as single or multivariate analysis.

Data exploration is a process in which users employ statistical and graphical approaches to look at and understand data. This method improves the identification of trends and issues in the dataset, as well as the selection of the model or algorithm to employ in later steps.

Most data analytics software contains data visualization tools and charting capabilities that make data exploration simpler at first, helping to decrease data by weeding out information that isn't needed or can affect findings over time.

You may start uncovering trends and determining if a given option is worth exploring—or if the information is less valuable—by taking the time to explore the information you have with data visualization tools.

Geographic Information System (GIS) software is one popular example of data exploration in practice, being used to collect, manage, display, and analyze many forms of geographic and spatial data.

Interactive and dynamically connected graphic tools are used to explore data in the GIS. Maps, graphs, and tables are dynamically linked and presented in various windows so that choosing data from a table highlights the relevant features in a graph and a map.

Data extraction

Data extraction is a Machine Learning service that takes data from one place and transports it to a new location—whether on-site, on the cloud, or a combination of both.

Data extraction is an important step in automating structured data collecting in preparation for subsequent analysis. The procedure gathers data from a variety of sources, such as receipts, emails, and contracts. This data helps in the automation of operations and provides useful insights and predictive analytics to make better decisions.

For example, you could be using data extraction to automatically collect customer feedback from various platforms, like Typeform and Zendesk, and categorize it based on the product feature it refers to by performing sentiment analysis.

Forecasting

The various data and KPIs at your fingertips, regardless of your industry, are gold you can use to obtain more accurate business forecasting. Because of its enhanced capacities to be precise, scale, adapt to variable behavior, and provide results in real-time, Machine Learning can independently fuel these forecasts.

Business forecasting is the practice of estimating and predicting future changes in departments such as marketing, financial revenue, and demand for resources and stock using time series data. Using MLaaS for forecasting can help businesses better use past data to improve business processes. It uses complex algorithms to evaluate data and find the best option moving forward.

Another forecasting application is with platforms that use maps. They can calculate the shortest route with the least traffic thanks to Machine Learning models. The algorithm examines traffic from a number of sources, considers complicated route dynamics, and draws on previous trip data to find the shortest route.

Computer Vision

Computer Vision uses Machine Learning models to teach computers to interpret and comprehend the visual environment. Thanks to Deep Learning algorithms, machines can recognize and categorize objects contained in digital pictures—including from cameras, and videos.

Computer Vision has the ability to:

• Classify objects into a pre-defined category.
• Identify particular objects from a picture or a video.
• Track the movement of an object in a video.

Pattern recognition is the foundation of Computer Vision algorithms. We use a lot of visual data to train computers—they analyze photos, identify items on them, and look for patterns.

For example, if we upload thousands of dog images to image recognition software, the Machine Learning model will process them and find patterns typically common for all dogs—such as four legs and a fluffy body. As a result, the next time the software sees a dog image, it will be able to identify the dog in it.

One of the most popular uses of this technology in today’s online shopping world is the automatic tagging of fashion items.

In their product catalog, fashion shops must handle a big number of distinct products. This requires hours of manual tagging according to variables such as color, season, length, and more. It’s time-consuming and unscalable.

AI-powered Computer Vision enables you to input labeled product images from your library and develops sophisticated Machine Learning models that can be used to identify new images automatically.

For example, when you get a new product image in your catalog, it will automatically be tagged with “long-sleeve,” “dress,” “red,” or any other product tag you have defined.

Speech recognition

Speech recognition is a feature that allows computer software to convert human speech into text. While it's sometimes mistaken with voice recognition, speech recognition is concerned with converting speech from a verbal to a written format, whereas voice recognition is concerned with identifying a specific user's voice.

Speech recognition employs Machine Learning models to interpret and analyze human speech by combining grammar, semantics, morphology, and content of audio and voice information. These models learn as they go—changing their reactions with each engagement.

Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana are all examples of speech recognition systems that use Machine Learning models.

How to use Machine Learning as a Service for your business

Machine Learning can help you in many business areas. To identify them, you should analyze your existing processes and problems and find out what are your needs. Based on these findings, you should start looking for a platform that suits your requirements. Here are some points to think of when choosing a platform:

• Ease of use.
• Scalability.
• Ability to integrate with your existing system.
• List of functionalities.
• Setup & implementation time.
• Responsive & helpful customer support.

Companies can now get a competitive advantage in the market with the use of Machine Learning technology and computing resources supplied by MLaaS. They’re able to offer similar services to their larger and more established competition without having to worry about complex and large-scale Machine Learning and data demands.

Reap the benefits of Machine Learning with Levity

As you generate more and more data, getting MLaaS for your company is an investment for the future. However, getting lost in a large number of solutions available is pretty easy.

When it comes to unstructured data, Levity is the ideal tool.

The Levity interface enables you to train your model in just a few clicks and allows you to limit errors by adding human review. You can then create AI workflows that connect with your existing tools to both extract data for analysis and automate actions following the machine's decision.

Join a Levity demo today to find out more about how AI can help your business automate the mundane and reach new heights of productivity.

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