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Recently, complex and expensive technologies have become accessible thanks to Software as a Service (SaaS) platforms. This also applies to machine learning, the purpose of which is the partial or complete automation of complex tasks in various fields of human activity.
Below, we’ll provide an in-depth comparison of Machine Learning as a Service providers: Amazon, Microsoft Azure, Google AI platform, and IBM Watson. We hope this overview will help you choose the best cloud provider and implement machine learning into your existing IT infrastructure without spending much on it.
Also, you can learn about data science services on our website.
Machine learning refers to artificial intelligence methods that train computers to find solutions to problems based on historical data without a predefined algorithm or the direct manual participation of the operator. This technology can be used for natural language processing (NLP), face recognition, data visualization, predictive analytics, data modeling, and more. Implementing such solutions from scratch is very expensive and time-consuming. This is how machine learning as a service was born.
Machine Learning as a Service, or MLaaS, is a part of cloud computing services. MLaaS providers offer advanced tools, including data visualization, APIs, face recognition, NLP, predictive analytics, and deep learning. At the same time, these providers offer the ability to deploy created solutions in the cloud and train models based on already prepared datasets (such as a database of human faces).
Google, Microsoft, Amazon, and IBM are the most well-known Machine Learning as a Service providers who offer machine learning tools. All of them offer trial ML solutions so that clients can evaluate the capabilities of the chosen platform before moving to a paid service.
The benefit of all these platforms is that clients can quickly start machine learning in the cloud without having to develop software from scratch and install their own servers. Clients of MLaaS vendors only have to pay for the services they use and data storage in the cloud (this is optional unless the company’s policy requires them to be stored locally). All solutions built with these services can be integrated with existing IT infrastructure through the REST API.
Thus, the main idea of implementing Machine Learning as a Service is to expand the target audience of this technology (first of all, in terms of the size of the company and the size of its budget allocated for the implementation of ML solutions). Indeed, if you decide to implement machine learning from scratch, your company will need to spend a considerable amount of money on this, not to mention the possible difficulties in finding a team of specialists who would bring your business idea to life.
This is why, to quickly and cost-effectively use this highly intelligent technology for business process optimization, it’s much easier to decide on an MLaaS supplier—fortunately, there are not so many leading Machine Learning as a Service players on the market.
With the introduction of machine learning in the cloud, all stages of ML-based software development, from training models to maintaining and updating them, become partly the responsibility of the chosen cloud provider. At the same time, the cost of the created solution is unlikely to reach hundreds of thousands of dollars, as is the case with building an intelligent solution from scratch.
Despite the fact that the MLaaS platforms discussed below differ in their functionality and pricing, they are all united by common principles for implementing the created solutions:
Optionally, you can integrate your model with third-party Machine Learning as a Service solutions and add code to optimize it (this option is available in all platforms listed above) through an interactive computing platform or built-in graphical interface (if you don’t have any experience with machine learning programming languages).
In the following paragraphs, we propose to consider our comparison of Machine Learning as a Service providers in detail.
Initially, Amazon’s MLaaS included two global platforms for developing machine learning solutions: Amazon Machine Learning and SageMaker. However, the first option has not been updated since 2021, and new clients can no longer integrate this service into their IT infrastructure. Therefore, we will discuss a more advanced and modern product launched in 2017: SageMaker.
SageMaker is a fully managed machine learning service that runs on Elastic Compute Cloud (EC2). This Machine Learning as a Service solution allows specialists to scale the created machine learning models, focusing on solving ML/AI problems and spending less time on maintenance and administration. At the same time, the service offers advanced features such as speech recognition, computer vision, and predictive analysis.
Amazon SageMaker provides companies with the tools to build, train, and deploy analytical and predictive machine learning models on EC2. This can greatly automate the workflows that their employees need to perform on a daily basis.
SageMaker supports all three types of machine learning: unsupervised learning, supervised learning, and reinforcement learning. As for the last option, in this case, the service is guided by advanced algorithms not available in any other solution. Thus, the speed of learning Amazon SageMaker is usually higher than analogs that provide identical capabilities.
Additionally, you may find it useful to know about the distributed training performed by Amazon SageMaker. With intelligent distribution of data operations across multiple GPUs, this platform provides unprecedented scalability. This ensures the conciseness of the program code that describes the model: it takes no more than ten lines.
As for the efficiency of model training, with SageMaker Machine Learning as a Service, this happens “in just one click.” At the same time, the platform focuses on the data source and types of instances, thus forming a distributed computing cluster.
To set up the model, Amazon SageMaker uses thousands of different parameters, resulting in the set that provides the most accurate prediction and improving these results over and over again. The choice of parameters is carried out automatically, without requiring any manual participation from specialists.
Amazon SageMaker Debugger also plays a special role in ensuring accurate results, changing the parameters of the created model in real-time to provide an even faster self-learning process. All this happens at the model training and testing stages, so that when it’s deployed on site, the efficiency and speed of the service are as expected.
If the standard features of this Machine Learning as a Service solution aren’t enough for you, and you need algorithms that demonstrate proven performance with unstructured graphical data, you can use Amazon Rekognition, a computer vision solution. This tool can be used for smart data classification, human face recognition, triggering event detection (on video), etc. It also does simpler things like identifying the quality of a face image (brightness and sharpness). It’s noteworthy that the training sample can only have a few dozen examples. Over time, the sample size can be increased to achieve even more accurate analysis results.
Two other useful tools within Amazon MLaaS are the Amazon Lex API for intelligent human speech processing and Amazon Polly. The first one can come in handy in the development of human-like chatbots (call center chatbots, QnA bots, informational bots, application bots, voice assistants, etc.), where you need to understand the correct context of the sentence left by the user instead of answering with one a pre-prepared question.
As for Polly, this service is based on neural networks and can turn written text into speech in newscaster and conversational styles. This can be helpful when creating program solutions that talk, from narrow-focused ones to apps for people with disabilities. For now, it supports English, French, Japanese, Korean, Chinese, Spanish, and many other languages.
The next tool to process natural language is Amazon Comprehend. This helps find insights and relationships in unstructured data within the text. In particular, the service can evaluate the tone of voice in product reviews, find keywords, and, in general, do everything that a regular search engine can’t do.
The most modern NLP and automatic speech recognition (ASR) deep learning techniques are used in these solutions, so human language, both oral and written, is recognized extremely accurately. Once created, you can integrate the chatbot in a few clicks, both into your custom application and into a ready-made solution like Slack or Facebook Messenger.
And finally, in addition to all of the above, Amazon offers a specialized service for the medical industry: Amazon Comprehend Medical. This provides users with information from the Medical Corpus information to define medical conditions, medications, and drug inventions.
Probably the most important advantage of SageMaker is the presence of a graphical interface, which in many situations eliminates the need to write code and greatly lowers the entry threshold.
This service offers access to an open-source Jupyter Notebook for code sharing and collaboration. Thus, system administrators and developers get the opportunity to adapt a ready-made notebook to the requirements of a particular system and train models with a specific data sample.
Also, SageMaker provides machine learning specialists with a colossal library of algorithms and individual blocks of program code packaged in Docker containers.
In terms of compatibility, Amazon SageMaker is a strong player. This service integrates with many Deep Learning as a Service solutions such as TensorFlow, PyTorch, etc., and allows data uploading from different sources like .csv files, Amazon Redshift, Amazon RDS, and more.
The pricing policies of this service are rather affordable: for the first two months, you don’t have to pay anything. Further calculations will be quite complicated. See specific examples on the official Amazon website.
The Microsoft Azure artificial intelligence platform is another cutting-edge solution with built-in services and APIs to implement machine learning quickly and cost-effectively. To better meet the clients’ needs, Azure is divided into two main solutions: machine learning services and bots.
The first solution is presented in the form of a large library of algorithms tailored for self-learning and based on samples with different types of data. In addition, there’s a development and testing environment for software algorithms. In particular, Azure ML services include a large number of Python packages, model management tools, AI-based solutions, and plug-ins built with Visual Studio.
As for the bot-building service, Microsoft Azure artificial intelligence offers numerous development tools, five large, well-thought-out templates, and a testing environment. The great thing is that the creation of bots is not limited to one programming language. In particular, you can use your Node.js and .NET programming skills to develop them. To run these bots, you can use off-the-shelf solutions like Skype, Bing, and Facebook Messenger, or integrate them with an existing custom application.
Objectively, Azure ML services are currently considered the most versatile product that exists in the market and provides the most extensive selection of tools for specialists of various levels. There’s also a convenient API for integrating with any standalone solutions—both from other vendors and from the Microsoft AI platform itself.
For model training with the Azure machine learning service, you can use a library with more than a hundred methods for evaluating and sorting input data, which, with the help of already trained models, provide precise analysis and evaluation of raw data.
You can also train models in the AutoAI Model Builder, which is incredibly user-friendly and adapted for beginners.
This proprietary service from the Microsoft AI platform doesn’t stand still—it’s constantly being improved. Over time, new features have been added to this progressive solution.
For example, a great feature that didn’t appear immediately is the training and retraining of models through the API. From a practical point of view, this is very convenient, as it doesn’t require developers to create a new training model from scratch (instead, you just need to load new data samples). This indicates that the Azure platform is remarkably predisposed to changes that may occur in your company and in your business niche as a whole.
There’s another feature that you may find useful: the Microsoft Azure Artificial Intelligence Gallery. It’s based on already-processed data samples that scientists from all over the world have worked with. In practice, this can be used for analytical and predictive decisions. In many cases, this becomes the only way to implement a highly intelligent solution for which, for some reason, you couldn’t derive input data.
A very important event in the history of Azure ML services was the addition of Python support, which implies the smooth compatibility of all elements of the platform and ensures that developers will be provided with hints to improve the efficiency of training algorithms. The platform also allows the compilation of a single project from blocks in different programming languages. Thus, for each individual component, you can choose the language that ensures maximum performance and code stability.
Want even more options? Visit the Azure ML Community Gallery, where developers share their experience of developing training models with Azure ML with each other. This web resource will be equally useful for beginners to learn the basics of working with Azure machine learning service, and experienced professionals to find new approaches to creating training models.
An important advantage of the Azure machine learning service is the lack of need to write program code from scratch: using a graphical interface, you can set up and deploy a machine learning solution in minutes. For this, Azure also provides Automated ML, an SDK that minimizes the need to write code. All this is achieved through high-level automation and support for advanced algorithms for classifying heterogeneous data and forecasting based on already-processed data.
Azure has access to Jupyter, which provides specialists with direct access to the capabilities of ML Studio. Speed optimization tools such as ONNX are also available here. This solution demonstrates the same efficiency regardless of the platform or operating system of the host device.
It would also not be superfluous to say that over time, the team behind the Microsoft AI platform has optimized the intuitiveness of the user interface. Thanks to this, training models can be created and adapted very fast. When dealing with Azure ML, you’ll get access to Azure Machine Learning Designer, which comes with an intuitive drag-and-drop GUI for a better and faster development experience. Through the interface, you can test various methods of machine learning in the cloud to choose the best option. The created solution can also be deployed on a web server.
Azure ML provides REST endpoint deployment of registered models, thereby ensuring their seamless integration with business applications.
As for data types, Azure supports a wide range of data sources for import, ranging from SQL databases and Azure internal storage to Hive queries and lists of web resources. To bring data to a common format, Azure ML uses proprietary algorithms and also opens up opportunities for developers to write their own algorithms based on various programming languages specifically designed to work with large amounts of heterogeneous data.
Solutions built using these advanced capabilities can be clustered and deployed to the cloud for testing or implementation in minutes. And this is just the beginning. For commercial projects, Azure provides the ability to download from the Azure Machine Learning Marketplace.
Azure offers users a free trial. After that, you should visit the official Microsoft website and apply filters to customize pricing options for services you need.
Let’s move on to the next player: Google AI services. This solution, like the previous two, has several separate products for specialists of different levels at once. The first is no-code Cloud AutoML, which is ideal for developers with basic ML skills, and the second is Google Cloud Machine Learning Engine, for advanced specialists who have experience with different types of data.
To work with Google Cloud AutoML, users need to load a prepared dataset, select an algorithm, and start the training process. The model can then be deployed to the cloud, which is also provided by Google. Thanks to a low entry threshold, this solution allows you to work even with complex samples from unstructured data, such as images and videos, as well as with human speech (thanks to advanced proprietary NLP algorithms). This platform has a set of ready-made models available through a set of APIs to save users from most programming-related tasks.
As for the Google Cloud ML Engine, this is a more flexible service that is tailored for the development of analytical and predictive solutions based on complex learning models. For this, Google provides its own GPU and Tensor Processing Unit (TPU) infrastructure. Along with pre-trained models created by the Google team, this engine allows you to implement your own algorithms and build your own containers for deployment.
Google AI services provide its users with the power of computer vision through deep learning VM images. When using this tool, deep learning algorithms are applied. At the same time, full compatibility with third-party solutions is ensured.
Another useful feature is the Google AI platform notebooks, where you can build, configure and manage virtual machine instances.
The data labeling service deserves special attention. It’s a great tool within the platform that allows you to label data according to its type. Advanced Google algorithms independently categorize the incoming data, simplifying their sorting and processing.
Finally, you should check Vertex AI, an ML and AI platform that allows developers to train and deploy ML models and AI applications. This solution became the training and deployment environment of several outstanding solutions, such as Nasnet, MNasnet, EfficientNet, NAS-FPN, and SpineNet.
Let’s start with Google Cloud AutoML. This is an ideal advanced solution for users with minimal technical background. It’s highly automated and provides a graphical interface for building self-learning models.
If we talk about the Google Cloud Machine Learning Engine, this engine provides both REST API and CLI to manage models. For even more convenience for specialists, there is also a Google Cloud Console.
Google Cloud AutoML and Google Cloud Machine Learning Engine solutions integrate with TensorFlow, another Google product for implementing machine learning models (note that this is not an MLaaS product). Thanks to this, specialists can combine several models at once in the same project—both created from scratch using third-party tools and ready-made ones that were provided by this Machine Learning as a Service solution. For even more convenience, it supports integration with PyTorch, Jupyter, and many other useful solutions.
The Google AI platform also provides flexible deployment options through the REST API, allowing you to upload pre-trained models either to a website or integrate them into complex AI-based infrastructures. Of course, it’s also integrated with all Google services and allows you to upload already working models to the cloud.
At the same time, this product offers extensive integration with third-party solutions through the Predictive Service. Thus, you can implement the models built with Google AI services both in ready-made, well-known services and in your own business applications.
Google AI platform provides an online calculator to estimate the budget for ML implementation.
And finally, the last player on our list: meet the IBM machine learning platform. IBM has developed its own solution, offering a set of intelligent services available through application programming interfaces. Today, Watson offers automated services for natural language recognition, machine translation, text sentiment analysis, image recognition, and intelligent bots.
Although this solution is less popular than the three above, it also includes the tools you need to use for the ML filecycle, from data collection and structuring to deploying and optimizing trained models.
At the same time, unlike the three services described above, the IBM machine learning solution can be effectively used equally by both beginners and specialists with many years of experience in machine learning. The latter can take advantage of the platform to create complex projects based on predictive analysis, which can then be integrated with other third-party solutions.
Watson Studio offers beginners AutoAI with a fully automated data processing and model-building interface. It’s characterized by requiring a minimal tech background and can be used by specialists with minimum experience in creating ML-based solutions.
This automated tool allows you to solve three main types of problems: binary classification, multiclass classification, and regression. The data analysis method can be selected both manually and in auto mode (this mode chooses the optimal method by applying its own algorithms). In total, IBM has more than ten methods, including logistic regression, decision tree classifier, linear regression, isotonic regression, etc.
Deep neural network training deserves special attention from Watson cloud services users (not all providers have this feature). It enables data scientists to visually design their neural networks and scale out their training runs. Optimized for production environments, it scales up using the NVIDIA Tesla V100 GPU and can be deployed to the cloud or to edge servers. That’s why IBM can compete with a seemingly stronger rival, Azure ML Studio.
One of the key features of Watson cloud services is SPSS Modeler. This is a tool for modeling neural networks through a special graphical interface. You can find it directly in Watson Studio. The main focus of the service is on the capabilities of deep learning and working with big data.
In particular, SPSS Modeler helps you take advantage of innovative open source solutions and ML programming languages, including R and Python, increase the productivity of data scientists of any profile, and benefit from a hybrid approach—on-premise and in the public domain, or private cloud.
Being the leading visual data analysis and machine learning solution, SPSS Modeler helps organizations accelerate ROI and achieve results by accelerating operational tasks for data scientists. It offers complete sets of algorithms and models ready for immediate use. It’s suitable for hybrid environments with high management and security requirements.
Also, SPSS Modeler can be used to monetize data assets. This tool is also available in IBM Cloud Pak for Data.
The IBM machine learning platform has a structure similar to that offered by other vendors. In particular, this platform provides two approaches to building and training the models: automatic with a GUI for beginners, and manual for experienced professionals.
As for the automated solution, Watson ML Studio provides a fully automated interface for data processing and model building. It supports three task groups: binary classification, multiclass classification, and regression. Along with this, you can always add code written in Watson ML Studio notebooks for R, Python, and Scala.
IBM Watson Studio gives professionals the power to run model training in any type of cloud environment. Several options for managing these models are also available here.
As we noted earlier, IBM Watson cloud services provide access to Jupyter to reduce the level of automation in the creation of training models and adapt them to the specifics of your business. You can also integrate the created models with projects based on TensorFlow, scikit-learn, PyTorch, and others.
As for data sources available for Watson cloud services, they can be stored in any type of cloud system. At the same time, their processing will be equally effective due to the proprietary Cloud Pack solution.
The pricing model of the IBM Watson machine learning platform includes a free trial. As for paid options, you can leave a request on the official IBM website.
Now, let’s move on to comparing the above MLaaS platforms. Note that in our comparison, we don’t insist on which of the services is better and which is worse. Instead, we’ll simply push you to think about the best fit for your business and its prospects.
First of all, Machine Learning as a Service can be useful not only for those companies that want to save money but also for those who want to compare analytics obtained using internal tools with analytics from leading ML service providers. So, let’s analyze a table with a comparison of cloud providers discussed in our article.
If this table of major ML service vendors isn’t enough to make a decision, let’s evaluate the business risks associated with the choice of a particular vendor and the Machine Learning as a Service model as a whole.
So, let’s consider whether the MLaaS you choose will meet the needs of your business. Does it have all the tools you need to create the most effective solution based on machine learning in the cloud?
In fact, all of the above vendors offer an extremely comprehensive set of tools, support for all three types of machine learning, and dozens of integrations. However, not all of them are equally easy to use and all of them have different pricing policies and data processing algorithms.
Another important aspect to take into account is the cloud services that your company already uses. In particular, if you have previously migrated your IT infrastructure to the cloud—for example, to Amazon—it will be easier for you to choose the services of the same provider for seamless integration with a new product based on machine learning. Or, if this solution doesn’t suit you for some reason, you can migrate your existing infrastructure to the cloud provider whose MLaaS suits you best.
You will also need to weigh the pros and cons of using MLaaS in general, as it’s by no means a one-size-fits-all solution for those who want to implement machine learning. The fact is that, for example, to manage events, companies may need to use systems for synchronizing real-time and batch data. And this scenario isn’t possible with MLaaS. Also, if there’s a large percentage of noise in the input data, the lack of manual adjustment of the sorting process (which is typical for MLaaS) greatly degrades the final accuracy of the results.
On top of that, machine learning algorithms are complex. This means that without having highly specialized skills, it’s difficult to put them into practice even if a graphical interface with a high degree of automation is available.
That’s why the main benefit of using MLaaS should be sought in the consonance of these services with your business goals. Therefore, if you are looking for a solution to quickly and affordably implement a high-intelligence solution into your business processes, you should definitely consider the offers from cloud providers that we described above.
The next factor to take into account is the potential decrease in the company’s internal expertise (since, with the introduction of MLaaS, most of the analytics come from pre-trained vendor models). As a result, niche companies may experience inaccurate results. Therefore, very often, to ensure high-quality analytics, such companies integrate solutions created using Machine Learning as a Service with products based on machine learning, with those that were created from scratch.
Finally, you must understand that by choosing one or another MLaaS provider, your company becomes limited by its services. Sometimes, this affects not only the further development of the company but also the pricing policy of the goods and services it provides. This is sometimes not profitable in the long run, so before implementing a quick and efficient solution, it’s important to analyze whether the company can maintain its business direction and the characteristics of its products in the conditions of cooperation with the selected supplier.
Note that such difficulties usually accompany large-scale companies with an already-established digital architecture that depends not on one, but on many vendors. Therefore, committing to only one of them can lead to significant costs and inefficient changes to the existing infrastructure. Therefore, in such cases, it’s worth considering the possibility of building a solution from scratch based on machine learning technologies.
We hope our comparison of MLaaS providers will help you choose the best one.
In general, MLaaS is a very powerful business model because you can either spend time and money on training the model yourself (which sometimes costs thousands of dollars), or you can purchase a pre-trained model for less, which is much more profitable.
The big advantage of such platforms is the huge amount of data that they are able to accumulate due to the infrastructure scaling. Thousands of applications send their data, which is then used as a dataset for additional training of the system. With this approach, companies manage to solve the problem of lack of data.
So, if used properly, machine learning based on a ready-made MLaaS solution can quickly and effectively improve the capabilities of its products and company services, automate the vast majority of work processes, increase the efficiency of interaction with customers, and protect activities from possible business risks by choosing the most effective strategies for further development.
From a practical point of view, data scientists get access to ready-made models and algorithms, the creation of which leading developers and scientists from all over the world worked (you can learn more about data science team structure here).
Thus, the time for the implementation of the model training pipeline is reduced by thousands of times, which has a positive effect on the budget of the solution being created. This means that companies get the opportunity to focus on their core business and not hire new specialists on an ongoing basis, which can be very expensive and irrational (not to mention the fact that finding really good experts who know how to work with data for machine learning isn’t an easy task).
Thus, with a correct comparison of all the advantages that MLaaS brings and the possible risks associated with its implementation, you get an extremely effective business model that will ensure end-to-end optimization of your business activities and provide new, previously unavailable prospects for its development.
The introduction of artificial intelligence isn’t limited to the use of disparate models or the development of special algorithms. It should be a holistic initiative to change the way the entire organization functions. To do this, vendors offer Machine Learning as a Service solutions. For example, according to experts, the volume of transactions in the global MlaaS market will grow to $8.48 billion by 2026. Major players in the ML as a service and offline machine learning markets predictably include Google Cloud ML, Azure ML, Amazon, and IBM Watson.
It’s very easy to get confused in the variety of available solutions for those who haven’t dealt with cloud systems before (since, along with differences in functionality and algorithms, they differ in price and in areas of application). This situation is quite typical for such an immature market as Machine Learning as a Service. This is why, when choosing the best MLaaS provider, it’s important to clearly identify the purpose of deploying machine learning models and only then choose the one that provides the most complete set of tools at an affordable price.
Another problem is finding specialists who will get the most benefits from the chosen MLaaS. So, if you are looking for experts who will guide you from start to finish on the path of introducing machine learning in the cloud into your company’s existing workflows—from choosing a supplier to deploying training models—just contact us!
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