Machine learning has become an essential tool for organizations across verticals to sustain in the cut-throat competition. The global machine learning market is projected to hit $30.6 billion Opens a new window over the next four years. However, machine learning platforms are expensive and difficult to integrate with on-premise systems. To overcome these barriers, more companies are turning toward Machine Learning as a Service (MLaaS) cloud platform providers. With MLaaS, they are freed from building their own ML infrastructure that comes with heavy investments for storage and computing power. Besides, there is no need to hire high-paying engineers and data scientists to get started with the ML application. MLaaS platform providers use their own data centers in the cloud to handle underlying infrastructure issues involved in machine learning.Â
Understanding the Benefits of MLaaS Platforms
MLaaS platforms equip companies with the tools needed to develop, deploy, and monitor ML algorithms — everything from data pre-processing, model training, and evaluation to model management and deployment. Depending on the platform, MLaaS provides your team with the tools for data visualization, face recognition, natural language processing (NLP), image and voice recognition, predictive analysis, and deep learning, that helps simplify integrating machine learning into your business or industrial processes. Even small- and mid-size companies that lack ML talent can benefit from pre-built algorithms and technologies from a cloud vendor, which will entail a much smaller initial investment than building ML algorithms in-house. IT teams can benefit from solutions that include a code-free visual interface, pre-trained models, and readymade AI services. They can also take advantage of the code-based environments of MLaaS platforms to develop custom machine learning models from scratch. Before deciding which platform is best suited for your business needs, it is crucial to determine what you want to achieve with machine learning.
What Do You Plan to Achieve With Machine Learning?
Despite the various advantages of MLaaS, organizations must first decide what they aim to achieve before deciding on a platform. High-level services include text recognition, translation, textual analysis, recommender systems, forecasting, machine translation, automated transcription, speech generation, conversational agents, image and video recognition. However, not all platforms come with all these services, and each is known to differ somehow. For example, Microsoft platforms are known to have the richest set of services, while Google provides the most versatile toolkit for image analysis. But only Amazon’s video analysis supports streaming videos. Therefore, it’s important to determine your ML goals before zeroing on the platform best-suited to the business model.Â
With this in mind, here is a list of the top seven cloud MLaaS platforms that you can choose from in 2021.
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Top 7 MLaaS Platforms to Choose From
If your team is new to data science, you can start building your first working ML model with a relatively small initial investment with these platforms.Â
1. Amazon MLOpens a new window
If you are looking for a fully automated solution, then Amazon ML is the right choice. Amazon ML is the ideal fit for deadline-sensitive operations. It can load data from multiple sources and perform all data preprocessing operations automatically. Using visualization tools and wizards, you can create a model that generates predictions for your application without code generation or infrastructure management. Prediction capacities are limited to binary classification, multiclass classification, and regression. While this platform doesn’t support any unsupervised learning methods, and you must select a target variable to label it in a training set, Amazon ML chooses the learning method automatically after checking the data that has been provided.Â
2. Microsoft Azure Machine Learning StudioOpens a new window
If you are looking for a drag-and-drop interface, Azure ML Studio might be the ideal choice for you. Almost all ML operations are completed using a GUI, including data exploration, data pre-processing, choosing different methods, and validating the modeling results. Supported methods include classification (binary+multiclass), anomaly detection, regression, recommendation, text analysis, and clustering. If you are starting out with machine learning, ML Studio would be the apt choice for introducing ML capabilities to employees who are new to machine learning and may not be familiar with coding.Â
3. Google Cloud AutoMLOpens a new window
Google Cloud AutoML provides users with a GUI to upload their datasets to the cloud, train custom models, and deploy them on the website or your apps via the REST API interface. Cloud AutoML assists developers who possess limited machine learning knowledge and expertise in training high-quality models specific to their business requirements. AutoML services include image and video processing, natural language processing, and a translation engine. Supported methods include classification, regression, and recommendation. For experienced ML pros looking to implement machine learning on a wider scale, the following would be the best platforms for making it all possible without having to attend to the underlying infrastructure.
4. Microsoft Azure Machine Learning ServicesOpens a new window
Azure Machine Learning Services is Microsoft’s cloud infrastructure meant for building, experimenting, and deploying models at scale, using any tool or framework such as TensorFlow. The Azure ML Services platform provides professional AI developers and data scientists who are proficient in working with Python with an environment for hosting, versioning, managing, and monitoring models running in Azure and on-premise and on Edge devices. Models can be deployed into production in a third-party service such as Docker. Unlike Microsoft’s ML Studio, there are no built-in methods, and thus, it requires custom model engineering. For those interested in building bots, Azure ML provides a complete environment for building, testing, and deploying bots by using different programming languages.
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5. Amazon SageMakerOpens a new window
Sagemaker is Amazon’s MLaaS platform for ML pros. It provides data scientists with tools for faster model building and deployment. This platform is accompanied by a multitude of integrated ML algorithms and pre-trained ML models. Its built-in algorithms are perfectly optimized for large amounts of computations and datasets in distributed systems. Its chatbot AI lets you build “Conversational Interfaces†into any application through voice and text by using the advanced deep learning techniques of Automatic Speech Recognition (ASR).Â
If you don’t wish to use SageMaker’s built-in tools, you can add your own methods and run models via Sagemaker’s deployment features or even integrate SageMaker with other ML libraries such as TensorFlow. In short, Amazon ML lets you dig deep into dataset prep and modeling. As such, it would be a good choice for those organizations that already use Amazon cloud services and do not intend to move to another cloud provider.Â
6. Google Cloud Machine Learning EngineOpens a new window
Google Cloud Machine Learning Engine is an MLaaS platform intended for ML specialists and experienced engineers. It employs cloud infrastructure with TensorFlow. While TensorFlow is ideal for deep neural network tasks, this tool is not confined to those tasks only. Google Cloud ML includes an extensive set of pre-built algorithms, a set of building block components for image/video analysis, language and sentiment analysis, and a JupyterLaB integrated enterprise notebook service for ML framework management.
Also included are virtual machines that are preconfigured and deep learning containers that can be used for rapid application development and hosting models as hosted prediction engines. Google provides Dialog Flow, a linguistic and visual bot-building platform for building bots to design and integrate a conversational user interface into mobile applications, web applications, and interactive voice response systems. This tool can analyze various types of inputs, be it text or audio information.
7. IBM Watson Machine Learning StudioOpens a new window
Unlike Amazon, Google, or Microsoft, IBM Watson Machine Learning Studio is intended for both experienced data scientists and newcomers alike as they work together to build ML applications. Data scientists can use this platform for developing analytical models and would be able to simultaneously train the model with their data and integrate it into native applications. However, business-level analysts may experience some difficulty with its user interface. Watson ML Studio provides a fully automated data processing and model building interface that hardly requires any training to begin data processing, preparing models, and deploying them into production.
It automatically supports three groups of tasks: binary classification, multiclass classification, and regression, or you can manually pick from the ten methods to cover these tasks. Watson ML Studio’s notebook tools for R, Python and Scala aid data scientists in analytics. This also comes with SPSS, a software package that can transform data into statistical business information, and Neural Network Modeler for processing visual and textual data. IBM also provides the complete infrastructure to build and deploy bots capable of live conversation, which leverage entity and user intent analysis in messages. To ensure that an organization’s AI automated technology helps make sound decisions, IBM offers extensive support for explainability, bias, fairness, accuracy and drift monitoring, synthetic data, and differential privacy.Â
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In Conclusion
As mentioned, first, it is essential to determine what you plan to achieve with machine learning and then choose one from the above-listed options that suit your ML needs the best. Also keep in mind that to reduce the time spent on configuring a data source, it is recommended that you choose the same provider for your storage as you do for your MLaaS platform. Challenges arise if your ML workflow comes from multiple sources. However, before making a decision, bear in mind that some of these platforms can be integrated with other vendors’ storage, e.g., Azure supports Hadoop, along with its own storage products. However, on the flip side, MLaaS platforms also come with some significant disadvantages that need to be kept in mind. For instance, if a company deploys event-driven machine learning, it might require a specific data management framework to align online and offline data, which is almost impossible with MLaaS.
Also, when companies resort solely to readymade solutions provided by MLaaS, they run the risk of losing in-house expertise, which may compromise their strategic advantage. Finally, as is common when a company becomes too dependent on a single provider, it risks a change in its product offerings, pricing options, and product or service characteristics, which could detrimentally affect its business activities. For these very reasons, it is safe to say that MLaaS platforms are a better fit for freelance data scientists, startups, or companies in which machine learning is not one of the most important activities. Larger companies, especially those operating in the tech industry and those who focus heavily on machine learning, would likely be better off building their in-house ML infrastructure that fits their specific business requirements.
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