Machine Learning as a Service (MLaaS): The basics

Machine learning is the science of getting computers to act without being explicitly programmed. It is currently the most popular form of AI (Artificial Intelligence). Rather than relying on fixed deterministic rules, machine learning programs use algorithms that learn from data and make predictions based on that information. Nowadays, machine learning programs can be executed as stand-along programs or as cloud services. Machine Learning as a Service (MLaaS) refers to cloud services that enable their users to take advantage of machine learning programs without having to build their own infrastructure or having to hire expensive machine learning experts and data scientists.

The MLaaS concept is similar to other forms of traditional cloud services like Infrastructure as a Service (IaaS). However, traditional IaaS services are more focused on managing infrastructure, while MLaaS focuses on helping developers build applications using Machine-Learning (ML) algorithms without having to build an entire system from scratch. MLaaS is gradually becoming a popular solution for businesses that look to leverage the power of Big Data analytics, Natural Language Processing (NLP), and other ML-related models and services like data exploration, anomaly detection, image recognition, and recommendation engines. To this direction, MLaaS services provide access to pre-built algorithms, tools, and infrastructures. Specifically, they enable their users to access fully managed services that make it easy to build, train and deploy ML models. Moreover, they provide end-to-end workflows for developing, training, and deploying predictive analytics applications on cloud infrastructures like Amazon Web Services (AWS), the Google Cloud Platform (GCP), and Microsoft Azure. There are also MLaaS services that analyze public or private data sets in seconds without a need to write code. Such services leveraging analytical functions and applications written in programming languages like Python and R, such as sentiment analysis, clustering, and other data mining functions.

Common MLaaS functionalities

As already outlined, MLaaS services provide support for all functions involved in the development and deployment of ML pipelines. Prominent examples of such functions include:

• Explorative Data Analysis (EDA) as a Service: To support EDA, MLaaS services offer access to cloud-based tools that enables users to create interactive visualizations and dashboards. To this direction, these services support access to both SQL and non-SQL data sources, while offering prebuilt templates for common use cases like sales forecasting and customer retention analysis. Popular EDA functionalities include the creation of dashboards from spreadsheets, including support for designing and formatting different types of charts. Moreover, cloud-based EDA is facilitated through sharing entire datasets in various forms that range from simple CSV (Comma Separated Values) files to entire workbooks.
• Anomaly Detection as a Service: Anomaly detection is one of the most common types of ML functions that uses historical data to detect and flag unusual activity. It is very often used in applications like fraud detection, where it helps to identify patterns that indicate someone has attempted to commit fraud. MLaaS services provide access to build in anomaly detection functions, including supervised learning, unsupervised learning and reinforcement learning algorithms.
• Image Recognition as a Service: There are many MLaaS services that collect and analyze non-textual data such as images. One of the most common MLaaS use cases for image recognition is for example to identify and tag images. This identification and tagging can take different forms based on a wide range of features including face detection and recognition, text analysis (i.e., Optical Character Recognition (OCR)), object detection, object tracking in collection of images and videos, as well as facial landmarking. Some of the MLaaS services for image recognition also provide support for challenging video scene analysis functionalities such as face tracking and real-time object tracking in video streams.
• Natural Language Processing (NLP) as a service: NLP is another popular MLaaS functionality, which is offered by several cloud providers. For instance, Amazon offers Comprehend, a natural language processing service that can extract insights from text. Likewise, Google Cloud offers the Natural Language API, a service that can analyze sentiment, entity recognition, and syntax analysis. As another example, Microsoft Azure provides Text Analytics, a service that can analyze sentiment, key phrases, and language detection.
• Recommendation Engines as a Service: Recommendation engines are a form of machine learning that use historical data to make predictions about future events. They’re used in many different industries, such as retail and e-commerce. Cloud providers enable the development and deployment of recommendation services on the cloud. For instance, Amazon uses its Personalize service to recommend products based on what customers previously purchased or searched for on their site. Google also offers an AI-based recommendation engine called Google Recommendations AI that helps businesses personalize the online experience that they offer to their users. They do this by providing them with relevant content based on their interests and preferences. Microsoft Azure Personalizer is another example: it enables the development of personalized experiences using real-time insights from customers’ behavior across multiple channels like chatbots, emails or websites.
• Models’ execution as a service: There also cloud tools and services that facilitate the execution of machine learning models. For instance, Google AutoML Tables is an automated machine learning tool that allows enterprises to build custom AI models using only spreadsheet data as input. Thus, Google AutoML users do not need any previous experience with machine learning or coding in order to use this tool.

Overall, there has been a rapid evolution of cloud services to support machine learning functionalities. The latter have had a positive impact on the machine learning ecosystem, as they lower the barriers for users to use and for developers to build sophisticated machine learning systems. MLaaS is a great way for businesses to get started with machine learning without having to invest in expensive hardware or software. It also allows companies that do not have dedicated data scientists on staff, to access advanced analytics tools without needing technical expertise themselves. This makes MLaaS ideal for small businesses who want access to powerful technology but aren’t able or willing to hire their own dedicated team members just yet.

In conclusion, MLaaS has become a popular solution for businesses looking to leverage the power of big data analysis, Natural Language Processing, execution of models like regression, data exploration, Anomaly detection, Image recognition, and Recommendation engines. Cloud providers like AWS, Google Cloud, and Microsoft Azure offer a range of services for MLaaS that can help businesses gain insights, improve customer experiences, and drive business outcomes.