The Future of Machine Learning As-A-Service (MLaaS)

The Future of Machine Learning As-A-Service (MLaaS)


Machine learning as a service (MLaaS) is an emerging technology that consists of developing machine learning-based applications. The development of MLaaS services generally involves three primary steps. To begin, enterprises need to develop machine learning algorithms and train them on appropriate training data. They then deploy these algorithms to a cost-effective cloud infrastructure where they can run in parallel on large numbers of nodes and utilize the computing power of cloud providers.

Machine learning as a service (MLaaS) is the use of any machine learning technologies on demand. It helps organizations to implement and scale machine learning models efficiently and cost-effectively.

As machine learning accelerates, the demand for MLaaS solutions will rise along with it. 

According to a report by prnewswire.com, by 2030, the market size of machine learning is expected to grow a staggering 39.8% annually for the next 10 years, launching off from a promising figure of a market share worth USD 2.2 billion in 2021. 

Machine learning is used in many industries because it helps businesses gain a competitive advantage by improving efficiency and reacting better to customer needs. In fact, as companies begin to recognize its potential, availability will increase over time.

The market for machine learning as a service (MLaaS) is projected to remain stable over the forecast period. However, the adoption rate of this technology among consumers is expected to increase. Companies are implementing this technology because it has a wide range of benefits, such as increased efficiency, reduced costs, and improved customer engagement.

Why Machine Learning as-a-Service (MLaaS) Has Become So Proliferative?

Machine learning as a service (MLaaS) is all the rage these days, with companies such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform offering MLaaS service offerings. However, MLaaS is not new: it has been around for a while and continues to evolve at an impressive pace. The use of machine learning models in various industries has experienced explosive growth over the past few years. This rapid growth can be attributed to advancements in technology including increased data availability and computing power, coupled with powerful machine learning algorithms that are being made available via open source software packages or cloud solutions.

For example, of all the cloud providers AWS has continually added new capabilities to Amazon SageMaker since its launch. The added features included Amazon SageMaker Ground Truth which helps developers build highly accurate annotated training datasets. Amazon SageMaker is a cloud-based machine learning service that enables users to create highly accurate annotated training datasets by reading the text of web content.

 In the past, machine learning has been mainly implemented as a full-fledged developed solution. However, advancements have allowed the industry to start using the software as a service (Saas) solution.

Exploring the Global Landscape of Machine Learning As-A-Service (MLaaS) 

Machine learning as-a-service is a new trend that is quickly gaining momentum in the industry. MLaaS provides an environment through which professional developers, data scientists, and analysts can use machine learning applications on demand, with minimal effort and time investment.

Machine learning is a broad and rapidly growing field that can be used for many applications. It can be used for decision-making, real-time processing of data, manipulation of data, and machine learning. Machine learning as-a-service (MLaaS) refers to the use of ML in order to create automated services that are available over the internet.  The proliferation of MLaaS and its scalability is due to intrinsic factors such as the availability of data and available computational resources, along with the fact that the Internet has become an essential platform for running MLaaS services.

With MLaaS becoming more popular across different verticals, we explore the scope of Machine Learning As-A-Service (MLaaS), what has driven demand for MLaaS globally in the recent past and what are the major loopholes for its implementation.

  1. The Emerging Cloud Players Have Led to Accentuation of MLaaS Market

Machine learning as a service (MLaaS) is all the rage these days, with companies such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform offering MLaaS service offerings. However, MLaaS is not new: it has been around for a while and continues to evolve at an impressive pace. The use of machine learning models in various industries has experienced explosive growth over the past few years. This rapid growth can be attributed to advancements in technology including increased data availability and computing power, coupled with powerful machine learning algorithms that are being made available via open source software packages or cloud solutions.

For example, of all the cloud providers AWS has continually added new capabilities to Amazon SageMaker since its launch. The added features included Amazon SageMaker Ground Truth which helps developers build highly accurate annotated training datasets.   Amazon SageMaker is a cloud-based machine learning service that enables users to create highly accurate annotated training datasets by reading the text of web content.

In the past, machine learning has been mainly implemented as a full-fledged developed solution. However, advancements have allowed the industry to start using the software as a service (SaaS) solutions.

  1.  COVID-19 Has Scaled Up the Machine Learning as-a-Service (MLaaS) Dominion 

The fight against COVID-19 has seen an exponential increase in the use of machine learning as a service (MLaaS), which is transforming how this viral outbreak is being managed around the world.   The impact of COVID-19 has been felt all over the world. It has caused huge disruption in the economy and private companies are working toward creating new solutions to tackle the challenges posed by COVID-19. Machine learning has provided a lot of help in providing solutions for such challenges. 

Machine learning has powerfully helped with the detection and tracking of the COVID-19 disease.   With the introduction of the Cordova-19 search, anyone could access the entire world of research documents on their phone. The database is powered by ML and can be accessed through natural language queries.

The Machine Learning as-a-Service (MLaaS) is a Cloud service that helps people in different industries perform real-time analysis and prediction of data. The MLaaS also provides them with new ways to interact with the same datasets by using advanced modeling techniques like deep learning, neural networks, and supervised learning.

  1. The proliferation of IoT & Automation Have Fueled the Demand for Machine Learning-as-a-Service

The Proliferation of IoT & Automation Has Fueled the Demand for MLaaS. Analyzing complex data can save IoT enterprises significant amounts of money. A modern enterprise relies on data to manage its business, but once that data is collected it must be analyzed to optimize processes within the organization. If IoT operations are not properly managed, the impact can be catastrophic – organizations have lost millions of dollars due to faulty business processes.  Machine learning can be used to improve operational efficiency by predicting the outcome of a process, improving production quality and customer satisfaction, automating workflows, and improving security.

Machine learning is more than a buzzword in the enterprise data world now. It has become the high-tech alternative to labor-intensive ETL and data modeling projects because ML can extract hidden patterns from vast volumes of data quickly. Plus, with machine learning, it’s easier than ever before to make decisions with less human intervention.

The Largest Application of Machine Learning As-A-Service Is Anticipated to Be With Marketing & Advertising Segment 

The application of machine learning is expected to be the largest segment in the market, especially in terms of marketing and advertising. Employing ML algorithms can also help marketers with customer segmentation and better targeting based on the historical data and preferences exhibited by the potential buyers across an array of marketing & advertising channels.

Marketing enterprises are provided with an opportunity to pre-plan the right message for appropriate consumers and provide little room for learned adaptation through their campaign as it matures.

Machine learning (ML) is proving to be among the most successful tools in the marketing and advertising industry. It provides marketing companies with an opportunity to make quick, critical decisions based on big data. Employing Machine-learning as-a-service (MLaaS) helps marketing enterprises respond faster to the changes in the quality of traffic brought about by advertisement campaigns.

Exploring the Solution – Privacy & Data Security Concerns Remain Major Constraints to the Implementation of the MLaaS Model 

The usage of machine learning as a service (MLaaS) introduces challenges for data owners and platform owners. Data owners are concerned about the privacy and safety of their data on MLaaS platforms. In contrast, MLaaS platform owners worry that their models could be stolen by adversaries who pose as clients.

The usage of MLaaS enables ML model owners to leverage an ML platform owned by data owners. However, these providers of AI models need to provide nondisclosure agreements or comply with other protocols in order to ensure the privacy and security of their models.

Both the parties implementing and providing MLaaS need to develop a stringent to solve the problem of model theft and data privacy. The core idea is to have both MLaaS platform owners and model owners work together to establish trust mechanisms in the MLaaS environment. In this way, both parties can benefit from trading their data. We then present three related architectures: a security model that enables MLaaS users to exchange sensitive information without revealing it; a privacy-enabling model that allows clients to maintain their privacy when providing their model data; and an auditing solution that collects information from key actors on how users interact with each other in the MLaaS environment.

Final Words

The Machine Learning-as-a-service (MLaaS) market is widely expected to grow exponentially over the next decade. This is due to the ability to get access to a large number of models with high accuracy, which can be deployed in a hassle-free manner. The users can get access to the services at a low cost compared to hiring the personnel for data collection, training the model, and then deploying it.

 Machine Learning-as-a-Service (MLaaS) can be used by global marketers to attribute data, train their models and deploy them in the cloud. In such a scenario, one can save a lot of money by hiring the personnel only once and then utilizing the service instead of hiring it over and over again at multiple stages.

 MLaaS has gained popularity because of its high scalability, efficiency, and accuracy. These qualities combined with a competitive pricing model give an edge to global marketers who can utilize the services to their advantage.  Real-time access to information can be obtained at a low cost. The power of machine learning algorithms can be leveraged widely. Hence, businesses benefit by improving productivity and efficiency at a lower cost.

Although the market is still nascent as far as adoption is concerned as these services improve they will be adopted more frequently in near future.

 The main purpose of buying machine learning-as-a-service is to utilize the services in a hassle-free manner. The users can get access to the services at a low cost compared to hiring the personnel for data collection, training the model, and then deploying it.

In conclusion, we can conclude that machine learning as a service is a vital function for marketers in this ever-changing world. The machine learning-as-a-service market is mildly concentrated in nature with few numbers of global players operating in the market such as Microsoft Corporation, SAS Institute Inc., Fair Isaac Corporation (FICO), Google LLC, IBM Corporation, Hewlett Packard Enterprise Company, Yottamine Analytics LLC and BigML Inc. Every company follows its own business strategy to maximize its market share, and achieve its core business objectives by leveraging MLaaS.

Valasys Media is an esteemed B2B Media publishing firm empowering marketers with real-time intent data to optimize their marketing and advertising endeavors and providing them with a whole suite of data-enabled services to maximize their business bottom lines.

Author Bio

Priya has about 7 years of experience in Market Research. Currently, she is working for Valasys Media, as an Assistant Manager – Content Strategist, which is amongst the top B2B Media Publishers across the globe. She has been preparing several personalized reports for our clients & has done a lot of research on market segmentation, cluster analysis of audiences & inbound methodologies. She has worked with government institutes as well as corporate houses in several projects. She possesses various interests and believes in a data-driven approach to problem solving. She holds a post-graduation in science also writes extensively on all things about life besides marketing, science, data science and statistics. She is a firm believer in higher realities and that there’s always more to life than we understand. She is a psychic healer and a tarot practitioner, who believes in a spiritual way of living and practices Yoga and meditation. When not writing you can find her enjoying music or cooking.