Machine Learning As A Service (Mlaas) Market - Growth, Trends, Covid-19 Impact, and Forecasts (2023 - 2028)

Machine Learning As A Service (Mlaas) Market - Growth, Trends, Covid-19 Impact, and Forecasts (2023 - 2028)

The machine learning-as-a-service (MLaaS) market is expected to register a CAGR of 39.25% during the forecast period.

• Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering critical insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, model complexity, and computational complexity, it requires skilled professionals to develop these solutions.
• With advancements in data science and artificial intelligence, the performance of machine learning accelerated at a rapid pace. Companies are identifying the potential of this technology, and therefore, the adoption rate of the same is expected to increase over the forecast period. Companies are offering machine learning solutions on a subscription-based model, making it easier for consumers to take advantage of this technology. In addition, it provides flexibility on a pay-as-you-use basis.
• Amazon released SageMaker Studio, the first machine learning IDE, in 2021. This application provides a web-based interface through which clients can run all ML model training tests in a single environment. SageMaker Studio provides access to all development methods and tools, including notebooks, debugging tools, data modeling, and its automatic creation.
• The significant players in the market are organizing competitions to train Ai and security communities to handle critical scenarios in real-time AI systems. For instance, In July 2021, Microsoft established MLSEC.IO, an educational Machine Learning Security Evasion Competition (MLSEC) for the AI and the security community to practice attacking critical AI systems in a realistic context. The competition, hosted and sponsored by Microsoft, NVIDIA, CUJO AI, VM-Ray, and MRG Effitas, awards competitors who successfully escape AI-based malware and AI-based phishing detectors.
• The ML startups are receiving fundings millions of dollars of ML investment. For instance, In June 2022, Inflection AI secured one of the largest artificial machine learning funding rounds, totaling USD 225 million. It is referred to as a machine learning and AI startup. It has obtained USD 225 million in equity financing from venture capitalists. This ML investment will improve machine learning, allowing for intuitive human-computer interfaces in the near future.
• Machine learning-as-a-service leverages deep learning techniques for predictive analytics to enhance decision-making. However, using MLaaS introduces security challenges for ML model owners and data privacy challenges for data 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 may be stolen by adversaries who pose as clients.
• The COVID-19 pandemic caused many organizations to accelerate their migrations to public cloud solutions since cloud service elasticity can meet unexpected spikes in service demand. Migrations to the cloud helped companies reinvent the way they conduct their businesses in the time of COVID-19. The need for AI services has grown, and many cloud providers offer AIaaS and MLaaS.

• IoT operations ensure that the thousands or more devices run correctly and safely on an enterprise network and the data being collected is both timely and accurate. While sophisticated back-end analytics engines work on the heavy lifting of processing the data stream, ensuring data quality is often left to obsolete methodologies. To ensure the rein in sprawling IoT infrastructures, some IoT platform vendors are baking machine learning technology to boost their operations management capabilities.
• Machine learning may demystify the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. ML inference may supplement or replace manual processes with automated systems using statistically derived actions in critical processes. Solutions built on ML automate the IoT data modeling process, thus, removing the circuitous and labor-intensive activities of model selection, coding, and validation.
• Small businesses adopting IoT may significantly save on the time-consuming machine learning process. MLaaS vendors may conduct more queries more quickly, providing more types of analysis to get more actionable information from vast caches of data generated by multiple devices in the IoT network.
• As per Zebra's Manufacturing Vision Study, smart asset monitoring systems based on IoT and RFID are predicted to outperform traditional, spreadsheet-based approaches by 2022. According to research conducted by Microsoft Corporation, 85% of businesses have at least one IIoT use case project. This figure could rise, as 94% of respondents said they would pursue IIoT initiatives in 2021. These instcances may create opportunities for the MLaaS vendors in the near future.
• The increasing use of cloud-based technology in many organizations benefits data transfer due to the ease with which these connections may be formed. This allows every employee in an organization to access data, increasing a company's cost efficiency. In May 2021, Google Cloud unveiled Vertex AI, a new managed machine learning platform that allows users to maintain and deploy AI models based on client needs.

• North America is expected to hold a significant share in the market owing to the robust innovation ecosystem, fueled by strategic federal investments into advanced technology, complemented by the presence of visionary scientists and entrepreneurs coming together from globally renowned research institutions, which has propelled the development of MLaaS.
• Because of remarkable growth in countries such as Canada and United States, the North American region accounts for most of Mlaas business. These countries are home to a wide diversity of small and large start-ups. As a result, the market for machine learning as a service is expanding in North America. Regarding technological breakthroughs and use, North America is the fastest-growing region worldwide in the machine learning as a service market. It has the infrastructure and funds to invest in machine learning as a service. Furthermore, increased defense spending and technical improvements in the telecommunications industry will likely boost market growth throughout the forecast period.
• The region also witnessed a significant proliferation of 5G, IoT, and connected devices. As a result, communications service providers (CSPs) need to manage an ever-growing complexity efficiently through virtualization, network slicing, new use cases, and service requirements. This is expected to drive MLaaS solutions as traditional network and service management approaches are no longer sustainable.
• Moreover, major technology firms in the region, such as Microsoft, Google, Amazon, and IBM, have stepped up as major players in the ML-as-a-service race. Because each of the companies has sizeable public cloud infrastructure and ML platforms, this allows the companies to make machine learning-as-a-service a reality for those looking to use AI for everything ranging from customer service to robotic process automation, marketing, analytics, predictive maintenance, etc., to assist in training the AI date models being deployed.
• The key players in this region are updating their platform with new processes to offer seamless experiences to their clients, increasing the MlaaS market's demand. For instance, In December 2021, BigMl added Image Processing to the BigML platform, a feature that enhances their offering to solve image data-driven business problems with remarkable ease of use. It labels the image data, train and evaluate models, make predictions, and automate end-to-end machine learning workflows.
• Moreover, In November 2021, SAS added support for open-source users to its flagship SAS Viya platform. SAS Viya is for open-source integration and utility. The software user established an API-first strategy that fueled a data preparation process with machine learning.
• The region's ML marketplace is changing due to the cloud, and serverless computing makes it possible for developers to get ML applications up and running quickly. Additionally, the prime driver of the ML-as-a-service business is information services. The most significant change serverless computing has brought eliminating the need to scale physical database hardware.

The high market consolidation has increased the competition among prominent players such as Microsoft, IBM, Google, and Amazon. To capture a significant share in the market, other players are actively expanding their product portfolios and geographical presence.

• February 2022 - Telecom giant AT&T and AI company H2O have collaborated and launched an artificial intelligence feature store for enterprises. This delivers a repository for collaborating, sharing, reusing, and discovering machine learning features to speed AI project deployments and improve ROI.
• December 2021 - AWS announced six new Amazon SageMaker capabilities. This will make machine learning even more accessible and cost-effective. This brings together powerful new capabilities, including a no-code environment for creating accurate machine learning predictions and more accurate data labeling using highly skilled annotators.

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