LONDON, Oct. 26, 2021 /PRNewswire/ -- Brandessence Market Research has published a new report title, "The machine learning as a service market reached USD 2.27 billion in 2020. The market is likely to grow at 39.2%, reaching USD 22.10 billion in 2027". The covid-19 pandemic has pushed many firms out of their comfort zones, and strongly towards digitalization, increased data analysis, and subsequent data automation. The machine learning promises a major prospect as migration to cloud also emerges as a key trends, thanks to increased interest in the arena by key tech players, lowering costs of data storage, and tremendous growth of data insights.
Machine Learning as a Service Market: Expert Analysis
"The fast learning pace of machine learning model has accelerated a shift towards subscription-based model, which has enabled a cost-effective pricing model for end-consumers. The model is increasingly offers core services like natural language processing, general machine learning, and computer vision algorithms. The increasing adoption of IoTs, automation, and cloud-based services will continue to drive adoption towards cost-effective data servicing centers. The growth of big data generators, super computers, and highly effective system for gaining new insights are key to growth for the machine learning as a service market", report leading analysts at Brandessence market research.
Machine Learning as a Service Market: Segment Analysis
The machine learning as a service market report is divided into Network Analytics and Automated Traffic Management as applications. Among these, the growth of augmented reality, risk analytics, predictive maintenance, fraud detection, and others continue to drive strong growth for the network analytics segment. Machine learning is essential for growth of automated traffic management, and network analytics. Furthermore, with the advent of 5G technology, the growth of machine learning remains promising as large datasets continue to drive growth of cloud, gaming sphere, analytics, and more.
The automated traffic management for automated electric vehicle also remains a promising prospect. The promising horizon of smart cities, the growth of smart infrastructure, and growing demand for interconnected automated vehicles remain promising opportunities for growth for players in the machine learning as a service market.
The machine learning as a service market report categorizes organizations into small, medium, and large as end-users for machine learning as a service market. The growth of the subscription-based models remains a promising prospect for small organizations, as increasing deployment in cloud, and growing importance of digitalization and analytics continues to drive growth of the machine learning as a service market. The machine learning as a service also continues to remain appealing to medium-sized, and large organizations alike. The machine learning as a service model is likely to show promising demand for all end-users, with large organizations being likely to hold largest share of total revenues, thanks to their sizable demand.
Machine Learning as a Service Market: Competitive Analysis
The machine learning as a service market is a fragmented landscape, with increased small players leading innovation, thanks to technologies like TinyML. The landscape continues to grow due to innovation, as open-ended operating systems continue to drive down costs, and create new opportunities for players in the machine learning as a service market. Some key players in the machine learning as a service market are IBM Corporation, FICO, Microsoft, Amazon Web Services, Google Inc., Hewlett Packard Enterprises, AT&T, BigML Inc., Ersatz Labs., SAS Institute Inc., FICO, Yottamine Analytics, Amazon Web Services, Microsoft Corporation, Prediction Labs Ltd., and IBM Corporation
In December 2020, HP started offering HPE Greenlake, a cloud platform with pay-per-use model to cater to most demanding and data-intensive workloads. The platform runs on the power of AI, and ML to create new products, and experiences through a hybrid cloud model.
Eurobank, a major Greek bank has expanded its use of FICO compliance solutions, after a new regulation from the European Union. The bank operates in six countries, and its new machine learning solutions offers it new capabilities like doing KYC and AML checks in real-time, and processes like digital on-boarding.
The increased advancement in machine learning has moved the traditional model from AI-based analytics towards advanced image processing. This advancement has made machine learning a key tool for healthcare imaging diagnostics. Furthermore, the machine learning model remains a frontrunner in in new technologies like OpenAI, which can generate new visual designs based on text input. The growing advancement in Open AI model remains a promising prospect for global manufacturing, distribution, packaging, and sales alike.
The advancement of machine learning in healthcare remains a promising prospect. Machine learning with the help of data analytics, and visual lab results promises a new era for understanding genetic sequencing. The AI algorithms are trained to look at data using multi-modal techniques like optical character recognition, and machine vision to improve medical diagnosis.
The growth of new innovations like Tiny MK, a model that runs on hardware constrained devices for powering refrigerators, cars, and utility meters also remains a new promise for players in the machine learning as a service market. The tiny algorithms promise to capture gestures and common sounds like baby crying, or gunshots for applications like tracking environmental conditions, asset location and orientation, and even vital medical signs. The increasing storage, battery, and functionalities of the Tiny MK remain a major promise for a wide variety of commercial applications.
The data learning continues to drive demand for data labeling, a subset of the core functionality. The increasing demand for human-led data labeling has driven low-cost data labeling services, mostly arising in Asia Pacific region. The semi-supervised, and novice data labeling services present a challenge in applications like voice assistants led commercial functions. The growth of these applications has also driven demand for automated data labeling. The improvement in automated data labeling continues to make way for new processes like PlatformOps, MLOps, and DataOps, which are now combinedly referred to as, the 'XOps'.
Machine learning and AI also promise to team up with employees to take burden off of mundane responsibilities. For example, sales employee often need to login information like customer name, their address, and other important details. Today, online assistants through customer management systems deliver this information to employees to begin a smooth conversation about consumer concerns. Furthermore, machine learning systems are also advancing troubleshooting, sales through text-based and voice-based assistants online. This not only saves various companies significant amounts of money, but also reduces downtime, prepares them for digital adoption, and more. Young, tech-savvy consumers also increasingly prefer to contact customer service through online portals, saving tons of money for companies in customer service, and increasingly report higher satisfaction.
Key Benefits of Global Machine Learning as a Service Industry Report
Global Machine Learning as a Service Industry report covers in depth historical and forecast analysis.
Global Machine Learning as a Service Industry research report provides detail information about Market Introduction, Market Summary, Global market Revenue (Revenue USD), Market Drivers, Market Restraints, Market opportunities, Competitive Analysis, Regional and Country Level.
Global Machine Learning as a Service Industry report helps to identify opportunities in market place.
Global Machine Learning as a Service Industry report covers extensive analysis of emerging trends and competitive landscape.
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