Machine-Learning-as-a-Service Market Research Report by Component (Services and Software), Application, End User, Region (Americas, Asia-Pacific, and Europe, Middle East & Africa) - Global Forecast to 2027 - Cumulative Impact of COVID-19
The Global Machine-Learning-as-a-Service Market size was estimated at USD 12.67 billion in 2021 and expected to reach USD 16.49 billion in 2022, and is projected to grow at a CAGR 30.29% to reach USD 62.04 billion by 2027.
The report provides market sizing and forecast across five major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2019 and 2020 are considered historical years, 2021 as the base year, 2022 as the estimated year, and years from 2023 to 2027 are considered the forecast period.
Market Segmentation & Coverage:
This research report categorizes the Machine-Learning-as-a-Service to forecast the revenues and analyze the trends in each of the following sub-markets:
Based on Component, the market was studied across Services and Software.
Based on Application, the market was studied across Augmented & Virtual Reality, Fraud Detection & Risk Management, Marketing & Advertising, Predictive Analytics, and Security & Surveillance.
Based on End User, the market was studied across BFSI, Healthcare & Life Sciences, Manufacturing, Retail, and Telecom.
Based on Region, the market was studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. The Europe, Middle East & Africa is further studied across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom.
Cumulative Impact of COVID-19:
COVID-19 is an incomparable global public health emergency that has affected almost every industry, and the long-term effects are projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlying COVID-19 issues and potential paths forward. The report delivers insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecasts, considering the COVID-19 impact on the market.
Cumulative Impact of 2022 Russia Ukraine Conflict:
We continuously monitor and update reports on political and economic uncertainty due to the Russian invasion of Ukraine. Negative impacts are significantly foreseen globally, especially across Eastern Europe, European Union, Eastern & Central Asia, and the United States. This contention has severely affected lives and livelihoods and represents far-reaching disruptions in trade dynamics. The potential effects of ongoing war and uncertainty in Eastern Europe are expected to have an adverse impact on the world economy, with especially long-term harsh effects on Russia.
This report uncovers the impact of demand & supply, pricing variants, strategic uptake of vendors, and recommendations for Machine-Learning-as-a-Service market considering the current update on the conflict and its global response.
Competitive Strategic Window:
The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.
FPNV Positioning Matrix:
The FPNV Positioning Matrix evaluates and categorizes the vendors in the Machine-Learning-as-a-Service Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.
Market Share Analysis:
The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.
The Competitive Scenario provides an outlook analysis of the various business growth strategies adopted by the vendors. The news covered in this section deliver valuable thoughts at the different stage while keeping up-to-date with the business and engage stakeholders in the economic debate. The competitive scenario represents press releases or news of the companies categorized into Merger & Acquisition, Agreement, Collaboration, & Partnership, New Product Launch & Enhancement, Investment & Funding, and Award, Recognition, & Expansion. All the news collected help vendor to understand the gaps in the marketplace and competitor’s strength and weakness thereby, providing insights to enhance product and service.
Company Usability Profiles:
The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Machine-Learning-as-a-Service Market, including Amazon Web Services, Inc., BigML, Inc., Crunchbase Inc., Fair Isaac Corporation., Google LLC, H2O.ai., IBM, Microsoft Corporation, PREDICTRON LABS, and Yottamine Analytics, LLC.
The report provides insights on the following pointers:
1. Market Penetration: Provides comprehensive information on the market offered by the key players
2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets
3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments
4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players
5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments
The report answers questions such as:
1. What is the market size and forecast of the Global Machine-Learning-as-a-Service Market?
2. What are the inhibiting factors and impact of COVID-19 shaping the Global Machine-Learning-as-a-Service Market during the forecast period?
3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Machine-Learning-as-a-Service Market?
4. What is the competitive strategic window for opportunities in the Global Machine-Learning-as-a-Service Market?
5. What are the technology trends and regulatory frameworks in the Global Machine-Learning-as-a-Service Market?
6. What is the market share of the leading vendors in the Global Machine-Learning-as-a-Service Market?
7. What modes and strategic moves are considered suitable for entering the Global Machine-Learning-as-a-Service Market?
Please Note: PDF & Excel + Online Access - 1 Year
Amazon Web Services, Inc.
Fair Isaac Corporation.
Yottamine Analytics, LLC
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