Impact Analysis of Covid-19
The complete version of the Report will include the impact of the COVID-19, and anticipated change on the future outlook of the industry, by taking into the account the political, economic, social, and technological parameters.
Asia-Pacific Machine Learning market
The value of the machine learning market in Asia-Pacific is expected to reach USD 10.00 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 51.3% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.
Asia-Pacific will experience the highest CAGR in the machine learning market.
The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into China, India, Japan, South Korea, Australia and New Zealand (ANZ), the rest of Asia-Pacific.
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.
Machine learning is no longer a novelty in Asia-Pacific countries. Business sectors having realised its potential are using machine learning technologies to draw maximum insights from the available data to increase the efficiency of operations.
Key growth factors
The enormous population base together with a diverse industry mix, which has the potential to generate a huge amount of data, is significantly driving the machine learning market in the Asia-Pacific countries.
The availability of a robust data set, the adoption of machine learning techniques in traditional industries and strengthening of the pipeline of cohorts with exceptional talent is driving the machine learning market in the Asia-Pacific region.
Threats and key players
Ethical issues and biased data leading to biased decisions are a matter of concern which restricts further development of the machine learning market.
The connectivity standards available in the Asia-pacific region still falls below the world's average. The digital divide is widening the gap among the sub-regions in the Asia-Pacific zone at an alarming rate. This again is causing a hindrance to the development in the machine learning market in this region.
The key players are Microsoft, Google Inc., IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.
What is covered in the report?
1. Overview of the machine learning market in Asia-Pacific region.
2. Market drivers and challenges in the machine learning market in Asia-Pacific region.
3. Market trends in the machine learning market in Asia-Pacific region.
4. Historical, current and forecasted market size data for the machine learning market in Asia-Pacific region.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).
9. Historical, current and forecasted regional (China, India, Japan, South Korea, Australia and New Zealand (ANZ), the rest of Asia-Pacific) market size data for machine learning market.
10. Analysis of the machine learning market in Asia-Pacific by value chain.
11. Analysis of the competitive landscape and profiles of major competitors operating in the market.
Why buy?
1. Understand the demand for machine learning to determine the viability of the market.
2. Determine the developed and emerging markets for machine learning.
3. Identify the challenge areas and address them.
4. Develop strategies based on the drivers, trends and highlights for each of the segments.
5. Evaluate the value chain to determine the workflow.
6. Recognize the key competitors of this market and respond accordingly.
7. Knowledge of the initiatives and growth strategies taken by the major companies and decide on the direction of further growth.
Customizations available
With the given market data, Netscribes offers customizations according to specific needs. Write to us at support@researchonglobalmarkets.com.
Chapter 1: Executive summary
1.1. Market scope and segmentation
1.2. Key questions answered in this study
1.3. Executive summary
Chapter 2: Asia-Pacific machine learning market - market overview
2.1. Asia-Pacific market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
2.2. Asia-Pacific - market drivers and challenges
2.3. Value chain analysis - machine learning market
2.4. Porter's five forces analysis
2.5. Market size- by components (software tools, cloud and web-based APIs and others)
2.5. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.5. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.5. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.6. Market size- by service (professional services and managed services)
2.6. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.6. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. Market size- by organization size (SMEs and large enterprises)
2.7. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. Market size- by application (BFSI, automotive, healthcare, government and others)
2.8. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 3: China machine learning market- market overview
3.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
3.2. China - market drivers and challenges
3.3. Market size- by components (software tools, cloud and web-based APIs and others)
3.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. Market size- by service (professional services and managed services)
3.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. Market size- by organization size (SMEs and large enterprises)
3.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. Market size- by application (BFSI, automotive, healthcare, government and others)
3.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 4: India machine learning market - market overview
4.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
4.2. India - market drivers and challenges
4.3. Market size- By components (software tools, cloud and web-based APIs and others)
4.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. Market size- by service (professional services and managed services)
4.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. Market size- by organization size (SMEs and large enterprises)
4.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. Market size- By application (BFSI, automotive, healthcare, government and others)
4.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 5: Japan machine learning market - market overview
5.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
5.2. Japan - market drivers and challenges
5.3. Market size- by components (software tools, cloud and web-based APIs and others)
5.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. Market size- by service (professional services and managed services)
5.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. Market size- by organization size ( SMEs and large enterprises)
5.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. Market size- by application (BFSI, automotive, healthcare, government and others)
5.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 6: South Korea machine learning market - market overview
6.1. Market overview- market trends, market attractiveness analysis, geography-wise
market revenue (USD)
6.2. South Korea- market drivers and challenges
6.3. Market size- by components (software tools, cloud and web-based APIs and others)
6.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. Market size- by service (professional services and managed services)
6.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. Market size- by organisation size (SMEs and large enterprises)
6.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. Market size- By application (BFSI, automotive, healthcare, government and others)
6.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 7: ANZ machine learning market - market overview
7.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
7.2. ANZ - market drivers and challenges
7.3. Market size- by components (software tools, cloud and web-based APIs and others)
7.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.3. c. Revenue of Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. Market size- by service (professional services and managed services)
7.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. Market size- by organization size (SMEs and large enterprises)
7.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. Market size- by application (BFSI, automotive, healthcare, government and others)
7.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 8: Rest of Asia Pacific machine learning market - market overview
8.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
8.2. Rest of Asia Pacific - market drivers and challenges
8.3. Market size- by components (software tools, cloud and web-based APIs and others)
8.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.3. c. Revenue of Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.4. Market size- by service (professional services and managed services)
8.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.5. Market size- by organisation size (SMEs and large enterprises)
8.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.6. Market size- by application (BFSI, automotive, healthcare, government and others)
8.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
8.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
Chapter 9: Competitive landscape
9.1. Microsoft
9.1.a. Company snapshot
9.1.b. Product offerings
9.1.c. Growth strategies
9.1.d. Initiatives
9.1.e. Geographical presence
9.1.f. Key numbers
9.2. Google Inc.
9.2.a. Company snapshot
9.2.b. Product offerings
9.2.c. Growth strategies
9.2.d. Initiatives
9.2.e. Geographical presence
9.2.f. Key numbers
9.3. IBM Watson
9.3.a. Company snapshot
9.3.b. Product offerings
9.3.c. Growth strategies
9.3.d. Initiatives
9.3.e. Geographical presence
9.3.f. Key numbers
9.4. Amazon
9.4.a. Company snapshot
9.4.b. Product offerings
9.4.c. Growth strategies
9.4.d. Initiatives
9.4.e. Geographical presence
9.4.f. Key numbers
9.5. Baidu
9.5.a. Company snapshot
9.5.b. Product offerings
9.5.c. Growth strategies
9.5.d. Initiatives
9.5.e. Geographical presence
9.5.f. Key numbers
9.6. Intel
9.6.a. Company snapshot
9.6.b. Product offerings
9.6.c. Growth strategies
9.6.d. Initiatives
9.6.e. Geographical presence
9.6.f. Key numbers
9.7. Facebook
9.7.a. Company snapshot
9.7.b. Product offerings
9.7.c. Growth strategies
9.7.d. Initiatives
9.7.e. Geographical presence
9.7.f. Key numbers
Chapter 10: Conclusion
Chapter 11: Appendix
11.1. List of tables
11.2. Research methodology
11.3. Assumptions
11.4. About Netscribes Inc.
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