New Tactics of Asia-Pacific Machine Learning Market Opportunities in Upcoming years
A Comprehensive research study
conducted by KD Market Insights on ‘‘Asia-Pacific
Machine Learning Market’’ report offers
extensive and highly detailed historical, current and future market trends in
the Asia-Pacific And regional /market. The Asia-Pacific
Machine Learning Market report includes market size, growth drivers,
barriers, opportunities, trends and other information which helps to find new
opportunities in this market for the growth of the business through new
technologies and developments.
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.
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.
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.
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.
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Full Report With TOC@ https://www.kdmarketinsights.com/product/asia-pacific-machine-learning-market
Table
of Contents:
Chapter 1:
Executive summary
1.1. Market scope and segmentation
1.2. Key questions answered in this study
1.3. 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
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
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
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
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
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