Global Machine learning market (2018-2023)

Global Machine learning market (2018-2023)


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Executive Summary

Machine learning market

The value of the machine learning market is expected to reach USD 23.46 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 42.6% 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.

North America has the most significant share of the machine learning market, while Asia-Pacific is expected to witness the highest CAGR.

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 North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa.

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.

The trend of using machine learning techniques in the healthcare sector, financial sector and retail sector are widespread. The world is moving towards a connected business world to make data-powered decisions.

Key growth factors

A large amount of data that is generated by the industries provide an impetus to this market. Also, an increased usage of deep learning techniques in the various industries is also one of the reasons giving thrust to the market. A lot of research and development (R&D) is done to improve the efficiency of the output provided by the machine learning market.

Technological advancement, proliferation of data and the dire need to derive maximum information from the available data have been identified as the key reasons for the growth in this market.

Threats and key players

Although an enormous amount of money has been spent, there remains an uncertainty environing how the deep training net works. Also, professionals are not equipped with adequate machine learning skills in the market. Wrong program formulations would lead to biased results, leading to difficulty in analysis.

The inefficiency of the cloud infrastructures in the developing countries, which are needed to store and seamlessly access data, act as a hurdle to the growth in this market.

The key players are Google Inc., Microsoft, IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.

What is covered in the report?

1. Overview of the machine learning market.

2. Market drivers and challenges in the machine learning market.

3. Market trends in the machine learning market.

4. Historical, current and forecasted market size data for the machine learning market.

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 (North America, Europe, Asia-Pacific, Latin America, the Middle East & Africa) market size data for machine learning market.

10. Analysis of the global machine learning market 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.



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Chapter 1: Executive summary

1.1. Market scope and segmentation

1.2. Key questions answered in this study

1.3. Executive summary

Chapter 2: Machine learning market-market overview

2.1. The global market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

2.2. Global-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: North America machine learning market- market overview

3.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

3.2. North America-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: Europe machine learning market-market overview

4.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

4.2. Europe-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: Asia-Pacific machine learning market-market overview

5.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

5.2. Asia-Pacific- 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: Latin America machine learning market-market overview

6.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

6.2. Latin America- 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: The Middle East & Africa machine learning market-market overview

7.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

7.2. Middle East and Africa- 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: Competitive landscape

8.1. Microsoft

8.1.a. Company snapshot

8.1.b. Product offerings

8.1.c. Growth strategies

8.1.d. Initiatives

8.1.e. Geographical presence

8.1.f. Key numbers

8.2. Google Inc.

8.2.a. Company snapshot

8.2.b. Product offerings

8.2.c. Growth strategies

8.2.d. Initiatives

8.2.e. Geographical presence

8.2.f. Key numbers

8.3. IBM Watson

8.3.a. Company snapshot

8.3.b. Product offerings

8.3.c. Growth strategies

8.3.d. Initiatives

8.3.e. Geographical presence

8.3.f. Key numbers

8.4. Amazon

8.4.a. Company snapshot

8.4.b. Product offerings

8.4.c. Growth strategies

8.4.d. Initiatives

8.4.e. Geographical presence

8.4.f. Key numbers

8.5. Baidu

8.5.a. Company snapshot

8.5.b. Product offerings

8.5.c. Growth strategies

8.5.d. Initiatives

8.5.e. Geographical presence

8.5.f. Key numbers

8.6. Intel

8.6.a. Company snapshot

8.6.b. Product offerings

8.6.c. Growth strategies

8.6.d. Initiatives

8.6.e. Geographical presence

8.6.f. Key numbers

8.7. Facebook

8.7.a. Company snapshot

8.7.b. Product offerings

8.7.c. Growth strategies

8.7.d. Initiatives

8.7.e. Geographical presence

8.7.f. Key numbers

8.8. Apple Inc.

8.8.a. Company snapshot

8.8.b. Product offerings

8.8.c. Growth strategies

8.8.d. Initiatives

8.8.e. Geographical presence

8.8.f. Key numbers

8.9. Uber

8.9.a. Company snapshot

8.9.b. Product offerings

8.9.c. Growth strategies

8.9.d. Initiatives

8.9.e. Geographical presence

8.9.f. Key numbers

8.10. Luminoso

8.10.a. Company snapshot

8.10.b. Product offerings

8.10.c. Growth strategies

8.10.d. Initiatives

8.10.e. Geographical presence

8.10.f. Key numbers

Chapter 9: Conclusion

Chapter 10: Appendix

10.1. List of tables

10.2. Research methodology

10.3. Assumptions

10.4. About Netscribes Inc.

To know more information on Purchase by Section, please send a mail to sales [@] kenresearch.com
 

1. Microsoft

2. Google Inc.

3. IBM Watson

4. Amazon

5. Baidu

6. Intel

7. Facebook

8. Apple Inc.

9. Uber

10. Luminoso

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