North America Machine Learning Market (2019-2027)

  • Report Code : 251160
  • Industry : Telecom and IT
  • Published On : Oct 2018
  • Pages : 90
  • Publisher : Netscribes
  • Format: CMI Insight PPT FormatCMI Insight PDF Format

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.

North America Machine Learning market

The value of the machine learning market in North America is expected to reach USD 8.07 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 43.4% 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.

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 the U.S. and Canada.
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.

There is a notable trend of using machine learning models in the media and entertainment industry. This is done to better the customer experiences, keep them engaged by providing them relevant content and make a drive towards greater viewer personalisation.
Insurance companies in North America are using machine learning algorithms to access the market trends for maximising business opportunities. Similarly, prospects of new developments in the product market are informed to the clients by applying machine learning algorithms to the available client data.
The tech-savvy consumers in North America are providing a myriad of opportunities to this machine learning market. The urge for exact prediction in all facets of life has pushed every organisation, irrespective of the industry they belong to, to use data to drive value and to provide more personalised user experience.

Key growth factors

The outbreak of mobile computing systems in the last decade, which paved the way for easy collection and transmission of data across platforms, has led to the emergence of big data on which machine learning is hugely dependent now, giving a boost to the machine learning market in North America.
Predictive analytics help in taking preventive measures for probable health emergencies by looking at the data for key health indicators. This drives machine learning in the hospitals of North America.

Threats and key players

There is a detachment between the actual potential and how machine learning is being used in the real world. A lot of research and development (R&D) is being done to push the boundary a bit further. Inability to realise the full value, the made investments may give a severe backlash to this machine learning market in North America.
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 North America.
2. Market drivers and challenges in the machine learning in North America.
3. Market trends in the machine learning in North America.
4. Historical, current and forecasted market size data for the machine learning in North America.
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 (US, Canada) market size data for the machine learning market.
10. Analysis of North America 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 [email protected]

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. North America market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
2.2. North America - 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: U.S. machine learning market- market overview
3.1. Market overview-market trends, market attractiveness analysis, geography wise market revenue (USD)
3.2. US - 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: Canada machine learning market - market overview
4.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
4.2. Canada - 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: Competitive landscape
5.1. Microsoft
5.1.a. Company snapshot
5.1.b. Product offerings
5.1.c. Growth strategies
5.1.d. Initiatives
5.1.e. Geographical presence
5.1.f. Key numbers
5.2. Google Inc.
5.2.a. Company snapshot
5.2.b. Product offerings
5.2.c. Growth strategies
5.2.d. Initiatives
5.2.e. Geographical presence
5.2.f. Key numbers

5.3. IBM Watson
5.3.a. Company snapshot
5.3.b. Product offerings
5.3.c. Growth strategies
5.3.d. Initiatives
5.3.e. Geographical presence
5.3.f. Key numbers

5.4. Amazon
5.4.a. Company snapshot
5.4.b. Product offerings
5.4.c. Growth strategies
5.4.d. Initiatives
5.4.e. Geographical presence
5.4.f. Key numbers

5.5. Baidu
5.5.a. Company snapshot
5.5.b. Product offerings
5.5.c. Growth strategies
5.5.d. Initiatives
5.5.e. Geographical presence
5.5.f. Key numbers

5.6. Intel
5.6.a. Company snapshot
5.6.b. Product offerings
5.6.c. Growth strategies
5.6.d. Initiatives
5.6.e. Geographical presence
5.6.f. Key numbers

5.7. Facebook
5.7.a. Company snapshot
5.7.b. Product offerings
5.7.c. Growth strategies
5.7.d. Initiatives
5.7.e. Geographical presence
5.7.f. Key numbers

5.8. Apple Inc.
5.8.a. Company snapshot
5.8.b. Product offerings
5.8.c. Growth strategies
5.8.d. Initiatives
5.8.e. Geographical presence
5.8.f. Key numbers

5.9. Uber
5.9.a. Company snapshot
5.9.b. Product offerings
5.9.c. Growth strategies
5.9.d. Initiatives
5.9.e. Geographical presence
5.9.f. Key numbers

5.10. Luminoso
5.10.a. Company snapshot
5.10.b. Product offerings
5.10.c. Growth strategies
5.10.d. Initiatives
5.10.e. Geographical presence
5.10.f. Key numbers

Chapter 6: Conclusion

Chapter 7: Appendix
7.1. List of tables
7.2. Research methodology
7.3. Assumptions
7.4. About Netscribes Inc.
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