Middle East and Africa Machine Learning Market (2019-2027)

  • Report Code : 251161
  • 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.

Middle East & Africa Machine Learning market

The value of the machine learning market in Middle East and Africa is expected to reach USD 0.50 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 29.1% 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.

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 UAE, Saudi Arabia, South Africa, Rest of 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 use of machine learning in healthcare has gained grounds in recent times. The hospitals in the Middle East are making use of the machine learning technologies for making a diagnosis of the diseases that may crop up in future, and for more precise analysis, prevention and treatment of individuals.

Key growth factors

The labour intensive sectors like retail and healthcare have taken up the use of machine learning to provide better consumer experiences. The urge for automation in these sectors is driving the machine learning market in the Middle East and Africa.
The high growth in the start-up culture with the government encouraging innovation has led them to make ample amount of investments in machine learning technologies, which in turn is driving the machine learning market.
Threats and key players

The adoption of machine learning in all the industries is going to be a slow process in Africa until and unless infrastructure and consumer spending power improves.
The unavailability of cohorts with adequate machine learning skills act as a barrier to the further development in the machine learning market.
The key players are Google Inc., Microsoft, IBM Watson, Amazon, and Intel.

What is covered in the report?

1. Overview of the machine learning market in the Middle East and Africa.
2. Market drivers and challenges in the machine learning in the Middle East and Africa.
3. Market trends in the machine learning in the Middle East and Africa.
4. Historical, current and forecasted market size data for the machine learning market in the Middle East and Africa.
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 (The UAE, Saudi Arabia, South Africa, Rest of the Middle East and Africa) market size data for machine learning market.
10. Analysis of machine learning market in the Middle East & Africa 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: The Middle East & Africa machine learning market - market overview
2.1. The Middle East & Africa market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
2.2. The Middle East & Africa - 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: The UAE machine learning market- market overview
3.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
3.2. UAE - 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: Saudi Arabia machine learning market - market overview
4.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
4.2. Saudi Arabia - 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: South Africa machine learning market - market overview
5.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
5.2. South Africa - 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: Rest of the Middle East & Africa machine learning market - market overview
6.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
6.2. Rest of the Middle East & Africa - 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: Competitive landscape
7.1. Microsoft
7.1.a. Company snapshot
7.1.b. Product offerings
7.1.c. Growth strategies
7.1.d. Initiatives
7.1.e. Geographical presence
7.1.f. Key numbers
7.2. Google Inc.
7.2.a. Company snapshot
7.2.b. Product offerings
7.2.c. Growth strategies
7.2.d. Initiatives
7.2.e. Geographical presence
7.2.f. Key numbers

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

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

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

Chapter 8: Conclusion

Chapter 9: Appendix
9.1. List of tables
9.2. Research methodology
9.3. Assumptions
9.4. About Netscribes Inc.
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