Latin America Machine Learning Market (2018-2023)

Latin America Machine Learning Market (2018-2023)


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Executive Summary

Latin America Machine Learning market

The value of the machine learning market in Latin America is expected to reach USD 0.93 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 24.8% 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.

Latin America is fast developing in the field of machine learning.

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 Brazil, Mexico, Argentina, the rest of Latin America).

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.

A notable trend of using machine learning in the health service sector is observed. The doctors use machine learning technologies to measure the likelihood of patients suffering from zika, dengue fever or chikungunya in order to prevent future outbreaks.

Machine learning has found its way into all kinds of industries. The Brazilian stock exchange makes intensive use of machine learning technologies to order out the chaos and put the theory into practice.

Key growth factors

Latin America's drive towards digital economy has led to the companies transforming into 'intelligent enterprises' improve business processes and install 'intelligent machines' to take up routine work.

Threats and key players

75 to 81 percent of the people in Latin America possess low or medium skills. The less skilled and less educated will find it more difficult to pick up the skills necessary for the machine learning industry. This, therefore, acts as a hindrance to the further development in the machine learning market.

The lack of understanding as to why technology is essential is partially present. There's also a belief that technology is not mature enough to give fruitful benefits from its utilisation. Also, the availability of proper data sets to use in the process of machine learning technologies is absent.

The key players are Microsoft, Google Inc., IBM Watson, Amazon, and Intel.

What is covered in the report?

1. Overview of the machine learning in Latin America.

2. Market drivers and challenges in the machine learning in Latin America.

3. Market trends in the machine learning in Latin America.

4. Historical, current and forecasted market size data for the machine learning in Latin 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 (Brazil, Mexico, Argentina, The rest of Latin America) market size data for machine learning market.

10. Analysis of machine learning market in Latin America 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: Latin America machine learning market-market overview

2.1. Latin America market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)

2.2. Latin 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: Brazil machine learning market- market overview

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

3.2. Brazil-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: Mexico machine learning market-market overview

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

4.2. Mexico-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: Argentina machine learning market-market overview

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

5.2. Argentina-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: The rest of Latin America machine learning market-market overview

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

6.2. Rest of 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: 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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1. Microsoft

2. Google Inc.

3. IBM Watson

4. Amazon

5. Intel

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