Global Machine Learning Market: 2018 Business Opportunity and Strategies, Forecast by 2023

The global machine learning market is projected to expand at a CAGR of 39.6% and is expected to be worth USD 13.5 Bn by 2023. With the generation of massive volumes of data across industries, data analytics is crucial to the success of businesses, leading to the rise of machine learning. Apart from this, the rapid evolution and increased use of Artificial intelligence (AI), development of new robust data models and associated algorithms, and a growing number of start-ups have given an impetus to the expansion of the global machine learning market.

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Segmentation based on deployment:
Machine learning models can be deployed through cloud or on-premises. Cloud deployment is expected to expand at the highest CAGR (42.6%) during the 2018-2023 period, leading to a global revenue generation of USD 9.2 Bn by 2023. Cloud deployment is highly favorable in the developed regions (North America and Europe), owing to extensive technological advancements and high rates of adoption. Machine learning through cloud deployment is less expensive in terms of operation, has convenient usage, and greater data storage capacities. On-premises deployment is mainly used in the developing regions of APAC and RoW (Latin America, and the Middle East and Africa). This is mainly due to the predominance of legacy systems and low technology adoption rates. However, with the shift from legacy systems to cloud technology, the market shares of on-premises deployment is expected to reach only 31.6% by 2023.

Segmentation based on end user industry:
Based on end user industry, application of machine learning pertains to the areas of BFSI, retail, digital advertising, healthcare, and ‘others’. The BFSI segment had a market share of 22.7% in 2018, and is expected to expand at a CAGR of 23.6% during the 2018-2023 period. This growth can be attributed to the increased use of AI, and generation of huge volumes of transactional data. ML provides advanced analytical capabilities, which is used to make accurate predictions about income leakages, cost reduction, revenue gains, data reporting to stakeholders, fraud detection, and other tasks. The ‘others’ segment comprises law, automobile, manufacturing, security, etc., and is expected to grow rapidly owing to the increased use of ML in self-driving cars (automobile), fraud detection (security), and self-learning robots (manufacturing sector). The healthcare sector is expected to expand at the highest CAGR (41.2%) during the 2018-2023 period, due to the increased use of AI and ML in image processing, automated systems, and to analyze refill patterns of customers and pharmacies, etc.

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Regional insights:
The machine learning market in North America is expected to expand at a CAGR of 35.1% during the 2018-2023 period. Rapid technological development, innovation, and high rates of adoption will fuel the growth of the market. Asia-Pacific, on the other hand, will be the leading contributor to the global machine learning market, and is expected to contribute USD 3.55 Bn by 2023. Additionally, the Asia-Pacific region will witness the highest CAGR (46.1%) during the forecast period of 2018-2023. The key countries contributing to APAC’s huge revenue are China, Singapore, Japan, and India. The revenue contribution by the ‘Rest of the World’ is expected to be USD 2.9 Bn by 2023. The European ML market is expected to expand at a CAGR of 30.3% during the 2018-2023 period, contributing USD 2.3 Bn by 2023

Companies covered:
Google
Microsoft Corporation
IBM Corporation
Apple
Amazon
Facebook
Salesforce
Baidu
Intel Corporation
SAP SE

Chapter 1. Executive summary

1.1. Market scope and segmentation

1.2. Key questions answered

1.3. Executive summary

Chapter 2. Global machine learning market – overview

2.1. Market definition

2.2. Global market overview – historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), geography-wise market revenue (USD Bn), and market attractiveness analysis

2.3. Global market drivers

2.4. Global market trends

2.5. Global market challenges

Chapter 3. Global machine learning market – based on deployment

3.1. Cloud deployment – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

3.2. On-premises deployment – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

Chapter 4. Global machine learning market – based on end user industry

4.1. BFSI – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

4.2. Retail – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

4.3. Digital advertising – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

4.4. Healthcare – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

4.5. Others (law, automotive, manufacturing, security, etc.) – market overview, global market revenue (USD Bn) – forecasted (2018-2023) market size (USD Bn), and key market observations

Chapter 5. North America machine learning market

5.1. Regional market overview – historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

5.2. Market segmentation based on deployment (cloud deployment, and on-premises deployment)- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

5.3. Market segmentation based on end user industry (BFSI, retail, digital advertising, healthcare and others [law, automotive, manufacturing, security, etc.])- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

Chapter 6. Europe machine learning market

6.1. Regional market overview – historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

6.2. Market segmentation based on deployment (cloud deployment, and on-premises deployment)- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

6.3. Market segmentation based on end user industry (BFSI, retail, digital advertising, healthcare and others [law, automotive, manufacturing, security, etc.])- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

Chapter 7. Asia-Pacific machine learning market

7.1. Regional market overview – historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

7.2. Market segmentation based on deployment (cloud deployment, and on-premises deployment)- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

7.3. Market segmentation based on end user industry (BFSI, retail, digital advertising, healthcare and others [law, automotive, manufacturing, security, etc.])- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

Chapter 8. RoW (Latin America, and the Middle East and Africa) machine learning market

8.1. Regional market overview – historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

8.2. Market segmentation based on deployment (cloud deployment, and on-premises deployment)- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

8.3. Market segmentation based on end user industry (BFSI, retail, digital advertising, healthcare and others [law, automotive, manufacturing, security, etc.])- historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), and key market observations

Chapter 9. Competitive landscape

9.1. Google

9.1. a. Company snapshot

9.1. b. Products

9.1. c. Strategic initiatives

9.1. d. Countries present

9.1. e. Competitors

9.1. f. Key numbers

Note: Similar information areas will be covered for the remaining competitors

9.2. Microsoft Corporation

9.3. IBM Corporation

9.4. Apple

9.5. Amazon

9.6. Facebook

9.7. Salesforce

9.8. Baidu

9.9. Intel Corporation

9.10. SAP SE

Chapter 10. Start-up firms

10.1. Anodot

10.2. Cinnamon

10.3. Citrine Informatics

10.4. Dataiku

10.5. CrowdAI

10.6. Drawbridge

Chapter 11. Conclusion

11.1. Future outlook

Chapter 12. Appendix

List of tables
Research methodology
Assumptions
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