Description
Europe Machine Learning market
The value of the machine learning market in Europe is expected to reach USD 3.96 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 33.5% 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.
Europe stands in the second position after North America in 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 European Union five (EU5), rest of Europe.
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 supporting, educating, enforcing and steering the economy towards a machine learning-friendly environment is seen to be followed throughout Europe.
European countries are successfully bridging the gap between additional renewable energy and excess power into the grid by making ultra-accurate forecasts of the demand and supply in real time by making use of the machine learning technologies, thereby saving energy and cost.
Key growth factors
The world-class research facilities, the emerging start-up culture, the innovation and commercialisation of machine intelligence technologies is giving thrust to the machine intelligence market in Europe.
Amongst all regions, Europe has the largest share of intraregional data flow. This, together with the machine learning technologies, is boosting the market in Europe.
The excessive usage of the machine learning technology across economy in all facets of businesses is proving to be a big thrust to the machine learning market. Profound usage has been found in sectors such as agriculture, healthcare and media for optimisation of prices and carrying out predictive maintenance in manufacturing.
Threats and key players
Investors in Europe are more concerned about the ROI from investing in the machine learning market. The adoption of machine learning by the start-ups is a farce in Europe since research suggests that only 5% of the start-ups investing in machine learning end up with a revenue of more than $50 Mn in revenue. Also, opportunities for external investments are bleak.
The machine learning market is in a stage of infancy; there is a lacuna between the skills required and that which is inherent in the workers. It requires a considerable amount of time to pick up the skills. Also, the Europeans are concerned about the penetration of machine learning into their lives, and how it is going to impact employment in the country. Concerns environing these factors are hindering the further developments in the machine learning market.
Given that machine intelligence depends on the easy availability of data, the practice of data minimisation and data privacy standards act as a barrier to the further development of the machine learning market in Europe.
The key players are Microsoft, Google Inc., IBM Watson, Amazon, Intel, Facebook and Apple.
What is covered in the report?
1. Overview of the machine learning in Europe.
2. Market drivers and challenges in the machine learning in Europe.
3. Market trends in the machine learning in Europe.
4. Historical, current and forecasted market size data for the machine learning market in Europe.
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 European Union five (EU5), rest of Europe) market size data for machine learning market.
10. Analysis of machine learning market in Europe 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.
Table of Contents
Chapter 1: Executive summary
1.1. Market scope and segmentation
1.2. Key questions answered in this study
1.3. Executive summary
Chapter 2: Europe machine learning market – market overview
2.1. Europe market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
2.2. Europe – 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: EU5 machine learning market- market overview
3.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
3.2. EU5 – market drivers and challenges
3.3. 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: Rest of Europe machine learning market – market overview
4.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
4.2. Rest of 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: 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. Intel
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. Facebook
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. Apple
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
Chapter 6: Conclusion
Chapter 7: Appendix
7.1. List of tables
7.2. Research methodology
7.3. Assumptions
7.4. About Netscribes Inc.
