Overview and Scope
Machine learning is a field of artificial intelligence that involves the development of algorithms allowing computers to learn from data, identify patterns, and make predictions or decisions without explicit programming. It focuses on creating models that improve their performance over time as they are exposed to more information.
Sizing and Forecast
The machine learning market size has grown exponentially in recent years. It will grow from $50.2 billion in 2023 to $68.88 billion in 2024 at a compound annual growth rate (CAGR) of 37.2%. The growth in the historic period can be attributed to advancements in algorithms, increasing data availability, computing power enhancement, industry adoption and integration, iot and edge computing integration.
The machine learning market size is expected to see exponential growth in the next few years. It will grow to $240.84 billion in 2028 at a compound annual growth rate (CAGR) of 36.7%. The growth in the forecast period can be attributed to explainability and ethical ai concerns, exponential data growth, expansion of edge computing, growing integration of ai and ml, global demand for predictive analytics. Major trends in the forecast period include continued algorithmic innovations, rise of big data technology, automated machine learning (automl), federated learning, integration of ml with other technologies, industry-specific ml solutions.
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Segmentation & Regional Insights
The machine learning market covered in this report is segmented –
1) By Component Outlook : Hardware, Software
2) By Deployment: Cloud, On-Premises
3) By Type: Large Enterprises, Small and Medium Enterprise
4) By End-user: Healthcare, Retail, BFSI, Manufacturing, IT & Telecom, Energy & Utilities, Agriculture, Automotive, Marketing & Advertising
North America was the largest region in the machine learning market in 2023. Europe is expected to be the fastest-growing region in the forecast period. The regions covered in the machine learning market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Major Driver Impacting Market Growth
Increasing adoption of cloud-based platforms is expected to propel the growth of machine learning market going forward. Cloud-based platforms refer to computing services and resources that are delivered over the internet, allowing users to access and utilize applications, storage, and processing power without the need for physical infrastructure or hardware. Cloud-based machine learning platforms facilitate collaboration, as multiple users can work on projects simultaneously and they allow seamless integration with other cloud services. For instance, in October 2023, according to Ofcom, a UK-based Government office, the market for cloud infrastructure in the UK was worth approximately £7.0 billion ($ 7.69) to £7.5 billion ($ 8.23) in 2022. Moreover, in 2023, 41.4% of the global tech and business leaders plan to increase their investment in cloud-based services and products. Therefore, increasing adoption of cloud-based platforms is driving the growth of machine learning market.
Key Industry Players
Major companies operating in the machine learning market report are Google Inc., Microsoft Corporation, Amazon Web Services Inc., International Business Machines Corporation, SAP SE, Hewlett Packard Enterprise Development LP, Baidu Inc., Intel Corporation, SAS Institute Inc., Fair Isaac Corporation, Alteryx, Dataiku, H2o.AI, KNIME.com AG, Alpine Data, Peltarion, RapidMiner Inc., BigML Inc., Luminoso Technologies Inc., Turing Analytics
The machine learning market report table of contents includes:
1. Executive Summary
2. Machine Learning Market Characteristics
3. Machine Learning Market Trends And Strategies
4. Machine Learning Market – Macro Economic Scenario
5. Global Machine Learning Market Size and Growth
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32. Global Machine Learning Market Competitive Benchmarking
33. Global Machine Learning Market Competitive Dashboard
34. Key Mergers And Acquisitions In The Machine Learning Market
35. Machine Learning Market Future Outlook and Potential Analysis
36. Appendix
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