Deep Learning Market
Report ID: SQMIG45F2165
Deep Learning Market Size, Share, Growth Analysis, By Offering (Hardware, Software), By Application, By End-User Industry, By Region - Industry Forecast 2025-2032
Report ID:
SQMIG45F2165 |
Region:
Global |
Published Date: December, 2024
Pages:
193
|Tables:
0
|Figures:
0
Deep Learning Market Insights
Deep Learning Market size was valued at USD 64.13 Billion in 2023 and is poised to grow from USD 85.02 Billion in 2024 to USD 811.63 Billion by 2032, growing at a CAGR of 32.58% during the forecast period (2025-2032).
Major factors fueling the deep learning market growth are improving continuing power, along with declining hardware price and rising adoption of cloud-based innovation. The increasing need of organizations for processing power and the growing presence of Internet of Things (IoT) devices in almost every field are fueling the deep learning market expansion. Moreover, there are 2.5 quintillion bytes of data per day created, and that number continues to grow exponentially. The colossal data from diverse sectors provide lucrative avenues for deep learning solutions to deliver adaptive and scalable insights to organizations efficiently.
Cloud analytics combines technological, infrastructural, and analytical tools and procedures to help customers extract desired information from a large dataset. Moreover, deep learning analytics in the cloud helps enterprises to save on their infrastructural and storage expenses alongside the operational cost. Deep learning is a type of AI and ML technology that simulates human behaviour and creates data generated by human brain cells. It helps perform classification tasks and pattern recognition on the pictures, text, audio, and other data. It is also used to automate tasks that ordinarily require human intelligence such as image labelling or audio file transcription.
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Deep Learning Market Segmental Analysis
Global Deep Learning Market is segmented by Offering, Application, End-User Industry and region. Based on Offering, the market is segmented into Hardware (Processor, Memory, Network), Software (Solution, Platform/API), Services (Installation, Training, Support & Maintenance). Based on Application, the market is segmented into Image Recognition, Signal Recognition, Data Mining, Others (Recommender System, Drug Discovery). Based on End-User Industry, the market is segmented into Healthcare, Manufacturing, Automotive, Agriculture, Retail, Security, Human Resources, Marketing, Law, and Fintech. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & and Africa.
Analysis by Solution
The hardware segment is dominating with the largest deep learning market share. Some of the major hardware for deep learning are GPU, FPGA etc. With the higher memory bandwidth and throughputs, the GPUs form a general-purpose category of hardware for advancing training and classification process in Computer Neural Networks. Additionally, GPU also has a better computing power thus enabling the system to perform multiple parallel processes. FPGA is most appropriate technology for deep learning. Prior FPGA configurations were restricted to train alone but presently, FPGA configurations are broadly utilized for varied applications. FPGA is a flexible, fast, power-efficient, and promising data centre application for data processing. In addition, another use of FPGAs as they are widely used towards engineers and researchers due to their ability to quickly prototype multiple designs in far short times than a conventional IC.
During the forecast period, the software segment will expand at the highest rate. Over the past couple of years, there has been a large number of software tools available for developers. Hence, companies are making deep learning frameworks via higher-level programming, strong tools, and libraries that help build, teach, and confirm deep neural networks. In addition, it also extends the deep learning experience to all organizations with ONNX architecture, machine comprehension, and edge intelligence. Numerous startups and established players are working on next-gen hardware that will enable more effective deep learning processing. This has attracted the attention of investors and big corporate companies toward these startups, which often tend to accelerate the adoption of deep-learning technology.
Analysis by Application
Based on the application, image recognition is dominating with the deep learning market share. Extracting visual keywords using Deep Learning, stock photography and video websites can discover visual content for the user. Visual search allows users to search for similar images or products using a reference image. In addition, the technology can assist in a medical image analysis, facial recognition for security and surveillance, and image detection in social media analytics. Growth of the Social Media platforms with abundant visual content combined with the necessity to modernize the content will positively grow the deep learning market of image recognition application across the globe.
During the forecast period, data mining application is anticipated to grow at highest CAGR. Challenges in the data mining and extraction process, such as fastmoving streaming data, trustworthiness of data analytics, and imbalanced input information and extremely dispersed input resources might be relieved through the deep learning approach. The method uses a deep learning which assists in semantic indexing and tagging video, text and image and it does this discriminative task. Deep learning has this capacity that performs the featured engineering to execute complex tasks and has a better notion to represent the data.
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Deep Learning Market Regional Insights
Based on region, North America is dominating with the largest market share. This is because of the rising investments in artificial intelligence and neural networks. Image and pattern recognition has a very high adoption in the US and Canada and is poised to provide new growth opportunities during the forecast period. Additionally, the region is an early adopter of advanced technologies to which organizations must adopt deep learning capabilities quickly. Moreover, rise in government support is projected to support the deep learning market growth in the region. Federal subcommittees on AI and ML are being formed and this is gaining momentum.
The fastest growth during the forecast period is expected to be experienced by Asia Pacific. The Asia Pacific region will grow rapidly owing to various technological developments in the countries including India, China, and Japan. Growing investment on artificial intelligence and its subfields from the major key players across the globe is likely to boost up for technological sector. This would be the significant factor for the growth of deep learning market in the region as growing application of deep learning solutions is expected to be driven by the growth of emerging retail sector as well as other industries, thus, calling for advanced solutions for data handling as well as simpler workflow.
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Deep Learning Market Dynamics
Drivers
- Growing Adopting in Healthcare Sector
Deep learning has quickly gained traction within the healthcare sector to improve diagnostic accuracy and provide custom-tailored treatment plans. Similarly, the healthcare industry would trend to implement deep learning algorithms to analyse medical imaging, for example MRI and CT scans, to identify diseases like cancer at the earliest possible time.
- Growing Usage of Chatbots
Growing demand for intelligent virtual assistants and chat agents is another factor driving the market. Such technologies are also continuously utilized for real-time personalized recommendations and support to customers in customer service, marketing, and sales applications. As a result, the algorithms behind these technologies are constantly evolving leading to a greater efficiency and accuracy of both technologies, which in result is contributing to growth of market.
Restraints
- Low Incorporation with Big Data
Big data is used as the training dataset for deep learning, which gives it an advantage. The unavailability of a sufficient amount of reliable data can be a drawback for the whole system. For a data model to function successfully, substantial data is required. Due to the lack of available resources, collecting this data can be challenging.
- High Initial Investment
Deep Learning can outperform other techniques, but only if we have sufficient data and a good investment at the start. Due to the complexity of the models over the datasets, training cost a lot of money. In, deep learning requires powerful GPU and hundreds of machines. Thus, the accuracy with the utmost precision is achieved with an increased initial cost. This is expected to be challenging for the growth of the deep learning industry.
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Deep Learning Market Competitive Landscape
The market is a highly competitive with quick technological adaptations and huge investments into research and development. Notably features are advanced algorithms for recognizing patterns, the use of neural networks architecture, and deployment in industries including healthcare, automotive, and finance. Innovation in computational power, data efficiency and algorithm complexity are what pushes the top market players to compete on top of each other. To cater to the varied demands of the deep learning industry and provide solutions ensuring expansion of the market, leading players are emphasizing on scalability, interpretability, and integration capabilities. In addition, the last few years witnessed number of product launches and mergers & acquisition in the market.
Top Player’s Company Profile
- Google LLC
- Microsoft Corporation
- IBM Corporation
- Amazon Web Services (AWS)
- NVIDIA Corporation
- Intel Corporation
- Meta Platforms (Facebook)
- Qualcomm Inc.
- Micron Technology
- Samsung Electronics
- H2O.ai
- Dataiku
- KNIME
- Pathmind
- Sensory Inc.
- Baidu
- OpenAI
- Cerebras Systems
- Graphcore
- SAS Institute Inc.
Recent Developments
- In March 2024, GE Healthcare announced its partnership with NVIDIA for targeted cohesive integration of the latter's AI technology and software with the GE Healthcare medical imaging expertise to enhance precise ultrasound diagnostic service offerings.
- In April 2023, NVIDIA introduced its H100 Tensor Core GPU, which is tailored specifically for deep learning jobs. The introduction of this chip is bound to redefine the AI computing landscape and elevate the bar for deep learning, benefitting countless fields such as autonomous driving and healthcare diagnostics.
- Baidu announced a new set of AI cloud services focused on deep learning and big data analytics in March 2024. The plan will speed up China’s digital development, specifically in industries such as e-commerce, autonomous driving, and healthcare.
Deep Learning Key Market Trends
- Emerging Manufacturing Sector: The manufacturing industry is making use of deep learning for developing industrial processes with a highest accuracy degree and the best efficiency. Employing deep learning algorithms for predictive maintenance, where machines can predict the need for repair or maintenance, decrease downtime and increase the production of the manufacturing sector.
- Growing Use of Predictive Analytics in Retail and Finance Sectors: Adoption is increasing across multiple industries such as finance and retail, as deep learning algorithms can analyse large amounts of data and find patterns. This allows companies to create insights and forecasts which are based on data and stimulate the growth. In addition, due to the rapid innovations in hardware technology along with the availability of the open-source frameworks for deep learning, implementing and deploying solutions became cheaper and straightforward.
Deep Learning Market SkyQuest Analysis
SkyQuest’s ABIRAW (Advanced Business Intelligence, Research & Analysis Wing) is our Business Information Services team that Collects, Collates, Correlates, and Analyses the Data collected by means of Primary Exploratory Research backed by robust Secondary Desk research.
As per SkyQuest analysis, market expansion is fuelled by trends such as ongoing innovations in model architectures, model pre-training with transfer learning, and the rise of explainable artificial intelligence. Deep neural network architectures are being continuously invented and improved for better performance and efficiency. Cutting edge innovation is on the rise for convolutional neural networks, recurrent neural networks, transformers and many more. Many researchers are now utilizing transfer learning with pre-trained models. Large datasets are used for training these models, but they are fine-tuned to intended specifications needing lesser labelled data.
| Report Metric | Details |
|---|---|
| Market size value in 2022 | USD 48.37 Billion |
| Market size value in 2031 | USD 612.18 Billion |
| Growth Rate | 32.58% |
| Base year | 2023 |
| Forecast period | 2024-2031 |
| Forecast Unit (Value) | USD Billion |
| Segments covered |
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| Regions covered | North America (US, Canada), Europe (Germany, France, United Kingdom, Italy, Spain, Rest of Europe), Asia Pacific (China, India, Japan, Rest of Asia-Pacific), Latin America (Brazil, Rest of Latin America), Middle East & Africa (South Africa, GCC Countries, Rest of MEA) |
| Companies covered |
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| Customization scope | Free report customization with purchase. Customization includes:-
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Table Of Content
Executive Summary
Market overview
- Exhibit: Executive Summary – Chart on Market Overview
- Exhibit: Executive Summary – Data Table on Market Overview
- Exhibit: Executive Summary – Chart on Deep Learning Market Characteristics
- Exhibit: Executive Summary – Chart on Market by Geography
- Exhibit: Executive Summary – Chart on Market Segmentation
- Exhibit: Executive Summary – Chart on Incremental Growth
- Exhibit: Executive Summary – Data Table on Incremental Growth
- Exhibit: Executive Summary – Chart on Vendor Market Positioning
Parent Market Analysis
Market overview
Market size
- Market Dynamics
- Exhibit: Impact analysis of DROC, 2021
- Drivers
- Opportunities
- Restraints
- Challenges
- Exhibit: Impact analysis of DROC, 2021
- SWOT Analysis
KEY MARKET INSIGHTS
- Technology Analysis
- (Exhibit: Data Table: Name of technology and details)
- Pricing Analysis
- (Exhibit: Data Table: Name of technology and pricing details)
- Supply Chain Analysis
- (Exhibit: Detailed Supply Chain Presentation)
- Value Chain Analysis
- (Exhibit: Detailed Value Chain Presentation)
- Ecosystem Of the Market
- Exhibit: Parent Market Ecosystem Market Analysis
- Exhibit: Market Characteristics of Parent Market
- IP Analysis
- (Exhibit: Data Table: Name of product/technology, patents filed, inventor/company name, acquiring firm)
- Trade Analysis
- (Exhibit: Data Table: Import and Export data details)
- Startup Analysis
- (Exhibit: Data Table: Emerging startups details)
- Raw Material Analysis
- (Exhibit: Data Table: Mapping of key raw materials)
- Innovation Matrix
- (Exhibit: Positioning Matrix: Mapping of new and existing technologies)
- Pipeline product Analysis
- (Exhibit: Data Table: Name of companies and pipeline products, regional mapping)
- Macroeconomic Indicators
COVID IMPACT
- Introduction
- Impact On Economy—scenario Assessment
- Exhibit: Data on GDP - Year-over-year growth 2016-2022 (%)
- Revised Market Size
- Exhibit: Data Table on Deep Learning Market size and forecast 2021-2027 ($ million)
- Impact Of COVID On Key Segments
- Exhibit: Data Table on Segment Market size and forecast 2021-2027 ($ million)
- COVID Strategies By Company
- Exhibit: Analysis on key strategies adopted by companies
MARKET DYNAMICS & OUTLOOK
- Market Dynamics
- Exhibit: Impact analysis of DROC, 2021
- Drivers
- Opportunities
- Restraints
- Challenges
- Exhibit: Impact analysis of DROC, 2021
- Regulatory Landscape
- Exhibit: Data Table on regulation from different region
- SWOT Analysis
- Porters Analysis
- Competitive rivalry
- Exhibit: Competitive rivalry Impact of key factors, 2021
- Threat of substitute products
- Exhibit: Threat of Substitute Products Impact of key factors, 2021
- Bargaining power of buyers
- Exhibit: buyers bargaining power Impact of key factors, 2021
- Threat of new entrants
- Exhibit: Threat of new entrants Impact of key factors, 2021
- Bargaining power of suppliers
- Exhibit: Threat of suppliers bargaining power Impact of key factors, 2021
- Competitive rivalry
- Skyquest special insights on future disruptions
- Political Impact
- Economic impact
- Social Impact
- Technical Impact
- Environmental Impact
- Legal Impact
Market Size by Region
- Chart on Market share by geography 2021-2027 (%)
- Data Table on Market share by geography 2021-2027(%)
- North America
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- USA
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Canada
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Europe
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- Germany
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Spain
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- France
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- UK
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of Europe
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Asia Pacific
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- China
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- India
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Japan
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- South Korea
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of Asia Pacific
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Latin America
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- Brazil
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of South America
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Middle East & Africa (MEA)
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- GCC Countries
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- South Africa
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of MEA
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
KEY COMPANY PROFILES
- Competitive Landscape
- Total number of companies covered
- Exhibit: companies covered in the report, 2021
- Top companies market positioning
- Exhibit: company positioning matrix, 2021
- Top companies market Share
- Exhibit: Pie chart analysis on company market share, 2021(%)
- Total number of companies covered
Methodology
For the Deep Learning Market, our research methodology involved a mixture of primary and secondary data sources. Key steps involved in the research process are listed below:
1. Information Procurement: This stage involved the procurement of Market data or related information via primary and secondary sources. The various secondary sources used included various company websites, annual reports, trade databases, and paid databases such as Hoover's, Bloomberg Business, Factiva, and Avention. Our team did 45 primary interactions Globally which included several stakeholders such as manufacturers, customers, key opinion leaders, etc. Overall, information procurement was one of the most extensive stages in our research process.
2. Information Analysis: This step involved triangulation of data through bottom-up and top-down approaches to estimate and validate the total size and future estimate of the Deep Learning Market.
3. Report Formulation: The final step entailed the placement of data points in appropriate Market spaces in an attempt to deduce viable conclusions.
4. Validation & Publishing: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helped us finalize data points to be used for final calculations. The final Market estimates and forecasts were then aligned and sent to our panel of industry experts for validation of data. Once the validation was done the report was sent to our Quality Assurance team to ensure adherence to style guides, consistency & design.
Analyst Support
Customization Options
With the given market data, our dedicated team of analysts can offer you the following customization options are available for the Deep Learning Market:
Product Analysis: Product matrix, which offers a detailed comparison of the product portfolio of companies.
Regional Analysis: Further analysis of the Deep Learning Market for additional countries.
Competitive Analysis: Detailed analysis and profiling of additional Market players & comparative analysis of competitive products.
Go to Market Strategy: Find the high-growth channels to invest your marketing efforts and increase your customer base.
Innovation Mapping: Identify racial solutions and innovation, connected to deep ecosystems of innovators, start-ups, academics, and strategic partners.
Category Intelligence: Customized intelligence that is relevant to their supply Markets will enable them to make smarter sourcing decisions and improve their category management.
Public Company Transcript Analysis: To improve the investment performance by generating new alpha and making better-informed decisions.
Social Media Listening: To analyze the conversations and trends happening not just around your brand, but around your industry as a whole, and use those insights to make better Marketing decisions.
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