The market size in the 'Machine Learning' segment of the artificial intelligence market in the United States was modeled to be ************* U.S. dollars in 2024. Between 2020 and 2024, the market size rose by ************ U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by ************** U.S. dollars over the period from 2024 to 2031, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Machine Learning.

The size of the U.S. Machine Learning (ML) Market was valued at USD 4.74 USD billion in 2023 and is projected to reach USD 43.38 USD billion by 2032, with an expected CAGR of 37.2% during the forecast period. The U.S. Machine Learning (ML) Market refers to the application and development of machine learning technologies within the United States. Machine learning, a subset of artificial intelligence (AI), involves algorithms and models that allow systems to learn from data, identify patterns, and make decisions or predictions without being explicitly programmed. In the U.S., the ML market is growing rapidly, driven by advancements in computing power, large data sets, and the increasing demand for automation and AI across industries. This remarkable ascent is fueled by a confluence of factors, including the advent of hybrid and genetically modified seeds, proactive government initiatives aimed at enhancing agricultural productivity, an escalating consciousness regarding food security, and the rapid advancement of technologies that underpin precision agriculture. Hybrid seeds, offering a potent combination of desirable traits from multiple parent varieties, are poised to revolutionize crop production by improving yield, resilience, and nutritional content. innovation. Key drivers for this market are: Growing Adoption of Mobile Commerce to Augment the Demand for Virtual Fitting Room Tool . Potential restraints include: Lack of Coding Skills Likely to Limit Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

According to a survey conducted among healthcare providers in the United States in April 2021, ** percent of respondents reported that in their hospital or health systems artificial intelligence (AI)/machine learning efforts were in the pilot stage and the rollout was to be decided, while a further ** percent said that it is in the early stage initiatives.

In 2024, the market size change in the 'Machine Learning' segment of the artificial intelligence market in the United States was modeled to stand at ***** percent. Between 2021 and 2024, the market size change dropped by ***** percentage points. The market size change is expected to drop by **** percentage points between 2024 and 2031, showing a continuous downward movement throughout the period.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Machine Learning.

The data is from qualitative case study research into the implementation of the Rapid Recruitment project at Walmart, US, in 2020. One of the key elements of the rapid recruitment project was the use of a machine learning algorithm in the hiring system for hourly paid store-level associates (employees). The research involved semi-structured interviews with fourteen respondents with different roles and responsibilities in relation to the hiring process including: seven head office staff responsible for developing and implementing the system, five store-level managers and HR staff who used the system and two recently recruited employees. Interviews lasted 30 to 90 minutes and were conducted via video conferencing during the Covid-19 pandemic from September to December 2020. Interviews were supplemented with bi-weekly meetings with a business sponsor at the organisation and follow-up information gathered by email. Interviews were recorded and transcribed by the researchers. The interviews explored: recent changes to the hiring system, aims and objectives of the changes, the of motivations behind the changes, the development and implementation process, user adoption and perceptions of the new system and its effectiveness. The research found that the Rapid Recruitment project had largely been successful. Most users were using the new system as intended, the system had sped up the hiring process, enabled the organisation to hire greater numbers of staff during the increased demand due to the pandemic and the organisation reported that it had improved hiring outcomes (90-day turnover rates). However, not all users were confident in the new system or trusted the technology used, which in some cases meant that they were not using the system in the way intended, potentially undermining some of the objectives of the changes. Interview data could not be deposited to the archive because it was protected by a non-disclosure agreement (NDA) but research documents and metadata is deposited.

The government of the United States of America has been steadily increasing their expenditure in AI, ML, and autonomy since 2018, having reached up from approximately **** billion U.S. dollars to **** billion U.S. dollars by 2023. In 2023, the largest share of this expenditure was directed specifically to machine learning.

An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supply groundwater zones (Ransom and others, 2022) by incorporating additional monitoring well samples and modifying and adding predictor variables. The shallow zone model included variables representing well characteristics, hydrologic conditions, soil type, geology, climate, oxidation/reduction, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. This data release documents the model and provides the model results. Included in this data release are, 1) a model archive of the R project: source code, input files (including model training and testing data, rasters of all final predictor variables, and an output raster representing predicted nitrate concentration in the shallow zone), 2) a read_me.txt file describing the model archive and an explanation of its use and the modeling details, and 3) a table describing the model variables.

United States AI in Manufacturing Market was valued at USD 1.1 billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 16.7% through 2029.

Pages	86
Market Size	2023: USD 1.1 Billion
Forecast Market Size	2029: USD 2.80 Billion
CAGR	2024-2029: 16.7%
Fastest Growing Segment	Machine Learning
Largest Market	Midwest US
Key Players	1. IBM Corporation 2. Siemens AG 3. General Electric Company 4. Microsoft Corporation 5. Oracle Corporation 6. SAP SE 7. Rockwell Automation, Inc. 8. NVIDIA Corporation 9. Intel Corporation 10. Cisco Systems, Inc.

This dataset contains 3,229 feather files with time series of all model inputs for machine learning models predicting streamflow drought across the conterminous United States (CONUS). Files contain weekly time series of streamflow percentiles, meteorology, snow water equivalent, forecast meteorology, estimated water use, soil moisture, as well as lagged versions of these datasets. Values in these files were assembled from existing published datasets as explained in the data quality and processing steps sections.

The automated machine learning market had an estimated market share worth US$ 700 million in 2023, and it is predicted to reach a global market valuation of US$ 42.2 billion by 2034, growing at a steady CAGR of 44.9% from 2024 to 2034.

Report Attribute	Details
Estimated Market Value for 2023	US$ 700 million
Expected Market Value for 2024	US$ 1 billion
Projected Forecast Value for 2034	US$ 42.2 billion
Anticipated Growth Rate from 2024 to 2034	4 4.9% CAGR

Automated Machine Learning Market Historical Analysis from 2019 to 2023 vs. Forecast Outlook from 2024 to 2034

Historical CAGR from 2019 to 2023	48.2%
Forecast CAGR from 2024 to 2034	44.9%

Category-wise Insights

Solution Type	Standalone
CAGR from 2024 to 2034	44.7%

Automation Type	Feature Engineering
Market Share in 2024	44.5%

Region-wise Analysis

Countries	CAGR from 2024 to 2034
The United States	45%
The United Kingdom	46.1%
China	45.4%
Japan	46%
South Korea	47.2%

Report Scope

Report Attribute	Details
Growth Rate	CAGR of 44.9% from 2024 to 2034
Market value in 2024	US$ 1 billion
Market value in 2034	US$ 42.2 billion
Base Year for Estimation	2023
Historical Data	2019 to 2023
Forecast Period	2024 to 2034
Quantitative Units	US$ billion for value
Report Coverage	Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis
Segments Covered	Solution Automation Type End User Region
Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia The Middle East & Africa
Countries Profiled	The United States Canada Brazil Mexico Germany The United Kingdom France Spain Italy Poland Russia Czech Republic Romania India Bangladesh Australia New Zealand China Japan South Korea GCC Countries South Africa Israel
Key Companies Profiled	Datarobot Inc. Amazon Web Services Inc. dotData Inc. IBM Corporation Dataiku SAS Institute Inc. Microsoft Corporation Google LLC H2O.ai Aible Inc.
Customization Scope	Available on Request

This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico. A total of 120 original research papers were included and details such as trends in publication, spatial location, institutions, publishing issues, subject areas, algorithms applied, and performance metrics were discussed. Furthermore, future directions and opportunities are presented. A total of 15 subject areas were identified, where Social Sciences and Medicine were the main application areas. It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. Regarding the performance metrics applied, accuracy and recall were the most employed. This paper could assist the readers in understanding the several Machine Learning and Deep Learning techniques used and their subject area of application in the Artificial Intelligence field in the country. Moreover, the study could provide significant knowledge in the development and implementation of a national AI strategy, according to country needs.

A survey carried out in July 2022 in the United States found that ** percent of marketing professionals were using artificial intelligence (AI) and machine learning (ML) tools in their marketing programs for e-mail marketing purposes. Another ** percent said that they used the same tools for advertising. An additional ** percent of American marketing professionals stated that AI and ML helped them with copywriting.

United States AI in Agriculture Market was valued at USD 351.09 Million in 2023 and is anticipated to reach USD 705.74 Million in the forecast period with a CAGR of 12.30% through 2029.

Pages	85
Market Size	2023: USD 351.09 Million
Forecast Market Size	2029: USD 705.74 Million
CAGR	2024-2029: 12.30%
Fastest Growing Segment	Predictive Analytics
Largest Market	Mid-west
Key Players	1.International Business Machines Corporation (IBM) 2.Granular, Inc. 3.Microsoft 4.Deere & Company 5.Awhere Inc. 6.Climate LLC. 7.Agribotix, LLC 8.Descartes Labs Inc. 9.Valmont Industries, Inc.

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AI And Machine Learning In Business Market Size 2025-2029

The AI and machine learning in business market size is valued to increase by USD 240.3 billion, at a CAGR of 24.9% from 2024 to 2029. Unprecedented advancements in AI technology and generative AI catalyst will drive the ai and machine learning in business market.

Major Market Trends & Insights

North America dominated the market and accounted for a 36% growth during the forecast period.
By Component - Solutions segment was valued at USD 24.98 billion in 2023
By Sector - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 906.25 million
Market Future Opportunities: USD 240301.30 million
CAGR from 2024 to 2029 : 24.9%

Market Summary

In the realm of business innovation, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as indispensable tools, shaping industries through unprecedented advancements. The market for AI in business is experiencing a surge in growth, with an estimated 1.2 billion dollars invested in AI startups in 2020 alone. This investment fuels the proliferation of generative AI copilots and embedded AI in enterprise platforms, revolutionizing processes and enhancing productivity. However, the integration of AI and ML in businesses presents a unique challenge: the scarcity of specialized talent.
As these technologies become increasingly essential, companies are compelled to invest in workforce transformation, upskilling their employees or hiring new talent to ensure they can harness the full potential of AI. This imperative for human capital development is a testament to the transformative power of AI and ML in business, driving growth and innovation across industries.

What will be the Size of the AI And Machine Learning In Business Market during the forecast period?

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How is the AI And Machine Learning In Business Market Segmented ?

The AI and machine learning in business industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Sector

  Large enterprises
  SMEs


Application

  Data analytics
  Predictive analytics
  Cyber security
  Supply chain and inventory management
  Others


End-user

  IT and telecom
  BFSI
  Retail and manufacturing
  Healthcare
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, driven by advancements in big data processing, algorithm performance metrics, and scalable infrastructure. API integrations, recommendation engines, and predictive analytics tools are increasingly common, with model training datasets becoming larger and more diverse. Business process automation relies on feature engineering processes, data mining techniques, and model deployment strategies. Cloud computing platforms facilitate the use of deep learning algorithms, machine learning models, and real-time data processing. In 2023, SAP introduced Joule, an AI copilot that uses natural language processing for proactive and contextualized insights, reflecting the trend towards AI-driven automation and process optimization. This includes supply chain optimization, sales forecasting models, sentiment analysis tools, and anomaly detection systems.

Furthermore, AI-powered chatbots, data visualization dashboards, and model explainability techniques support data governance frameworks. Cybersecurity protocols and fraud detection models are also essential components of this dynamic landscape. According to a recent report, the global AI in business market is projected to reach USD267 billion by 2027, underscoring its transformative impact on industries.

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The Solutions segment was valued at USD 24.98 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The artificial intelligence (AI) and machine learning (ML) in business market is experiencing a significant surge, with North America leading the charge. The region, particularly the United States, h

The United States has the largest machine learning market worldwide in 2024, with a value of over ** billion U.S. dollars. India, despite its large population, is still only comparable to the large European nations in market size.

Snapshot img

Artificial Intelligence (AI) Market In Education Sector Size 2025-2029

The artificial intelligence (ai) market in education sector size is forecast to increase by USD 4.03 billion at a CAGR of 59.2% between 2024 and 2029.

The Artificial Intelligence (AI) market in the education sector is experiencing significant growth due to the increasing demand for personalized learning experiences. Schools and universities are increasingly adopting AI technologies to create customized learning paths for students, enabling them to progress at their own pace and receive targeted instruction. Furthermore, the integration of AI-powered chatbots in educational institutions is streamlining administrative tasks, providing instant support to students, and enhancing overall campus engagement. However, the high cost associated with implementing AI solutions remains a significant challenge for many educational institutions, particularly those with limited budgets. Despite this hurdle, the long-term benefits of AI in education, such as improved student outcomes, increased operational efficiency, and enhanced learning experiences, make it a worthwhile investment for forward-thinking educational institutions. Companies seeking to capitalize on this market opportunity should focus on developing cost-effective AI solutions that cater to the unique needs of educational institutions while delivering measurable results. By addressing the cost challenge and providing tangible value, these companies can help educational institutions navigate the complex landscape of AI adoption and unlock the full potential of this transformative technology in education.

What will be the Size of the Artificial Intelligence (AI) Market In Education Sector during the forecast period?

Request Free SampleArtificial Intelligence (AI) is revolutionizing the education sector by enhancing teaching experiences and delivering personalized learning. AI technologies, including deep learning and machine learning, power adaptive learning platforms and intelligent tutoring systems. These systems create learner models to provide personalized recommendations and instructional activities based on individual students' needs. AI is transforming traditional educational models, enabling intelligent systems to handle administrative tasks and data analysis. The integration of AI in education is leading to the development of intelligent training software for skilled professionals. Furthermore, AI is improving knowledge delivery through data-driven insights and enhancing the learning experience with interactive and engaging pedagogical models. AI technologies are also being used to analyze training formats and optimize domain models for more effective instruction. Overall, AI is streamlining administrative tasks and providing personalized learning experiences for students and professionals alike.

How is this Artificial Intelligence (AI) In Education Sector Industry segmented?

The artificial intelligence (ai) in education sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHigher educationK-12Learning MethodLearner modelPedagogical modelDomain modelComponentSolutionsServicesApplicationLearning platform and virtual facilitatorsIntelligent tutoring system (ITS)Smart contentFraud and risk managementOthersTechnologyMachine LearningNatural Language ProcessingComputer VisionSpeech RecognitionGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilMiddle East and AfricaUAE

By End-user Insights

The higher education segment is estimated to witness significant growth during the forecast period.The global education sector is witnessing significant advancements with the integration of Artificial Intelligence (AI). AI technologies, including Machine Learning (ML), are revolutionizing various aspects of education, from K-12 schools to higher education and corporate training. Intelligent Tutoring Systems and Adaptive Learning Platforms are increasingly popular, offering Individualized Instruction and Personalized Learning Experiences based on each student's Learning Pathways and Skills Gap. AI-enabled solutions are enhancing Student Engagement by providing Interactive Learning Tools and Real-time communication, while AI platforms and startups are developing Smart Content and Tailored Content for Remote Learning environments. AI is also transforming administrative tasks, such as Assessment processes and Data Management, by providing Personalized Recommendations and Automated Grading. Universities and educational institutions are leveraging AI for Pedagogical model development and Virtual Classrooms, offering Educational Experiences and Virtual support. AI is also being used for Academic mapping an

A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells, and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. This data release documents the model and provides the model results. The model and results are discussed in the associated journal article, Ransom and others (2021). Included in this data release are, 1) a model archive of the R project: source code, input files (including model training and hold-out data, rasters of all final predictor variables, and rasters representing domestic and public supply depth zones), and output files (two rasters of predicted nitrate concentration at the depth zones typical of domestic and public supply wells), 2) a read_me file describing the model archive and an explanation of its use, and 3) tables describing model variables, model fit statistics, and model results [these tables are also included in the Supporting Information published with the journal article Ransom and others (2021)].

As of 2024, the largest shares of AI-related job postings in the United States were related to Artificial Intelligence and Machine Learning, with about 0.94 and 0.92 percent of all job postings in the country, respectively. AI ethics, governance, and regulations had the smallest share, with about 0.02 percent of all job postings in the United States.

Introduction

Global Artificial Intelligence in Drug Discovery Market size is expected to be worth around US$ 13.6 Billion by 2033 from US$ 1.2 Billion in 2023, growing at a CAGR of 27.5% during the forecast period from 2024 to 2033. In 2023, North America led the market, achieving over 60.1% share with a revenue of US$ 0.72 Billion.

This significant growth is driven by the expanding use of AI within the pharmaceutical industry, aimed at enhancing efficiency, reducing the time-to-market for new drugs, and addressing the complexities involved in drug development.

AI technologies are transforming the drug discovery field by providing solutions that quicken the process while increasing precision and cost efficiency. North America, especially the United States, is spearheading this market expansion, propelled by widespread adoption of AI in pharmaceutical research, robust healthcare infrastructure, and a strong emphasis on innovation. This is exemplified by notable partnerships between leading tech companies and pharmaceutical firms, which are crucial for leveraging AI to streamline drug discovery, optimize clinical trials, and advance the development of new therapeutics.

However, the market encounters challenges, particularly the significant costs linked to AI integration and the extensive infrastructure required, which may be daunting for smaller companies and underdeveloped regions. Despite these obstacles, the industry continues to progress, with software and machine learning technologies taking a dominant stance in the market due to their profound impact on data analysis and decision-making in drug discovery. This is especially pertinent in developing treatments for neurodegenerative diseases and in enhancing drug repurposing efforts, where AI’s analytical prowess can lead to innovative breakthroughs in therapeutics.

Further propelled by the COVID-19 pandemic, recent developments have highlighted AI’s indispensable role in drug discovery. The pandemic accelerated a shift towards digitalization in biomedical research, promoting rapid adoption of AI tools for data analysis, disease pattern recognition, and vaccine development. This trend reflects a larger movement towards digital transformation in healthcare, which is expected to persist in driving innovation and growth in the AI drug discovery market.

https://sp-ao.shortpixel.ai/client/to_auto,q_lossy,ret_img,w_1222,h_685/https://market.us/wp-content/uploads/2023/10/Artificial-Intelligence-in-Drug-Discovery-Market-Size.jpg" alt="Artificial Intelligence in Drug Discovery Market Size" class="wp-image-113570">

This metadata record describes 99 streamflow (referred to as flow) metrics calculated using the observed flow records at 1851 streamflow gauges across the conterminous United States from 1950 to 2018. Calculation of these metrics are often used as dependent variables in statistical models to make predictions of these flow metrics at ungaged locations. Specifically, this record describes (1) the U.S. Geological Survey streamgauge identification number, (2) the 1-, 7-, and 30-day consecutive minimum flow normalized by drainage area, DA (Q1/DA, Q7/DA, and Q30/DA [cfs/sq km]), (3) the 1st, 10th, 25th, 50th, 75th, 90th, and 99th nonexceedence flows normalized by DA (P01/DA, P10/DA, P25/DA, P50/DA, P75/DA, P90/DA, P99/DA [cfs/sq km]), (4) the annual mean flows normalized by DA (Mean/DA [cfs/sq km]), (5) the coefficient of variation of the annual minimums and maximum flows (Vmin and Vmax [dimensionless]), the average annual duration of flow pulses less than P10 and greater than P90 (Dl and Dh [number of days]), (6) the average annual number of flow pulses less than P10 and greater than P90 (Fl and Fh [number of events]), (7) the average annual skew of daily flows (Skew [dimensionless]), (8) the number of days where flow greater than the previous day divided by the total number of days (daily rises [dimensionless]), (9) the low- and high-flow timing metrics for winter, spring, summer, and fall (Winter_Tl, Spring_Tl, Summer_Tl, Fall_Tl, Winter_Th, Spring_Th, Summer_Th, and Fall_Th [dimensionless]), (10) the monthly nonexceedence flows normalized by DA (JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, and DEC P'X'/DA where the 'X'=10, 20, 50, 80, and 90 [cfs/sq km]), and (11) monthly mean flow normalized by DA (JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, and DEC mean/DA [cfs/sq km]). For more details for flow metrics related to (2) through (8) and (11), please see Eng, K., Grantham, T.E., Carlisle, D.M., and Wolock, D.M., 2017, Predictability and selection of hydrologic metrics in riverine ecohydrology: Freshwater Science, v. 36(4), p. 915-926 [Also available at https://doi.org/10.1086/694912]. For more details on (9), please see Eng, K., Carlisle, D.M., Grantham, T.E., Wolock, D.M., and Eng, R.L., 2019, Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980-2014: U.S. Geological Survey Scientific Investigations Report 2019-5001, 25 p. [Also available at https://doi.org/10.3133/sir20195001]. For (10), all daily flow values for the month of interest across all years are ranked in descending order, and the flow values associated with 10, 20, 50, 80, and 90 percent of all flow values are assigned as the monthly percent values. The data are in a tab-delimited text format.