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The market for AI toolkits is expected to grow from USD 19.25 billion in 2023 to USD 91.26 billion by 2030, expanding at a CAGR (Compound Annual Growth Rate) of 37.2%.
The rising accessibility of high-performance hardware components such as GPUs and TPUs has substantially advanced the efficiency of training and deploying sophisticated AI models. Artificial intelligence toolkits are built specifically to maximize the most of this computing power, allowing for quicker model building and better performance. This integration of AI toolkits with robust hardware resources is a vital component driving the rise of the AI toolkit market, as it empowers developers and organizations to harness cutting-edge AI capabilities for a broad spectrum of applications.
An important market driver in the AI toolkit ecosystem is software that can generate content like articles, ads, and social media updates. These kits are automating the generation of high-quality, contextually relevant content at scale, which is changing the game for content creators and distributors across all sectors. As a result, content creation is accelerated, costs are decreased, and content distribution is widened. Furthermore, AI-generated content expands beyond text-based materials to include AI-generated music, movies, photos, and songs. With this multimodal method, artists, content creators, and corporations have access to previously unexplored channels for communicating with their respective audiences. Artificial intelligence toolkits are growing in popularity because they can meet a wide variety of content requirements without sacrificing quality or relevancy.
Hence, the need for AI models and applications has also increased. Using the right model architecture, gathering the right data, and tuning the model to meet the intended KPIs are all crucial steps in creating a reliable AI tool. The process of manually identifying the optimal models and hyperparameters for the target KPI can be simplified with the aid of automated machine learning (AutoML). It can automatically discover the optimal AI model for a certain purpose and hide a lot of the difficult steps involved in generating and optimizing AI models. Several AI frameworks, including NVIDIA TAO, have an AutoML capability that can optimize a model's hyperparameters without any human intervention. This aids in training and deploying high-quality models, two areas where AutoML is gaining popularity.
A significant obstacle for companies and developers is the lack of uniformity in the market for AI toolkits. Each AI toolset available in the market has its pros and weaknesses. This complicates matters for both organizations and developers, since each must become familiar with and adept at using a unique set of toolkits. The rapid pace of AI invention, the variety of AI applications, and the open-source nature of many AI toolkits all contribute to the need for more uniformity in the AI toolkits. New AI toolkits appear regularly due to the dynamic nature of the field.
This makes it harder for standards to be produced and adopted. AI is being employed in numerous industries and applications, which has led to the development of various specialized AI toolkits. Open source software development communities create and update several AI toolkits. This makes it tough to coordinate development and maintain compatibility between multiple toolkits. There are a number of ways in which organizations and developers may suffer as a result of the lack of standardization in the market for AI toolkits.
An important factor propelling the artificial intelligence toolkit market is the growing number of businesses that are adopting computer vision for use in factory automation. Robotic assembly, object tracking, and visual inspection are just some of the many industrial applications for computer vision. It provides precision and efficiency in industrial operations, boosting product quality and decreasing errors. The need for AI toolkits with built-in computer vision capabilities has skyrocketed as companies adopt automation and smart manufacturing methods. These toolkits promote the integration of advanced vision systems into automated processes, stimulating innovation and supporting the growth of the AI toolkit market for industrial applications.
Report Coverage
Global Artificial Intelligence (AI) Toolkit research report categorizes the market for global based on various segments and regions, forecasts revenue growth, and analyzes trends in each submarket. Global Artificial Intelligence (AI) Toolkit report analyses the key growth drivers, opportunities, and challenges influencing the global market. Recent market developments and Artificial Intelligence (AI) Toolkit competitive strategies such as expansion, product launch and development, partnership, merger, and acquisition have been included to draw the competitive landscape in the market. The report strategically identifies and profiles the key Artificial Intelligence (AI) Toolkit market players and analyses their core competencies in each global market sub-segments.
Key Points Covered in the Report
Competitive Environment:
The research provides an accurate study of the major organisations and companies operating in the global Artificial Intelligence (AI) Toolkit market, along with a comparative evaluation based on their product portfolios, corporate summaries, geographic reach, business plans, Artificial Intelligence (AI) Toolkit market shares in specific segments, and SWOT analyses. A detailed analysis of the firms' recent news and developments, such as product development, inventions, joint ventures, partnerships, mergers and acquisitions, strategic alliances, and other activities, is also included in the study. This makes it possible to assess the level of market competition as a whole.
List of Major Market Participants
Microsoft (US), Google (US), IBM (US), Oracle (US), Thales Group (France), Salesforce (US), Intel (US), Adobe (US), Meta Platforms (US), AWS (US), NVIDIA Corporation (US), H2O.ai (US), Alteryx (US), Altair (US), KNIME (Switzerland), DataRobot (US), Jasper (US), Rasa (US), SuperAnnotate (US), OpenAI (US), Obviously AI (US), Fiddler AI (US), Determined AI (US), Snorkel AI (US), Levity AI (Germany), Union AI (US), Attri AI (US), Regie.ai (US)
Primary Target Market
Market Segment:
This study forecasts global, regional, and country revenue from 2019 to 2030. INFINITIVE DATA EXPERT has segmented the global Artificial Intelligence (AI) Toolkit market based on the below-mentioned segments:
Global Artificial Intelligence (AI) Toolkit Market, By Offering
Software
Hardware
Services
Global Artificial Intelligence (AI) Toolkit market, By Technology
Natural Language Processing
Machine Learning
Computer Vision
Robotic Process Automation
Global Artificial Intelligence (AI) Toolkit Market, By Vertical
Banking, Financial Services, & Insurance (BFSI)
Retail & eCommerce
Healthcare & Life Sciences
Manufacturing
Telecom
IT & ITeS
Media & Entertainment
Energy & Utilities
Government & Defense
Automotive, Transportation, & Logistics
Other Verticals (Education, Travel & Hospitality, Construction & Real Estate, and Agriculture)
Global Artificial Intelligence (AI) Toolkit market, Regional Analysis
The rising accessibility of high-performance hardware components such as GPUs and TPUs has substantially advanced the efficiency of training and deploying sophisticated AI models. Artificial intelligence toolkits are built specifically to maximize the most of this computing power, allowing for quicker model building and better performance. This integration of AI toolkits with robust hardware resources is a vital component driving the rise of the AI toolkit market, as it empowers developers and organizations to harness cutting-edge AI capabilities for a broad spectrum of applications.
An important market driver in the AI toolkit ecosystem is software that can generate content like articles, ads, and social media updates. These kits are automating the generation of high-quality, contextually relevant content at scale, which is changing the game for content creators and distributors across all sectors. As a result, content creation is accelerated, costs are decreased, and content distribution is widened. Furthermore, AI-generated content expands beyond text-based materials to include AI-generated music, movies, photos, and songs. With this multimodal method, artists, content creators, and corporations have access to previously unexplored channels for communicating with their respective audiences. Artificial intelligence toolkits are growing in popularity because they can meet a wide variety of content requirements without sacrificing quality or relevancy.
Hence, the need for AI models and applications has also increased. Using the right model architecture, gathering the right data, and tuning the model to meet the intended KPIs are all crucial steps in creating a reliable AI tool. The process of manually identifying the optimal models and hyperparameters for the target KPI can be simplified with the aid of automated machine learning (AutoML). It can automatically discover the optimal AI model for a certain purpose and hide a lot of the difficult steps involved in generating and optimizing AI models. Several AI frameworks, including NVIDIA TAO, have an AutoML capability that can optimize a model's hyperparameters without any human intervention. This aids in training and deploying high-quality models, two areas where AutoML is gaining popularity.
A significant obstacle for companies and developers is the lack of uniformity in the market for AI toolkits. Each AI toolset available in the market has its pros and weaknesses. This complicates matters for both organizations and developers, since each must become familiar with and adept at using a unique set of toolkits. The rapid pace of AI invention, the variety of AI applications, and the open-source nature of many AI toolkits all contribute to the need for more uniformity in the AI toolkits. New AI toolkits appear regularly due to the dynamic nature of the field.
This makes it harder for standards to be produced and adopted. AI is being employed in numerous industries and applications, which has led to the development of various specialized AI toolkits. Open source software development communities create and update several AI toolkits. This makes it tough to coordinate development and maintain compatibility between multiple toolkits. There are a number of ways in which organizations and developers may suffer as a result of the lack of standardization in the market for AI toolkits.
An important factor propelling the artificial intelligence toolkit market is the growing number of businesses that are adopting computer vision for use in factory automation. Robotic assembly, object tracking, and visual inspection are just some of the many industrial applications for computer vision. It provides precision and efficiency in industrial operations, boosting product quality and decreasing errors. The need for AI toolkits with built-in computer vision capabilities has skyrocketed as companies adopt automation and smart manufacturing methods. These toolkits promote the integration of advanced vision systems into automated processes, stimulating innovation and supporting the growth of the AI toolkit market for industrial applications.
Report Coverage
Global Artificial Intelligence (AI) Toolkit research report categorizes the market for global based on various segments and regions, forecasts revenue growth, and analyzes trends in each submarket. Global Artificial Intelligence (AI) Toolkit report analyses the key growth drivers, opportunities, and challenges influencing the global market. Recent market developments and Artificial Intelligence (AI) Toolkit competitive strategies such as expansion, product launch and development, partnership, merger, and acquisition have been included to draw the competitive landscape in the market. The report strategically identifies and profiles the key Artificial Intelligence (AI) Toolkit market players and analyses their core competencies in each global market sub-segments.
| REPORT ATTRIBUTES | DETAILS |
|---|---|
| Study Period | 2017-2030 |
| Base Year | 2022 |
| Forecast Period | 2022-2030 |
| Historical Period | 2017-2021 |
| Unit | Value (USD Billion) |
| Key Companies Profiled | Microsoft (US), Google (US), IBM (US), Oracle (US), Thales Group (France), Salesforce (US), Intel (US), Adobe (US), Meta Platforms (US), AWS (US), NVIDIA Corporation (US), H2O.ai (US), Alteryx (US), Altair (US), KNIME (Switzerland), DataRobot (US), Jasper (US), Rasa (US), SuperAnnotate (US), OpenAI (US), Obviously AI (US), Fiddler AI (US), Determined AI (US), Snorkel AI (US), Levity AI (Germany), Union AI (US), Attri AI (US), Regie.ai (US) |
| Segments Covered | • By Product |
| Customization Scope | Free report customization (equivalent to up to 3 analyst working days) with purchase. Addition or alteration to country, regional & segment scope |
Key Points Covered in the Report
- Market Revenue of Artificial Intelligence (AI) Toolkit Market from 2021 to 2030.
- Market Forecast for Artificial Intelligence (AI) Toolkit Market from 2021 to 2030.
- Regional Market Share and Revenue from 2021 to 2030.
- Country Market share within region from 2021 to 2030.
- Key Type and Application Revenue and forecast.
- Company Market Share Analysis, Artificial Intelligence (AI) Toolkit competitive scenario, ranking, and detailed company
profiles. - Market driver, restraints, and detailed COVID-19 impact on Artificial Intelligence (AI) Toolkit
Market
Competitive Environment:
The research provides an accurate study of the major organisations and companies operating in the global Artificial Intelligence (AI) Toolkit market, along with a comparative evaluation based on their product portfolios, corporate summaries, geographic reach, business plans, Artificial Intelligence (AI) Toolkit market shares in specific segments, and SWOT analyses. A detailed analysis of the firms' recent news and developments, such as product development, inventions, joint ventures, partnerships, mergers and acquisitions, strategic alliances, and other activities, is also included in the study. This makes it possible to assess the level of market competition as a whole.
List of Major Market Participants
Microsoft (US), Google (US), IBM (US), Oracle (US), Thales Group (France), Salesforce (US), Intel (US), Adobe (US), Meta Platforms (US), AWS (US), NVIDIA Corporation (US), H2O.ai (US), Alteryx (US), Altair (US), KNIME (Switzerland), DataRobot (US), Jasper (US), Rasa (US), SuperAnnotate (US), OpenAI (US), Obviously AI (US), Fiddler AI (US), Determined AI (US), Snorkel AI (US), Levity AI (Germany), Union AI (US), Attri AI (US), Regie.ai (US)
Primary Target Market
- Market Players of Artificial Intelligence (AI) Toolkit
- Investors
- End-users
- Government Authorities
- Consulting And Research Firm
- Venture capitalists
- Third-party knowledge providers
- Value-Added Resellers (VARs)
Market Segment:
This study forecasts global, regional, and country revenue from 2019 to 2030. INFINITIVE DATA EXPERT has segmented the global Artificial Intelligence (AI) Toolkit market based on the below-mentioned segments:
Global Artificial Intelligence (AI) Toolkit Market, By Offering
Software
Hardware
Services
Global Artificial Intelligence (AI) Toolkit market, By Technology
Natural Language Processing
Machine Learning
Computer Vision
Robotic Process Automation
Global Artificial Intelligence (AI) Toolkit Market, By Vertical
Banking, Financial Services, & Insurance (BFSI)
Retail & eCommerce
Healthcare & Life Sciences
Manufacturing
Telecom
IT & ITeS
Media & Entertainment
Energy & Utilities
Government & Defense
Automotive, Transportation, & Logistics
Other Verticals (Education, Travel & Hospitality, Construction & Real Estate, and Agriculture)
Global Artificial Intelligence (AI) Toolkit market, Regional Analysis
- Europe: Germany, Uk, France, Italy, Spain, Russia, Rest of Europe
- The Asia Pacific: China,Japan,India,South Korea,Australia,Rest of Asia Pacific
- South America: Brazil, Argentina, Rest of South America
- Middle East & Africa: UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa
About This Research
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