Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

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US Deep Learning Market Size 2024-2028

The US deep learning market size is forecast to increase by USD 3.55 billion at a CAGR of 27.17% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. Firstly, the increasing demand for industry-specific solutions is fueling market expansion. Additionally, the high data requirements for deep learning applications are leading to increased data generation and collection. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. However, challenges persist, including the escalating cyberattack rate and the need for strong customer data security. Education institutes are also investing in deep learning research and development to prepare the workforce for the future. Overall, the market is poised for continued growth, driven by these factors and the potential for innovation and advancement in various sectors.

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Deep learning, a subset of artificial intelligence (AI), is a machine learning technique that uses neural networks to model and solve complex problems. This technology is gaining significant traction in various industries across the US, driven by the availability of large datasets and advancements in cloud-based technology. One of the primary areas where deep learning is making a mark is in data centers. Deep learning algorithms are being used to analyze vast amounts of data, enabling businesses to gain valuable insights and make informed decisions. Cloud-based technology is facilitating the deployment of deep learning models at scale, making it an attractive solution for businesses looking to leverage their data.

Furthermore, the market is rapidly evolving, driven by innovations in cloud-based technology, neural networks, and big-data analytics. The integration of machine vision technology and image and visual recognition has driven advancements in industries such as self driving vehicles, digital marketing, and virtual assistance. Companies are leveraging generative adversarial networks (GANs) for cutting-edge news accumulation and content generation. Additionally, machine vision is transforming sectors like retail and manufacturing by enhancing automation and human behavior analysis. With the use of human brain cells generated information, researchers are pushing the boundaries of artificial intelligence. The growing importance of photos and visual data in decision-making further accelerates the market, highlighting the potential of deep learning technologies.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. Deep learning, a subset of artificial intelligence (AI), is revolutionizing various industries in the US through its ability to analyze and interpret complex data. One of its key applications is image recognition, which utilizes neural networks and graphics processing units (GPUs) to identify objects or patterns within images and videos. This technology is increasingly being adopted in data centers and cloud-based solutions for applications such as visual search, product recommendations, and inventory management. In the automotive sector, image recognition is integral to advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Additionally, image recognition is essential for cybersecurity applications, industrial automation, Internet of Things (IoT) devices, and robots, enhancing their functionality and efficiency. Image recognition is transforming industries by providing accurate and real-time insights from visual data, ultimately improving user experience and productivity.

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The Image recognition segment was valued at USD 265.10 billion in 2017 and showed a gradual increase during the forecast period.

Our market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Market Driver

Industry-specific solutions is the key driver of the market. Deep learning has become a pivotal technology in addressing classification tasks across numerous industrie

LinkedIn companies use datasets to access public company data for machine learning, ecosystem mapping, and strategic decisions. Popular use cases include competitive analysis, CRM enrichment, and lead generation.

Use our LinkedIn Companies Information dataset to access comprehensive data on companies worldwide, including business size, industry, employee profiles, and corporate activity. This dataset provides key company insights, organizational structure, and competitive landscape, tailored for market researchers, HR professionals, business analysts, and recruiters.

Leverage the LinkedIn Companies dataset to track company growth, analyze industry trends, and refine your recruitment strategies. By understanding company dynamics and employee movements, you can optimize sourcing efforts, enhance business development opportunities, and gain a strategic edge in your market. Stay informed and make data-backed decisions with this essential resource for understanding global company ecosystems.

Dataset Features

• timestamp: Represents the date and time when the company data was collected.
• id: Unique identifier for each company in the dataset.
• company_id: Identifier linking the company to an external database or internal system.
• url: Website or URL for more information about the company.
• name: The name of the company.
• about: Brief description of the company.
• description: More detailed information about the company's operations and offerings.
• organization_type: Type of the organization (e.g., private, public).
• industries: List of industries the company operates in.
• followers: Number of followers on the company's platform.
• headquarters: Location of the company's headquarters.
• country_code: Code for the country where the company is located.
• country_codes_array: List of country codes associated with the company (may represent various locations or markets).
• locations: Locations where the company operates.
• get_directions_url: URL to get directions to the company's location(s).
• formatted_locations: Human-readable format of the company's locations.
• website: The official website of the company.
• website_simplified: A simplified version of the company's website URL.
• company_size: Number of employees or company size.
• employees_in_linkedin: Number of employees listed on LinkedIn.
• employees: URL of employees.
• specialties: List of the company’s specializations or services.
• updates: Recent updates or news related to the company.
• crunchbase_url: Link to the company’s profile on Crunchbase.
• founded: Year when the company was founded.
• funding: Information on funding rounds or financial data.
• investors: Investors who have funded the company.
• alumni: Notable alumni from the company.
• alumni_information: Details about the alumni, their roles, or achievements.
• stock_info: Stock market information for publicly traded companies.
• affiliated: Companies or organizations affiliated with the company.
• image: Image representing the company.
• logo: URL of the official logo of the company.
• slogan: Company’s slogan or tagline.
• similar: URL of companies similar to this one.

Distribution

• Data Volume: 56.51M rows and 35 columns.
• Structure: Tabular format (CSV, Excel).

Usage

This dataset is ideal for:
- Market Research: Identifying key trends and patterns across different industries and geographies.
- Business Development: Analyzing potential partners, competitors, or customers.
- Investment Analysis: Assessing investment potential based on company size, funding, and industries.
- Recruitment & Talent Analytics: Understanding the workforce size and specialties of various companies.

Coverage

• Geographic Coverage: Global, with company locations and headquarters spanning multiple countries.
• Time Range: Data likely covers both current and historical information about companies.
• Demographics: Focuses on company attributes rather than demographics, but may contain information about the company's workforce.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For building models, conducting research, or enhancing machine learning algorithms with business data.
• Researchers: For academic analysis in fields like economics, business, or technology.
• Businesses: For analysis, competitive benchmarking, and strategic development.
• Investors: For identifying and evaluating potential investment opportunities.

Dataset Name Ideas

• Global Company Profile Database
• **Business Intellige

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

Machine Learning Market Outlook

The global machine learning market is projected to witness a remarkable growth trajectory, with the market size estimated to reach USD 21.17 billion in 2023 and anticipated to expand to USD 209.91 billion by 2032, growing at a compound annual growth rate (CAGR) of 29.2% over the forecast period. This extraordinary growth is primarily propelled by the escalating demand for artificial intelligence-driven solutions across various industries. As businesses seek to leverage machine learning for improving operational efficiency, enhancing customer experience, and driving innovation, the market is poised to expand rapidly. Key factors contributing to this growth include advancements in data generation, increasing computational power, and the proliferation of big data analytics.

A pivotal growth factor for the machine learning market is the ongoing digital transformation across industries. Enterprises globally are increasingly adopting machine learning technologies to optimize their operations, streamline processes, and make data-driven decisions. The healthcare sector, for example, leverages machine learning for predictive analytics to improve patient outcomes, while the finance sector uses machine learning algorithms for fraud detection and risk assessment. The retail industry is also utilizing machine learning for personalized customer experiences and inventory management. The ability of machine learning to analyze vast amounts of data in real-time and provide actionable insights is fueling its adoption across various applications, thereby driving market growth.

Another significant growth driver is the increasing integration of machine learning with the Internet of Things (IoT). The convergence of these technologies enables the creation of smarter, more efficient systems that enhance operational performance and productivity. In manufacturing, for instance, IoT devices equipped with machine learning capabilities can predict equipment failures and optimize maintenance schedules, leading to reduced downtime and costs. Similarly, in the automotive industry, machine learning algorithms are employed in autonomous vehicles to process and analyze sensor data, improving navigation and safety. The synergistic relationship between machine learning and IoT is expected to further propel market expansion during the forecast period.

Moreover, the rising investments in AI research and development by both public and private sectors are accelerating the advancement and adoption of machine learning technologies. Governments worldwide are recognizing the potential of AI and machine learning to transform industries, leading to increased funding for research initiatives and innovation centers. Companies are also investing heavily in developing cutting-edge machine learning solutions to maintain a competitive edge. This robust investment landscape is fostering an environment conducive to technological breakthroughs, thereby contributing to the growth of the machine learning market.

Supervised Learning, a subset of machine learning, plays a crucial role in the advancement of AI-driven solutions. It involves training algorithms on a labeled dataset, allowing the model to learn and make predictions or decisions based on new, unseen data. This approach is particularly beneficial in applications where the desired output is known, such as in classification or regression tasks. For instance, in the healthcare sector, supervised learning algorithms are employed to analyze patient data and predict health outcomes, thereby enhancing diagnostic accuracy and treatment efficacy. Similarly, in finance, these algorithms are used for credit scoring and fraud detection, providing financial institutions with reliable tools for risk assessment. As the demand for precise and efficient AI applications grows, the significance of supervised learning in driving innovation and operational excellence across industries becomes increasingly evident.

From a regional perspective, North America holds a dominant position in the machine learning market due to the early adoption of advanced technologies and the presence of major technology companies. The region's strong focus on R&D and innovation, coupled with a well-established IT infrastructure, further supports market growth. In addition, Asia Pacific is emerging as a lucrative market for machine learning, driven by rapid industrialization, increasing digitalization, and government initiatives promoting AI adoption. The region is witnessing significant investments in AI technologies, particu

This dataset provides an in-depth view of any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental US. We map US facilities (including factories, warehouses, and retail outlets) to companies. With this dataset, it is possible to track the movement of trucks and devices between locations to identify supply chain connections. Machine learning algorithms ingest 7-15bn daily events to estimate the volume of goods transported between locations. Consequently, we can map supply chain connections between: •Different companies (expressed as a percentage of volume transported). •Locations owned by the same company (e.g. warehouse to shop). With this novel geolocation approach, it is possible to "draw" a knowledge graph of any private or public company´s relations with other companies within the country. This solution, in the form of a dataset, provides an in-depth view into any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental United States. Use cases: - Identification and understanding of relations company-to-company: It helps to identify and infer relationships and connections between specific companies or facilities and between sectors/industries. - Identification and understanding of relations place-to-place: A logistics and domestic distribution supply chain can be mapped, both nationwide and state-wide in the US, and across countries in Europe. - Visualization and mapping of an entire supply chain network. - Tracking of products in any distribution or supply chain. - Risk assessment - Correlation analysis. - Disruption analysis. - Analysis of illicit networks and tracking of illegal use of corporate assets. - Improvement of casualty risk management. - Optimization of supply chain risk management. - Security and compliance. - Identification of not only the first tier of suppliers in the value chain, but also 2nd and 3rd tier suppliers, and more. Current largest use case: global corporation using it to model risk at a facility level (+100,000 locations).

Context

Since i have started research in the field of data science, i have noticed there are lot of data sets available for NLP, medicine, images and other subjects but i could not find any single adequate data for the domain of software testing. The data sets which are hardly available are extracted from some piece of code or some historical data that too not available publicly to analyze. The domain of software testing and data science, especially machine learning has a lot of potential. While conducting research on testcase prioritization especially in initial stages of software test cycle the way companies set the priorities in software industry there is no black box data set available in that format. This was the reason that i wanted such data set to exist. So i collected the necessary attributes , arrange them against their values and make one.

Content

This data was gathered in [Aug, 2020], from a software company worked on a car financing lease company's whole software package from web to their management system. The dataset is in .csv format, there are 2000 rows and 6 columns in this data set. The detail of six attributes are as under: B_Req --> Business Requirement R_Prioirty --> Requirement Priority of particular business requirement FP --> Function point of each testing task, which in our case are test cases against each requirement under covers a particular FP Complexity --> Complexity of a particular function point or related modules(the description of assigning complexity is listed below in this section)* Time --> Estimated max time assigned to each Function Point of particular testing task by QA team lead or sr. SQA analyst Cost --> Calculated cost for each function point using complexity and time with function point estimation technique to calculates cost using the formula listed below: cost = “Cost = (Complexity * Time) * average amount set per task or per Function Point note: In this case it is set as 5$ per FP. The criteria for complexity is listed in .txt file attached with new version.

Acknowledgements

I would like to thank the persons from QA departments of different software companies. Especially team of the the company who provided me this estimation data and traceability matrix to extract data and compile these in to a dataset. I get a great help from the websites like www.softwaretestinghelp.com, www.coderus.com and many other sources which helps me to understand all the testing process and in which phases priorities are assigned usually.

Inspiration

My inspiration to collect this data is the shortage of dataset showing the priority of testcases with their requirements and estimated metrics to analyze the data while doing research in automation of testcase priority using machine learning. --> The dataset can be used to analyze and apply classification or any machine learning algorithm to prioritize testcases. --> Can be used reduce , select or automate testing based on priority, or cost and time or complexity and requirements. --> Can be used to build recommendation system problem related to software testing which helps software testing team to ease their task based estimation and recommendation.

This private company dataset provides an in-depth view of any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental US.

Also, using robust supply chain data you will be able to map US facilities (including factories, warehouses, and retail outlets).

With this private company dataset, it is possible to track the movement of trucks and devices between locations to identify supply chain connections and company data insights.

Our Machine learning algorithms ingest 7-15bn daily events to estimate the volume of goods transported between locations. Consequently, we can map supply chain connections between:

•Different companies (expressed as a percentage of volume transported).

•Locations owned by the same company (e.g. warehouse to shop).

With this novel geolocation approach, it is possible to "draw" a knowledge graph of any private or public company´s relations with other companies within the country.

This solution, in the form of a dataset, provides an in-depth view of any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental United States.

Use cases:

• Identification and understanding of relations company-to-company: It helps to identify and infer relationships and connections between specific companies or facilities and between sectors/industries.

• Identification and understanding of relations place-to-place: A logistics and domestic distribution supply chain can be mapped, both nationwide and state-wide in the US, and across countries in Europe.

• Visualization and mapping of an entire supply chain network.

• Tracking of products in any distribution or supply chain.

• Risk assessment

• Correlation analysis.

• Disruption analysis.

• Analysis of illicit networks and tracking of illegal use of corporate assets.

• Improvement of casualty risk management.

• Optimization of supply chain risk management.

• Security and compliance.

• Identification of not only the first tier of suppliers in the value chain, but also 2nd and 3rd tier suppliers, and more.

Current largest use case: global corporation using it to model risk at a facility level (+100,000 locations).

Why should you trust PREDIK Data-Driven? In 2023, we were listed as Datarade's top providers. Why? Our solutions for private company data, supply chain data, and B2B data adapt according to the specific needs of companies. Also, PREDIK methodology focuses on the client and the necessary elements for the success of their projects.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Types of Products : Clothing , Sports , and Electronic Supplies

Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.

Machine Learning (ML) Platforms Market Outlook

The global Machine Learning (ML) platforms market size was valued at approximately USD 15 billion in 2023 and is projected to reach around USD 120 billion by 2032, growing at a compound annual growth rate (CAGR) of 25.8% during the forecast period. This rapid expansion is primarily driven by the increasing adoption of artificial intelligence (AI) across various industries, the rising need for predictive analytics, and the growing demand for automated solutions.

The first major growth factor driving the ML platforms market is the widespread adoption of AI technologies in various sectors such as healthcare, finance, and retail. Organizations are increasingly recognizing the potential of machine learning to improve operational efficiency, deliver personalized customer experiences, and drive innovation. For instance, in healthcare, ML algorithms are being used to predict patient outcomes, optimize treatment plans, and enhance diagnostic accuracy. Similarly, in the financial sector, ML models are employed for fraud detection, risk management, and algorithmic trading. The versatility and wide-ranging applications of ML are compelling businesses to invest in robust ML platforms, thereby fueling market growth.

Another significant factor contributing to the market growth is the burgeoning volume of big data. The exponential increase in data generated from various sources such as social media, IoT devices, and business transactions necessitates advanced analytics tools capable of processing and analyzing massive datasets. Machine learning platforms offer the computational power and sophisticated algorithms required to extract valuable insights from big data. Companies are leveraging ML platforms to uncover hidden patterns, make data-driven decisions, and gain a competitive edge in the market. This data-driven approach is particularly beneficial for sectors like retail, where understanding customer behavior and preferences is crucial for business success.

The growing emphasis on automation and the need for efficient business processes is also propelling the market forward. Machine learning platforms enable organizations to automate repetitive tasks, streamline workflows, and enhance productivity. For instance, in manufacturing, ML algorithms are used for predictive maintenance, quality control, and supply chain optimization. By automating these processes, companies can reduce operational costs, minimize downtime, and improve overall efficiency. The ability of ML platforms to drive automation and enhance operational performance is encouraging businesses across various industries to adopt these technologies.

Machine Learning in Finance is revolutionizing the way financial institutions operate by enhancing their capabilities in areas such as fraud detection, risk management, and algorithmic trading. Financial markets are highly dynamic and data-driven, making them ideal for the application of machine learning algorithms. These algorithms can analyze vast amounts of financial data in real-time, identifying patterns and anomalies that may indicate fraudulent activities or potential risks. Furthermore, machine learning models are being used to optimize trading strategies, enabling financial firms to execute trades with greater precision and speed. The integration of machine learning in finance not only improves operational efficiency but also enhances decision-making processes, ultimately leading to better financial outcomes for both institutions and their clients.

Regionally, North America currently holds the largest share in the ML platforms market, owing to the presence of major technology companies, early adoption of advanced technologies, and significant investments in research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation, increasing adoption of AI and ML technologies, and supportive government initiatives in countries like China, India, and Japan are driving the market growth in this region. Additionally, Europe is also experiencing substantial growth, driven by the strong presence of automotive and manufacturing industries, which are increasingly integrating ML solutions into their operations.

Component Analysis

When analyzing the Machine Learning (ML) platforms market by component, it is essential to consider the two primary segments: software and services. The software segment includ

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

Machine Learning in Finance Market Outlook

The global machine learning in finance market size was valued at approximately $8.2 billion in 2023 and is projected to reach around $35.4 billion by 2032, growing at a robust CAGR of 18.1% from 2024 to 2032. This impressive growth trajectory underscores the increasing integration of machine learning technologies across various financial sectors, driven by the necessity for improved decision-making processes, enhanced customer satisfaction, and heightened operational efficiencies. The finance sector is under constant pressure to optimize and innovate, and machine learning provides a crucial toolset to address these demands by offering sophisticated algorithms and predictive analytics capabilities.

One significant growth factor in the machine learning in finance market is the ever-increasing volume and complexity of data generated in the financial services sector. Financial institutions rely on vast amounts of data to make informed decisions. Machine learning algorithms are adept at analyzing large datasets quickly and accurately, which allows financial institutions to extract actionable insights, identify patterns, and predict future trends. The technology's ability to enhance data-driven decision-making processes is a compelling driver for its adoption. Moreover, as financial markets become more interconnected and globalized, the ability to process and analyze data from multiple sources in real-time is becoming increasingly important, further fueling the demand for machine learning solutions.

In addition to data management, the rise in cyber threats and financial fraud has also accelerated the adoption of machine learning in finance. Financial institutions are constantly under threat from sophisticated cyber-attacks and fraudulent activities. Machine learning models can identify anomalies and detect fraud faster and more efficiently than traditional methods, thereby offering a more robust security framework. These solutions can learn from historical fraud patterns and adapt to new threats, providing an evolving defense mechanism. As the cost of financial fraud and cyber-attacks continues to rise, so does the need for advanced machine learning solutions capable of mitigating such risks.

The demand for personalized financial services is another pivotal growth factor for machine learning in the financial market. Today's consumers expect personalized, real-time services tailored to their specific needs and preferences. Machine learning can analyze customer behavior, transaction history, and preferences to provide tailored financial advice, product recommendations, and customer service. This personalization not only enhances customer satisfaction and loyalty but also enables financial institutions to differentiate themselves in a competitive market. The ability to offer individualized services is becoming a crucial competitive advantage, prompting more institutions to integrate machine learning into their operations.

Regionally, North America is expected to remain a dominant player in the machine learning in finance market, driven by the presence of major financial institutions and early technology adopters. The region's advanced technological infrastructure and regulatory environment are conducive to the integration of machine learning technologies. Furthermore, the Asia Pacific region is anticipated to experience the highest growth rate, fueled by rapid digitalization and the proliferation of fintech companies. Governments in countries such as China and India are also promoting the use of artificial intelligence and machine learning, further accelerating market growth. Europe's well-established financial sector and strong emphasis on data privacy and security also make it a significant market, while emerging economies in the Middle East & Africa are beginning to explore the potential of machine learning in finance.

Component Analysis

The component segment of the machine learning in finance market can be broadly categorized into software, hardware, and services. Software solutions form the backbone of machine learning applications in finance, encompassing a variety of platforms, algorithms, and tools utilized to analyze financial data and generate insights. These software solutions are crucial for developing predictive models, automating processes, and enhancing decision-making capabilities. As financial institutions increasingly adopt digital transformation initiatives, the demand for sophisticated machine learning software solutions is expected to grow significantly. Companies are investing heavily in developing advanced analytics platforms

The AI training data market is experiencing robust growth, driven by the escalating demand for advanced AI applications across diverse sectors. The market's expansion is fueled by the increasing adoption of machine learning (ML) and deep learning (DL) algorithms, which require vast quantities of high-quality data for effective training. Key application areas like autonomous vehicles, healthcare diagnostics, and personalized recommendations are significantly contributing to market expansion. The market is segmented by application (IT, Automotive, Government, Healthcare, BFSI, Retail & E-commerce, Others) and data type (Text, Image/Video, Audio). While North America currently holds a dominant market share due to the presence of major technology companies and robust research & development activities, the Asia-Pacific region is projected to witness the fastest growth rate in the coming years, propelled by rapid digitalization and increasing investments in AI infrastructure across countries like China and India. The competitive landscape is characterized by a mix of established technology giants and specialized data annotation companies, each vying for market dominance through innovative data solutions and strategic partnerships. Significant restraints include the high cost of data acquisition and annotation, concerns about data privacy and security, and the need for specialized expertise in data management and labeling. However, advancements in automated data annotation tools and the emergence of synthetic data generation techniques are expected to mitigate some of these challenges. The forecast period of 2025-2033 suggests a continued upward trajectory for the market, driven by factors such as increasing investment in AI research, expanding adoption of cloud-based AI platforms, and the growing need for personalized and intelligent services across numerous industries. While precise figures for market size and CAGR are unavailable, a conservative estimate, considering industry trends and recent reports on similar markets, would project a substantial compound annual growth rate (CAGR) of around 20% from 2025, resulting in a market value exceeding $50 billion by 2033.

Analysis of ‘JB Link Telco Customer Churn’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnflag/jb-link-telco-customer-churn on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This is a customized version of the widely known IBM Telco Customer Churn dataset. I've added a few more columns and modified others in order to make it a little more realistic.

My customizations are based on the following version: Telco customer churn (11.1.3+)

Below you may find a fictional business problem I created. You may use it in order to start developing something around this dataset.

JB Link Customer Churn Problem

JB Link is a small size telecom company located in the state of California that provides Phone and Internet services to customers on more than a 1,000 cities and 1,600 zip codes.

The company is in the market for just 6 years and has quickly grown by investing on infrastructure to bring internet and phone networks to regions that had poor or no coverage.

The company also has a very skilled sales team that is always performing well on attracting new customers. The number of new customers acquired in the past quarter represent 15% over the total.

However, by the end of this same period, only 43% of this customers stayed with the company and most of them decided on not renewing their contracts after a few months, meaning the customer churn rate is very high and the company is now facing a big challenge on retaining its customers.

The total customer churn rate last quarter was around 27%, resulting in a decrease of almost 12% in the total number of customers.

The executive leadership of JB Link is aware that some competitors are investing on new technologies and on the expansion of their network coverage and they believe this is one of the main drivers of the high customer churn rate.

Therefore, as an action plan, they have decided to created a task force inside the company that will be responsible to work on a customer retention strategy.

The task force will involve members from different areas of the company, including Sales, Finance, Marketing, Customer Service, Tech Support and a recent formed Data Science team.

The data science team will play a key role on this process and was assigned some very important tasks that will support on the decisions and actions the other teams will be taking : - Gather insights from the data to understand what is driving the high customer churn rate. - Develop a Machine Learning model that can accurately predict the customers that are more likely to churn. - Prescribe customized actions that could be taken in order to retain each of those customers.

The Data Science team was given a dataset with a random sample of 7,043 customers that can help on achieving this task.

The executives are aware that the cost of acquiring a new customer can be up to five times higher than the cost of retaining a customer, so they are expecting that the results of this project will save a lot of money to the company and make it start growing again.

--- Original source retains full ownership of the source dataset ---

As per Cognitive Market Research's latest published report, the Global Data Collection and Labeling market size was USD 2.41 Billion in 2022 and it is forecasted to reach USD 18.60 Billion by 2030. Data Collection and Labeling Industry's Compound Annual Growth Rate will be 29.1% from 2023 to 2030. What are the key driving factors for the Data Collection and Labeling Market?

As machine learning and artificial intelligence become more prevalent, the demand for high-quality training data is increasing. This is because algorithms need accurate and well-labeled data to learn and make accurate predictions. This factor is accelerating the growth of the Data Collection and Labeling Market. Moreover, the advancement in technology is one of the major factors contributing to the market growth. Technological advancements have made data collection and labeling more efficient and accurate. For example, computer vision algorithms can now label images and videos automatically, reducing the need for manual labeling. Similarly, the growing need for data in various industries and data collection and labeling is critical in industries such as healthcare, finance, retail, and automotive. As these industries become more data-driven, the need for accurate and well-labeled data is increasing, which is driving the market’s growth.

Growing use of AI and machine learning is creating demand for high-quality labelled data sets across sectors.

High-quality labelled data sets across sectors are needed due to growing use of AI and machine learning. More companies are now seeking to train AI models to do things like autonomous cars, medical diagnosis or natural language processing, and data annotation is getting in the way. Automated and AI-based data labelling technologies have streamlined the process, which in turn has minimized manual labelling cost and time. Concurrently, the accelerated expansion of e-commerce, social media, and customer analytics industries is also fueling an unquenchable thirst for copious amounts of labelled data. Cloud-based platforms enabled organizations to embrace scalable solutions for real-time data labelling, which will support faster market growth.

Key Restraint of Market.

Data privacy laws, high expense, and inefficient manual labelling can restraint the market.

While it is slowly being adopted, we are inevitably going to encounter non-trivial issues with data collection, data labelling, data privacy, data security, and compliance. Laws such as GDPR and CCPA have a genuine effect on what you can do with user data, and the amount of usable high-quality datasets available is few and far between. While manual tagging has proven to be time-consuming and error-filled, reducing accuracy and scalability. High costs of skilled annotators and advanced AI-powered tagging technologies may be unaffordable for small-to-mid-sized entities. Bias data and its impact on the AI decision-making process is another ethical problem that significantly holds back the digital workforce, which compels entities to follow transparent data labelling practices properly, according to the information they want.

Key Opportunity of Market.

AI-powered automation and self-supervised learning improve scalability and precision in data labeling.

The increasing penetration of AI-powered automation in data labeling, along with the vast scale, provides profitable growth opportunities in the market. The latency will decrease, and the costs will be less due to the integration of AI-powered annotation tools with a human-in-the-loop model that offers a trade-off between the accuracy and costs. Self-supervised and semi-supervised learning expands the potential of an AI model to tag data with minimal or no human intervention but offers robust scalability. New uses in healthcare, robotics, and autonomous systems open up new use cases by the day. Additionally, increased growth in edge computing and IoT devices organically generates large amounts of unstructured data, providing a pathway for AI-based data-labeling solutions to help improve real-time processing and analysis. What is Data Collection and Labeling?

Data collection and labeling is the process of gathering and organizing data and adding metadata to it for better analysis and understanding. This process is critical in machine learning and artificial intelligence, as it provides the found...

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

Analysis of ‘Resume Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gauravduttakiit/resume-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Companies often receive thousands of resumes for each job posting and employ dedicated screening officers to screen qualified candidates.

Hiring the right talent is a challenge for all businesses. This challenge is magnified by the high volume of applicants if the business is labour-intensive, growing, and facing high attrition rates.

IT departments are short of growing markets. In a typical service organization, professionals with a variety of technical skills and business domain expertise are hired and assigned to projects to resolve customer issues. This task of selecting the best talent among many others is known as Resume Screening.

Typically, large companies do not have enough time to open each CV, so they use machine learning algorithms for the Resume Screening task.

--- Original source retains full ownership of the source dataset ---

CompanyKG is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.

Edges: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.

Nodes: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "Klarna is a fintech company that provides support for direct and post-purchase payments ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: mSBERT (multilingual Sentence BERT), ADA2, SimCSE (fine-tuned on the raw company descriptions) and PAUSE.

Evaluation Tasks. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:

• Similarity Prediction (SP). To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.
• Competitor Retrieval (CR). Each sample contains one target company and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with N direct competitors in this CR evaluation set, we expect a competent method to retrieve all N competitors when searching for similar companies to A.
• Similarity Ranking (SR) is designed to assess the ability of any method to rank candidate companies (numbered 0 and 1) based on their similarity to a query company. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development.
• Edge Prediction (EP) evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).

Background and Motivation

In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as company similarity quantification, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.

While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment.

In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.

However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a Knowledge Graph (KG). Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.

Source Code and Tutorial:
https://github.com/llcresearch/CompanyKG2

Paper: to be published

The industry has seen surging growth in recent years. Strong AI investments in the mid- to late 2010s saw a raft of new companies enter the industry. Many of these companies have now entered commerciality and begun generating meaningful revenue. ChatGPT’s public release has also supported the industry, pushing AI’s capabilities into the public consciousness and encouraging companies to actively explore how they can integrate AI into their operations. Overall, industry revenue is expected to grow an annualised 15.6% over the five years through 2024-25, to reach $3.4 billion. Negative or extremely thin margins over the past decade have largely been a symptom of success. Strong investment growth in the 2010s drove up enterprise numbers, which led to average industry margins declining rapidly. AI firms have long development cycles and often take years to become commercial, relying largely on investment funding to support their operations. A glut of new companies has led to negative or extremely weak margins since 2013-14, but margins are set to start improving in 2024-25 as more AI companies enter the commercial phase of their development The industry’s demand base is expanding, driven by AI products’ increased accessibility and the excitement stoked by ChatGPT’s launch. Rapid AI technology advancements have also improved AI products’ functionality and applicability, creating a rapidly expanding total addressable market. These factors are forecast to support strong growth over the coming years, but a high interest rate environment, elevated inflation and economic uncertainty are projected to partially offset this growth. These economic headwinds may slow the investment funding that Australia’s AI industry is highly reliant on. Overall, industry revenue is projected to grow at an annualised 13.1% through the end of 2029-30, to reach $6.3 billion.