Data Modeling Software Market Outlook

The global data modeling software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach around USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032. The market's robust growth can be attributed to the increasing adoption of data-driven decision-making processes across various industries, which necessitates advanced data modeling solutions to manage and analyze large volumes of data efficiently.

The proliferation of big data and the growing need for data governance are significant drivers for the data modeling software market. Organizations are increasingly recognizing the importance of structured and unstructured data in generating valuable insights. With data volumes exploding, data modeling software becomes essential for creating logical data models that represent business processes and information requirements accurately. This software is crucial for implementation in data warehouses, analytics, and business intelligence applications, further fueling market growth.

Technological advancements, particularly in artificial intelligence (AI) and machine learning (ML), are also propelling the data modeling software market forward. These technologies enable more sophisticated data models that can predict trends, optimize operations, and enhance decision-making processes. The integration of AI and ML with data modeling tools allows for automated data analysis, reducing the time and effort required for manual processes and improving the accuracy of the results. This technological synergy is a significant growth factor for the market.

The rise of cloud-based solutions is another critical factor contributing to the market's expansion. Cloud deployment offers numerous advantages, such as scalability, flexibility, and cost-effectiveness, making it an attractive option for businesses of all sizes. Cloud-based data modeling software allows for real-time collaboration and access to data from anywhere, enhancing productivity and efficiency. As more companies move their operations to the cloud, the demand for cloud-compatible data modeling solutions is expected to surge, driving market growth further.

In terms of regional outlook, North America currently holds the largest share of the data modeling software market. This dominance is due to the high concentration of technology-driven enterprises and a strong emphasis on data analytics and business intelligence in the region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Rapid digital transformation, increased cloud adoption, and the rising importance of data analytics in emerging economies like China and India are key factors contributing to this growth. Europe, Latin America, and the Middle East & Africa also present significant opportunities, albeit at varying growth rates.

Component Analysis

In the data modeling software market, the component segment is divided into software and services. The software component is the most significant contributor to the market, driven by the increasing need for advanced data modeling tools that can handle complex data structures and provide accurate insights. Data modeling software includes various tools and platforms that facilitate the creation, management, and optimization of data models. These tools are essential for database design, data architecture, and other data management tasks, making them indispensable for organizations aiming to leverage their data assets effectively.

Within the software segment, there is a growing trend towards integrating AI and ML capabilities to enhance the functionality of data modeling tools. This integration allows for more sophisticated data analysis, automated model generation, and improved accuracy in predictions and insights. As a result, organizations can achieve better data governance, streamline operations, and make more informed decisions. The demand for such advanced software solutions is expected to rise, contributing significantly to the market's growth.

The services component, although smaller in comparison to the software segment, plays a crucial role in the data modeling software market. Services include consulting, implementation, training, and support, which are essential for the successful deployment and utilization of data modeling tools. Many organizations lack the in-house expertise to effectively implement and manage data modeling software, leading to increased demand for professional services.

The Large-Scale AI Models database documents over 200 models trained with more than 10²³ floating point operations, at the leading edge of scale and capabilities.

Data Modeling Tool Market Outlook

The global data modeling tool market size was valued at USD 1.2 billion in 2023 and is expected to reach approximately USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% from 2024 to 2032. The growth of the data modeling tool market is driven by the increasing need for precise data management and analytics to bolster data-driven decision-making across various industries. The widespread adoption of cloud computing and the proliferation of data across organizations are pivotal in driving this market forward.

One of the primary factors fueling the growth of the data modeling tool market is the accelerating digital transformation across industries. As businesses increasingly rely on data to drive their operations and strategic decisions, the need for robust data modeling tools that can efficiently manage and analyze large volumes of data becomes paramount. Furthermore, the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data modeling tools enhances their functionalities, thereby providing more accurate and insightful data analytics, which drives market demand.

Another significant growth factor is the rising adoption of cloud-based solutions. Cloud-based data modeling tools offer several advantages over traditional on-premises solutions, including scalability, cost-effectiveness, and ease of access. These tools enable organizations to manage and analyze data from multiple sources in real-time, facilitating faster and more informed decision-making. The increasing preference for cloud-based solutions is expected to drive substantial growth in the data modeling tool market over the forecast period.

Additionally, the growing focus on regulatory compliance and data governance is contributing to the market's expansion. With the introduction of stringent data protection regulations such as GDPR and CCPA, organizations are compelled to adopt data modeling tools to ensure compliance and mitigate risks associated with data breaches and non-compliance. These tools assist in creating transparent and auditable data processes, which are critical for regulatory adherence, further boosting their adoption across various sectors.

Regionally, North America holds a significant share of the data modeling tool market, driven by the presence of a large number of technology giants and early adopters of advanced data management solutions. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, attributable to the rapid digitalization and increasing investments in IT infrastructure in emerging economies such as China and India. The growing awareness about the benefits of data modeling tools among businesses in this region is likely to propel market growth significantly.

In the context of the growing need for efficient data management, the role of a Data Catalog becomes increasingly significant. A Data Catalog serves as a comprehensive inventory of data assets within an organization, enabling users to discover, understand, and manage their data more effectively. By providing metadata about data sources, it facilitates data governance and compliance, ensuring that data is used responsibly and ethically. As organizations grapple with vast amounts of data, a well-implemented Data Catalog can streamline data access and enhance collaboration across departments, ultimately driving more informed decision-making.

Component Analysis

The data modeling tool market is segmented by component into software and services. The software segment holds the largest market share, driven by the increasing need for sophisticated data modeling solutions that can handle complex data structures and provide actionable insights. Software tools are essential for creating, managing, and analyzing data models, enabling organizations to streamline their data processes and improve operational efficiency. As businesses continue to generate vast amounts of data, the demand for advanced data modeling software is expected to surge.

Services form a crucial segment of the data modeling tool market, encompassing a range of offerings such as consulting, integration, support, and maintenance. As organizations adopt data modeling tools, they often require expert guidance to customize and integrate these tools into their existing systems. Additionally, ongoing support and maintenance services are essential to ensure

It is difficult to handle the extraordinary data volume generated in many fields with current computational resources and techniques. This is very challenging when applying conventional statistical methods to big data. A common approach is to partition full data into smaller subdata for purposes such as training, testing, and validation. The primary purpose of training data is to represent the full data. To achieve this goal, the selection of training subdata becomes pivotal in retaining essential characteristics of the full data. Recently, several procedures have been proposed to select “optimal design points” as training subdata under pre-specified models, such as linear regression and logistic regression. However, these subdata will not be “optimal” if the assumed model is not appropriate. Furthermore, such subdata cannot be useful to build alternative models because it is not an appropriate representative sample of the full data. In this article, we propose a novel algorithm for better model building and prediction via a process of selecting a “good” training sample. The proposed subdata can retain most characteristics of the original big data. It is also more robust that one can fit various response model and select the optimal model. Supplementary materials for this article are available online.

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the emph{model} of the system. Since the state of the system is constantly changing, it is necessary to keep the models up-to-date. Computing global data mining models e.g. decision trees, k-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient emph{local} algorithm which can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its k-means clustering. The theoretical claims are corroborated with a thorough experimental analysis.

Efficient-Large-Model/RULER-data dataset hosted on Hugging Face and contributed by the HF Datasets community

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large-scale Bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank Bayesian VAR of Geweke (1996). We find that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast and for key variables such as industrial production growth, inflation, and the federal funds rate. The robustness of this finding is confirmed by a Monte Carlo experiment based on bootstrapped data. We also provide a consistency result for the reduced rank regression valid when the dimension of the system tends to infinity, which opens the way to using large-scale reduced rank models for empirical analysis.

Lucror Analytics: Fundamental Fixed Income Data and Financial Models for High-Yield Bond Issuers

At Lucror Analytics, we deliver expertly curated data solutions focused on corporate credit and high-yield bond issuers across Europe, Asia, and Latin America. Our data offerings integrate comprehensive fundamental analysis, financial models, and analyst-adjusted insights tailored to support professionals in the credit and fixed-income sectors. Covering 400+ bond issuers, our datasets provide a high level of granularity, empowering asset managers, institutional investors, and financial analysts to make informed decisions with confidence.

By combining proprietary financial models with expert analysis, we ensure our Fixed Income Data is actionable, precise, and relevant. Whether you're conducting credit risk assessments, building portfolios, or identifying investment opportunities, Lucror Analytics offers the tools you need to navigate the complexities of high-yield markets.

What Makes Lucror’s Fixed Income Data Unique?

Comprehensive Fundamental Analysis Our datasets focus on issuer-level credit data for complex high-yield bond issuers. Through rigorous fundamental analysis, we provide deep insights into financial performance, credit quality, and key operational metrics. This approach equips users with the critical information needed to assess risk and uncover opportunities in volatile markets.

Analyst-Adjusted Insights Our data isn’t just raw numbers—it’s refined through the expertise of seasoned credit analysts with 14 years average fixed income experience. Each dataset is carefully reviewed and adjusted to reflect real-world conditions, providing clients with actionable intelligence that goes beyond automated outputs.

Focus on High-Yield Markets Lucror’s specialization in high-yield markets across Europe, Asia, and Latin America allows us to offer a targeted and detailed dataset. This focus ensures that our clients gain unparalleled insights into some of the most dynamic and complex credit markets globally.

How Is the Data Sourced? Lucror Analytics employs a robust and transparent methodology to source, refine, and deliver high-quality data:

• Public Sources: Includes issuer filings, bond prospectuses, financial reports, and market data.
• Proprietary Analysis: Leveraging proprietary models, our team enriches raw data to provide actionable insights.
• Expert Review: Data is validated and adjusted by experienced analysts to ensure accuracy and relevance.
• Regular Updates: Models are continuously updated to reflect market movements, regulatory changes, and issuer-specific developments.

This rigorous process ensures that our data is both reliable and actionable, enabling clients to base their decisions on solid foundations.

Primary Use Cases 1. Fundamental Research Institutional investors and analysts rely on our data to conduct deep-dive research into specific issuers and sectors. The combination of raw data, adjusted insights, and financial models provides a comprehensive foundation for decision-making.

1. Credit Risk Assessment Lucror’s financial models provide detailed credit risk evaluations, enabling investors to identify potential vulnerabilities and mitigate exposure. Analyst-adjusted insights offer a nuanced understanding of creditworthiness, making it easier to distinguish between similar issuers.

2. Portfolio Management Lucror’s datasets support the development of diversified, high-performing portfolios. By combining issuer-level data with robust financial models, asset managers can balance risk and return while staying aligned with investment mandates.

3. Strategic Decision-Making From assessing market trends to evaluating individual issuers, Lucror’s data empowers organizations to make informed, strategic decisions. The regional focus on Europe, Asia, and Latin America offers unique insights into high-growth and high-risk markets.

Key Features of Lucror’s Data - 400+ High-Yield Bond Issuers: Coverage across Europe, Asia, and Latin America ensures relevance in key regions. - Proprietary Financial Models: Created by one of the best independent analyst teams on the street. - Analyst-Adjusted Data: Insights refined by experts to reflect off-balance sheet items and idiosyncrasies. - Customizable Delivery: Data is provided in formats and frequencies tailored to the needs of individual clients.

Why Choose Lucror Analytics? Lucror Analytics and independent provider free from conflicts of interest. We are committed to delivering high-quality financial models for credit and fixed-income professionals. Our proprietary approach combines proprietary models with expert insights, ensuring accuracy, relevance, and utility.

By partnering with Lucror Analytics, you can: - Safe costs and create internal efficiencies by outsourcing a highly involved and time-consuming processes, including financial analysis and modelling. - Enhance your credit risk ...

AI Large Language Model Market Outlook

The AI Large Language Model market size is projected to grow from USD 12.1 billion in 2023 to USD 84.3 billion by 2032, at a compound annual growth rate (CAGR) of 24.5% over the forecast period. This growth is driven by the increasing adoption of advanced AI technologies across various industries to enhance operational efficiency, customer experience, and decision-making processes.

A key driver of this market growth is the exponential increase in data generation and the need for advanced data processing capabilities. Large language models, such as GPT-3 and its successors, have demonstrated remarkable proficiency in understanding and generating human-like text, making them indispensable tools for applications requiring natural language understanding and generation. The ability of these models to perform a wide range of tasks—ranging from customer support to content creation and beyond—has significantly expanded their appeal and utility in the business world.

Another significant factor contributing to the market's growth is the surging investments in AI and machine learning by both public and private sectors. Governments worldwide are recognizing the strategic importance of AI technologies and are launching various initiatives to support AI research and development. Concurrently, private companies are investing heavily in AI to gain a competitive edge, which is boosting the demand for large language models. Furthermore, advancements in computational power and cloud computing are facilitating the seamless deployment and scaling of these models, thereby driving market growth.

The increasing demand for personalized customer experiences is also propelling the adoption of AI large language models. Businesses are leveraging these models to offer customized interactions and recommendations, thereby improving customer satisfaction and loyalty. For instance, in the retail and e-commerce sectors, large language models are being used to provide personalized shopping experiences by understanding customer preferences and behavior. Similarly, in the healthcare sector, these models are assisting in providing personalized treatment plans and improving patient outcomes.

Regionally, North America holds a significant share of the AI large language model market, driven by robust technological infrastructure, high adoption rates of advanced technologies, and substantial investments in AI research. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digitalization, increasing internet penetration, and supportive government initiatives. Europe also represents a strong market due to its focus on technological innovation and stringent data protection regulations, which drive the demand for advanced AI solutions.

Component Analysis

The AI large language model market is segmented into components such as software, hardware, and services. The software component is expected to dominate the market, driven by continuous advancements in AI algorithms and the growing need for sophisticated AI applications across various industries. The software segment includes natural language processing (NLP) tools, machine learning frameworks, and AI development platforms that enable the creation and deployment of large language models. These tools have become essential in developing applications that require text generation, translation, summarization, and other language-related tasks.

The hardware component is also witnessing significant growth, primarily due to the increasing demand for high-performance computing (HPC) systems and specialized processors such as GPUs and TPUs. These hardware solutions are crucial for training large language models, which require immense computational power. Companies are investing in advanced hardware to accelerate the training process and improve the efficiency of AI models. With the rise of AI-driven applications, the demand for scalable and efficient hardware solutions is expected to grow, further driving the hardware segment's expansion.

Services form another critical component of the AI large language model market, encompassing consulting, integration, and support services. As businesses increasingly adopt AI technologies, there is a growing need for specialized services to ensure successful implementation and integration of large language models into existing systems. Service providers offer expertise in AI strategy development, model training, deployment, and maintenance, helping organizations maximize the

One of the large language models trained in this paper: https://arxiv.org/abs/1810.10045

Our most comprehensive database of AI models, containing over 800 models that are state of the art, highly cited, or otherwise historically notable. It tracks key factors driving machine learning progress and includes over 300 training compute estimates.

Software Model simulations were conducted using WRF version 3.8.1 (available at https://github.com/NCAR/WRFV3) and CMAQ version 5.2.1 (available at https://github.com/USEPA/CMAQ). The meteorological and concentration fields created using these models are too large to archive on ScienceHub, approximately 1 TB, and are archived on EPA’s high performance computing archival system (ASM) at /asm/MOD3APP/pcc/02.NOAH.v.CLM.v.PX/. Figures Figures 1 – 6 and Figure 8: Created using the NCAR Command Language (NCL) scripts (https://www.ncl.ucar.edu/get_started.shtml). NCLD code can be downloaded from the NCAR website (https://www.ncl.ucar.edu/Download/) at no cost. The data used for these figures are archived on EPA’s ASM system and are available upon request. Figures 7, 8b-c, 8e-f, 8h-i, and 9 were created using the AMET utility developed by U.S. EPA/ORD. AMET can be freely downloaded and used at https://github.com/USEPA/AMET. The modeled data paired in space and time provided in this archive can be used to recreate these figures. The data contained in the compressed zip files are organized in comma delimited files with descriptive headers or space delimited files that match tabular data in the manuscript. The data dictionary provides additional information about the files and their contents. This dataset is associated with the following publication: Campbell, P., J. Bash, and T. Spero. Updates to the Noah Land Surface Model in WRF‐CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(1): 231-256, (2019).

language: en

license: mit

GPT-2 Large

Table of Contents

• Model Details
• How To Get Started With the Model
• Uses
• Risks, Limitations and Biases
• Training
• Evaluation
• Environmental Impact
• Technical Specifications
• Citation Information
• Model Card Authors

Model Details

Model Description: GPT-2 Large is the 774M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.

• Developed by: OpenAI, see associated research paper and GitHub repo for model developers.
• Model Type: Transformer-based language model
• Language(s): English
• License: Modified MIT License
• Related Models: GPT-2, GPT-Medium and GPT-XL
• Resources for more information:
  • Research Paper
  • OpenAI Blog Post
  • GitHub Repo
  • OpenAI Model Card for GPT-2
  • Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large

How to Get Started with the Model

Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:

>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

[{'generated_text': "Hello, I'm a language model, I can do language modeling. In fact, this is one of the reasons I use languages. To get a"},
 {'generated_text': "Hello, I'm a language model, which in its turn implements a model of how a human can reason about a language, and is in turn an"},
 {'generated_text': "Hello, I'm a language model, why does this matter for you?

When I hear new languages, I tend to start thinking in terms"},
 {'generated_text': "Hello, I'm a language model, a functional language...

I don't need to know anything else. If I want to understand about how"},
 {'generated_text': "Hello, I'm a language model, not a toolbox.

In a nutshell, a language model is a set of attributes that define how"}]

Here is how to use this model to get the features of a given text in PyTorch:

from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = GPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

and in TensorFlow:

from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = TFGPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

Uses

Direct Use

In their model card about GPT-2, OpenAI wrote:

The primary intended users of these models are AI researchers and practitioners.

We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models.

Downstream Use

In their model card about GPT-2, OpenAI wrote:

Here are some secondary use cases we believe are likely:

• Writing assistance: Grammar assistance, autocompletion (for normal prose or code)
• Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.
• Entertainment: Creation of games, chat bots, and amusing generations.

Misuse and Out-of-scope Use

In their model card about GPT-2, OpenAI wrote:

Because large-scale language models like GPT-2 ...

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee). MatSci-Instruct helps alleviate the scarcity of relevant, high-quality materials science textual data available in the open literature, and HoneyBee is the first billion-parameter language model specialized to materials science. In MatSci-Instruct we improve the trustworthiness of generated data by prompting multiple commercially available large language models for generation with an Instructor module (e.g. Chat-GPT) and verification from an independent Verifier module (e.g. Claude). Using MatSci-Instruct, we construct a dataset of multiple tasks and measure the quality of our dataset along multiple dimensions, including accuracy against known facts, relevance to materials science, as well as completeness and reasonableness of the data. Moreover, we iteratively generate more targeted instructions and instruction-data in a finetuning-evaluation-feedback loop leading to progressively better performance for our finetuned HoneyBee models. Our evaluation on the MatSci-NLP benchmark shows HoneyBee's outperformance of existing language models on materials science tasks and iterative improvement in successive stages of instruction-data refinement. We study the quality of HoneyBee's language modeling through automatic evaluation and analyze case studies to further understand the model's capabilities and limitations. Our code and relevant datasets are publicly available at https://github.com/BangLab-UdeM-Mila/NLP4MatSci-HoneyBee.

This HydroShare resource was created to support the study presented in Choi et al. (2024), titled "Toward Reproducible and Interoperable Environmental Modeling: Integration of HydroShare with Server-side Methods for Exposing Large-Extent Spatial Datasets to Models." Ensuring the reproducibility of scientific studies is crucial for advancing research, with effective data management serving as a cornerstone for achieving this goal. In hydrologic and environmental modeling, spatial data is used as model input, and sharing this spatial data is a main step in the data management process. However, by focusing only on sharing data at the file level through small files rather than providing the ability to Find, Access, Interoperate with, and directly Reuse subsets of larger datasets, online data repositories have missed an opportunity to foster more reproducible science. This has led to challenges when accommodating large files that benefit from consistent data quality and seamless geographic extent.

To utilize the benefits of large datasets, the objective of the Choi et al. (2024) study was to create and test an approach for exposing large extent spatial (LES) datasets to support catchment-scale hydrologic modeling needs. GeoServer and THREDDS Data Server connected to HydroShare were used to provide seamless access to LES datasets. The approach was demonstrated using the Regional Hydro-Ecologic Simulation System (RHESSys) for three different-sized watersheds in the US. Data consistency was assessed across three different data acquisition approaches: the 'conventional' approach, which involved sharing data at the file level through small files, as well as GeoServer and THREDDS Data Server. This assessment was conducted using RHESSys to evaluate differences in model streamflow output. This approach provided an opportunity to serve datasets needed to create catchment models in a consistent way that could be accessed and processed to serve individual modeling needs. For full details on the methods and approach, please refer to Choi et al. (2024). This HydroShare resource is essential for accessing the data and workflows that were integral to the study.

This collection resource (HS 1) comprises 7 individual HydroShare resources (HS 2-8), each containing different datasets or workflows. These 7 HydroShare resources consist of the following: three resources for three state-scale LES datasets (HS 2-4), one resource with Jupyter notebooks for three different approaches and three different watersheds (HS 5), one resource for RHESSys model instances (i.e., input) of the conventional approach and observation data for all data access approaches in three different watersheds (HS 6), one resource with Jupyter notebooks for automated workflows to create LES datasets (HS 7), and finally one resource with Jupyter notebooks for the evaluation of data consistency (HS 8). More information on each resource is provided within it.

Snapshot img

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

Species and environmental dataThis compiled (zip) file consists of 7 matrices of data: one species data matrix, with abundance observations per visited plot; and 6 environmental data matrices, consisting of land cover classification (Class), simulated EnMAP and Landsat data (April and August), and a 6 time-step Landsat time series (January, March, May, June, July and September). All data is compiled to the 125m radius plots, as described in the paper.Leitaoetal_Mapping beta diversity from space_Data.zip,1. Spatial patterns of community composition turnover (beta diversity) may be mapped through Generalised Dissimilarity Modelling (GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional datasets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing datasets, including a time series of Landsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformed GDM models when fit on high-dimensional datasets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single date multispectral imagery. 4. This approach improved the direct use of high-dimensional remote sensing data, such as time series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional datasets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity.

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Big Data Market Size 2025-2029

The big data market size is forecast to increase by USD 193.2 billion at a CAGR of 13.3% between 2024 and 2029.

The market is experiencing a significant rise due to the increasing volume of data being generated across industries. This data deluge is driving the need for advanced analytics and processing capabilities to gain valuable insights and make informed business decisions. A notable trend in this market is the rising adoption of blockchain solutions to enhance big data implementation. Blockchain's decentralized and secure nature offers an effective solution to address data security concerns, a growing challenge in the market. However, the increasing adoption of big data also brings forth new challenges. Data security issues persist as organizations grapple with protecting sensitive information from cyber threats and data breaches.
Companies must navigate these challenges by investing in robust security measures and implementing best practices to mitigate risks and maintain trust with their customers. To capitalize on the market opportunities and stay competitive, businesses must focus on harnessing the power of big data while addressing these challenges effectively. Deep learning frameworks and machine learning algorithms are transforming data science, from data literacy assessments to computer vision models.

What will be the Size of the Big Data Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In today's data-driven business landscape, the demand for advanced data management solutions continues to grow. Companies are investing in business intelligence dashboards and data analytics tools to gain insights from their data and make informed decisions. However, with this increased reliance on data comes the need for robust data governance policies and regular data compliance audits. Data visualization software enables businesses to effectively communicate complex data insights, while data engineering ensures data is accessible and processed in real-time. Data-driven product development and data architecture are essential for creating agile and responsive business strategies. Data management encompasses data accessibility standards, data privacy policies, and data quality metrics.
Data usability guidelines, prescriptive modeling, and predictive modeling are critical for deriving actionable insights from data. Data integrity checks and data agility assessments are crucial components of a data-driven business strategy. As data becomes an increasingly valuable asset, businesses must prioritize data security and privacy. Prescriptive and predictive modeling, data-driven marketing, and data culture surveys are key trends shaping the future of data-driven businesses. Data engineering, data management, and data accessibility standards are interconnected, with data privacy policies and data compliance audits ensuring regulatory compliance.
Data engineering and data architecture are crucial for ensuring data accessibility and enabling real-time data processing. The data market is dynamic and evolving, with businesses increasingly relying on data to drive growth and inform decision-making. Data engineering, data management, and data analytics tools are essential components of a data-driven business strategy, with trends such as data privacy, data security, and data storytelling shaping the future of data-driven businesses.

How is this Big Data Industry segmented?

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

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


End-user

  BFSI
  Healthcare
  Retail and e-commerce
  IT and telecom
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the realm of big data, on-premise and cloud-based deployment models cater to varying business needs. On-premise deployment allows for complete control over hardware and software, making it an attractive option for some organizations. However, this model comes with a significant upfront investment and ongoing maintenance costs. In contrast, cloud-based deployment offers flexibility and scalability, with service providers handling infrastructure and maintenance. Yet, it introduces potential security risks, as data is accessed through multiple points and stored on external servers. Data