Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data

Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and high-quality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-the-art human pose estimation and 3D compression methods. For the former, we apply 2D and 3D pose estimation algorithms both on single- and multi-view data cues. For the latter, we benchmark open-source 3D codecs on volumetric data respecting online volumetric video encoding and steady bit-rates. Furthermore, qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities showcases the available options with respect to 4D representations. HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues.The dataset and its code are available online.

feature_table: Features for learning flow-sensitivity heuristics. Features present syntactic or semantic properties of variables in C programs.

Figure 1: Figure 1 presents the learned flow-sensitivity heuristic (f0, f1). F0 presents the variables that will be analyzed flow-insensitively while F1 presents the variables that will be analyzed flow-sensitively.

According to Cognitive Market Research, the global Decision Intelligence Market will be USD 12.1 billion in 2024 and expand at a compound annual growth rate (CAGR) of 21.4 % from 2024 to 2031.

Market Dynamics of Decision Intelligence Market

Key Drivers for Decision Intelligence Market

Advanced Analytics - The developing decision intelligence market is expanding rapidly, owing in great part to advanced analytics' crucial position. These cutting-edge analytical approaches, augmented by the capabilities of artificial intelligence, machine learning, and predictive modeling, serve as a driving force in transforming decision-making processes across a variety of industries. First and foremost, advanced analytics encourages firms to explore deeper into their data reservoirs, revealing detailed patterns, developing trends, and hidden correlations that standard methodologies may ignore. This enhanced clarity enables businesses to make more precise and forward-thinking judgments. Furthermore, modern analytics can anticipate future events with astonishing accuracy, transforming strategy planning. Decision intelligence systems, bolstered by advanced analytics, can predict market dynamics, customer behaviors, and prospective dangers, allowing for proactive strategy modifications and risk reduction. Real-time data analysis, enabled by sophisticated analytics, gives firms a competitive advantage in responding quickly to changing conditions. This agility is especially beneficial for effective crisis management and the timely grabbing of emerging possibilities.

Operational efficiency

Key Restraints for Decision Intelligence Market

Complex implementation

Security related data concerns may hinder market growth Introduction of Decision Intelligence Market

The decision intelligence market is a thriving industry dedicated to harnessing cutting-edge technology such as artificial intelligence, machine learning, and data analytics to improve the art of decision-making across multiple industries. It entails the creation of sophisticated tools and platforms that enable organizations to collect, analyze, and decode data, allowing them to make informed decisions, optimize their operational processes, and forecast future results. Decision Intelligence solutions are useful in a variety of industries, including finance, healthcare, supply chain management, and marketing, as they help firms achieve a competitive advantage by translating data into actionable and strategic insights. This market's expansion is being driven by the growing importance of data-centric decision-making in today's complex and competitive corporate environment.

The global Machine Learning in Finance market was valued at USD 7.52 billion in 2022 and is projected to reach USD 38.13 billion by 2030, registering a CAGR of 22.50% for the forecast period 2023-2030. Market Dynamics of the Machine Learning in Finance Market

Market Driver of the Machine Learning in Finance Market

The growing demand for predictive analytics and data-driven insights is driving the market for Machine Learning in Finance Market.

The rising need for data-driven insights and predictive analytics can be attributed for the machine learning (ML) industry's rapid expansion and adoption. The necessity of using the vast databases and find insightful patterns has become important as financial institutions try to navigate the complexity of a constantly shifting global economy. This increase in demand is being driven by the understanding that standard analytical techniques frequently fail to capture the details and complex relationships contained in financial data. The ability of ML algorithms to analyse enormous volumes of data at high speeds gives them the power to find hidden trends, correlations, and inconsistencies that are inaccessible to manual testing. In the financial markets, where a slight edge in anticipating market movements, asset price fluctuations, and risk exposures can result in significant gains or reduced losses, this skill is particularly important. Additionally, the use of ML in finance goes beyond trading and investing plans. Various fields, including risk management, fraud detection, customer service, and regulatory compliance, are affected. Financial organizations can more effectively analyze and manage risk by recognizing possible risks and modeling scenarios that allow for better decision-making by utilizing advanced algorithms. Systems that use machine learning to detect fraud are more accurate than those that use rule-based methods because they can identify unexpected patterns and behaviors that could be signs of fraud in real time. For instance, Customers who use its machine learning (ML)-based CPP Fraud Analytics software for credit card fraud detection and prevention experience increases in detection rates between 50% and 90% and decreases in investigation times for individual fraud cases of up to 70%.

Growing demand for cost-effectiveness and scalability

Market Restraint of the Machine Learning in Finance Market

The efficiency of machine learning models in finance may be affected by a lack of reliable, unbiased financial data.

The accessibility and quality of the data used to develop and employ machine learning (ML) models in the field of finance are directly related to these factors. The absence of high-quality and unbiased financial data is a significant barrier that frequently prevents the effectiveness of ML applications in finance. Lack of thorough and reliable information can compromise the effectiveness and dependability of ML models in a sector characterized by complexity, quick market changes, and a wide range of affecting factors. Financial data includes market prices, economic indicators, trade volumes, sentiment research, and much more. It is also extremely diverse. For ML algorithms to produce useful insights and precise forecasts, it is essential that this data be precise, current, and indicative of the larger financial scene. If the historical data is biased and provides half information the machine learning software might give biased result depending on the data which would also results in the wrong and ineffective trends.

The growing use of Artificial Intelligence to improve customer service and automate financial tasks is a trend in Machine Learning in Finance Market.

The rapid and prevalent adoption of artificial intelligence (AI) is currently driving a revolutionary trend in the financial market. There is growing use of artificial intelligence (AI) to improve customer service and automate a variety of financial processes. For instance, AI has the ability to increase economic growth by 26% and financial services revenue by 34%. This change is radically changing how financial organizations engage with their customers, streamline their processes, and provide services. These smart systems are made to respond to consumer queries, offer immediate support, and make specific suggestions. These AI-driven interfaces can comprehend and reply to consumer inquiries in a human-like manner by utilizin...

Living With Data (https://livingwithdata.org/current-research) was a research project funded by The Nuffield Foundation. It aimed to understand people’s perceptions of how data about them is collected, analysed, shared and used, and how these processes could be improved. The term ‘data uses’ was used as a short and accessible way of talking to people about these processes. The Living With Data website includes a full description of the research aims and of the methods used, and links to all publications that resulted from the project. Links to the visualisations we used in our interviews and focus group research can be found here: https://livingwithdata.org/resources. We have consent to share our qualitative data with authorised researchers only. Please send your request to Professor Helen Kennedy (h.kennedy@sheffield.ac.uk) explaining why you are interested in accessing the data and your institutional/researcher affiliation.

The data is divided into two sets corresponding to the experiments in the publication (preprint: https://arxiv.org/abs/2101.08944): FinalData_ppzee.hdf5 and FinalData_ppttbar.hdf5.

The data was generated using Madgraph5 v.2.6.3.2 [1], Pythia v.8.240 [2], Delphes v.3.4.1 [3], and ROOT v.6.08/00 [4]. Relevant run cards can be found with the code repository linked with this dataset.

[1] Johan Alwall et al. MadGraph 5 : Going Beyond. arxiv:1106.0522. 2011. URL: http://arxiv.org/abs/1106.0522.

[2] Torbjorn Sjostrand, Stephen Mrenna, and Peter Z. Skands. “PYTHIA 6.4 Physics and Manual”. In: JHEP 0605 (2006), p. 026. DOI: 10.1088/1126-6708/2006/05/026. arXiv: hep-ph/0603175 [hep-ph].

[3] J. de Favereau et al. “DELPHES 3, A modular framework for fast simulation of a generic collider experiment”. In: JHEP 02 (2014), p. 057. DOI: 10.1007/JHEP02(2014)057. arXiv: 1307.6346 [hep-ex].

[4] R. Brun and F. Rademakers. “ROOT: An object oriented data analysis framework”. In: Nucl. Instrum. Meth. A 38...

The global machine learning framework market is projected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX%. The growth of this market is attributed to the increasing adoption of machine learning across various industries, the growing need for data-driven decision-making, and the increasing popularity of cloud-based machine learning platforms. The market is also expected to benefit from the growing adoption of artificial intelligence (AI) technologies, as machine learning is a key component of AI systems. Some of the key trends in the machine learning framework market include the increasing adoption of open-source machine learning frameworks, the growing popularity of cloud-based machine learning platforms, and the increasing adoption of machine learning for automated machine learning (AutoML). Additionally, advancements in hardware technologies, such as the development of GPUs and TPUs, are also expected to drive the growth of the machine learning framework market. Some of the key companies in the machine learning framework market include TensorFlow, IBM Watson Studio, Amazon Web Services (AWS), Microsoft Azure, OpenNN, Auto-WEKA, Datawrapper, Google Cloud Platform (GCP), MLJAR, Tableau, PyTorch, Apache Mahout, Keras, Shogun, RapidMiner, Neural Designer, Scikit-learn, KNIME, Spell, and others.

This is the updated version of the dataset from 10.5281/zenodo.6320761 Information The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144648 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design. The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation. This dataset belongs to the publication: https://doi.org/10.3390/molecules27082513 Structure and content of the dataset Dataset structure ChEMBL ID PubChem ID IUPHAR ID Target Activity type Assay type Unit Mean C (0) ... Mean PC (0) ... Mean B (0) ... Mean I (0) ... Mean PD (0) ... Activity check annotation Ligand names Canonical SMILES C ... Structure check (Tanimoto) Source The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file. Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format. Column content: ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases Target: biological target of the molecule expressed as the HGNC gene symbol Activity type: for example, pIC50 Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified Unit: unit of bioactivity measurement Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence no comment: bioactivity values are within one log unit; check activity data: bioactivity values are not within one log unit; only one data point: only one value was available, no comparison and no range calculated; no activity value: no precise numeric activity value was available; no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration Ligand names: all unique names contained in the five source databases are listed Canonical SMILES columns: Molecular structure of the compound from each database Structure check (Tanimoto): To denote matching or differing compound structures in different source databases match: molecule structures are the same between different sources; no match: the structures differ. We calculated the Jaccard-Tanimoto similarity coefficient from Morgan Fingerprints to reveal true differences between sources and reported the minimum value; 1 structure: no structure comparison is possible, because there was only one structure available; no structure: no structure comparison is possible, because there was no structure available. Source: From which databases the data come from

Abstract—Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

The supervised learning market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse industries. The market, estimated at $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033. This significant expansion is fueled by several key factors. The escalating volume of data generated across various sectors provides rich training datasets for supervised learning algorithms. Businesses are increasingly leveraging supervised learning for predictive analytics, fraud detection, risk management, and personalized customer experiences. The cloud-based deployment model is gaining traction, offering scalability, cost-effectiveness, and accessibility. Furthermore, advancements in deep learning techniques and the availability of powerful computing resources are accelerating the adoption of sophisticated supervised learning models. The market segmentation reveals a strong presence of both on-premise and cloud-based solutions catering to Small and Medium Enterprises (SMEs) and Large Enterprises. Major players like Microsoft, IBM, Amazon, and others are investing heavily in research and development, fueling innovation and competition. Geographical analysis indicates a dominant market share for North America and Europe, driven by early adoption and robust technological infrastructure. However, Asia-Pacific is emerging as a rapidly growing region, presenting significant opportunities for future expansion. The continued advancements in algorithm efficiency, the decreasing cost of cloud computing, and the growing awareness of AI's potential across various industries are expected to propel the sustained growth of the supervised learning market throughout the forecast period.

The data-driven retail solutions market is experiencing robust growth, fueled by the increasing adoption of advanced analytics and the urgent need for retailers to enhance customer experiences and operational efficiency. The market, estimated at $15 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% through 2033, reaching approximately $50 billion. This expansion is driven primarily by the rising volume of consumer data generated through various touchpoints – e-commerce platforms, mobile apps, loyalty programs, and in-store interactions. Retailers leverage this data to personalize marketing campaigns, optimize pricing strategies, improve supply chain management, and predict future demand more accurately. The shift toward omnichannel retail strategies necessitates robust data analytics capabilities, further driving market growth. Large enterprises are currently the leading adopters, but small and medium-sized enterprises (SMEs) are increasingly investing in these solutions to compete effectively. The market is segmented by solution type (software, hardware, services), application (customer relationship management, inventory management, pricing optimization), and deployment mode (cloud, on-premises). Competitive landscape analysis shows a mix of established players like Oracle and Microsoft alongside emerging technology firms focusing on AI and machine learning for retail insights. The key restraints to market growth include concerns regarding data security and privacy, the high initial investment cost for implementing data-driven solutions, and the lack of skilled professionals proficient in data analytics and interpretation. However, these challenges are being addressed through advancements in data encryption and privacy-preserving technologies, alongside increasing investments in training and development programs to bridge the skills gap. Future growth will be shaped by the continued adoption of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) to enhance predictive modeling, personalized recommendations, and real-time inventory management. Regional growth will be led by North America and Europe due to higher technological adoption and established retail infrastructure, but significant growth potential exists in Asia-Pacific driven by rapid e-commerce expansion and a burgeoning middle class.

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes.

This record contains the data and code required to reproduce the results from the corresponding paper, computing message-passing inspired machine learning features built on top of density correlation. The data used in this article is a subset of other existing datasets, which can be found online:

• methane dataset: https://archive.materialscloud.org/record/2020.105
• NaCl dataset: https://github.com/dilkins/TENSOAP/tree/ea671154b3642b4ec879a4292a4dd4399ddbdea6/example/random_nacl
• QM7 and QM9 with dipole moments: https://archive.materialscloud.org/record/2020.56

Machine Learning Development Market 2025: Projected to hit USD 298.06 B by 2029 at 41.6% CAGR. Access in-depth analysis on trends, market dynamics, and competitive landscape for data-driven decisions.

This is a simple dataset of raw IQ measurements on a point-to-point wireless indoor channel, captured in a static laboratory environment. A sequence of random symbols (either QPSK symbols or random Gaussian symbols) are passed through a raised-root-cosine filter and modulated to different frequencies (433 MHz, 708 MHz, 2450 MHz). These measurements are carried out for varying signal-to-noise conditions (approximately 0 dB, 10 dB, 20 dB, estimated pre-measurement). These datasets may be used, for example, for data-driven channel modeling with state-of-the-art AI and machine learning algorithms (e.g., generative adversarial networks), for validating conventional channel models against real measurements, or for investigating the properties of real wireless channels. A detailed description is provided in the file README.pdf

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 to 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1 – 6 hours of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available.

The Data Science Platform market is experiencing robust growth, projected to reach $10.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.50% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability and affordability of cloud computing resources are lowering the barrier to entry for organizations of all sizes seeking to leverage data science capabilities. Furthermore, the growing volume and complexity of data generated across various industries necessitates sophisticated platforms for efficient data processing, analysis, and model deployment. The rise of AI and machine learning further fuels demand, as organizations strive to gain competitive advantages through data-driven insights and automation. Strong demand from sectors like IT and Telecom, BFSI (Banking, Financial Services, and Insurance), and Retail & E-commerce are major contributors to market growth. The preference for cloud-based deployment models over on-premise solutions is also accelerating market expansion, driven by scalability, cost-effectiveness, and accessibility. Market segmentation reveals a diverse landscape. While large enterprises are currently major consumers, the increasing adoption of data science by small and medium-sized enterprises (SMEs) represents a significant growth opportunity. The platform offering segment is anticipated to maintain a substantial market share, driven by the need for comprehensive tools that integrate data ingestion, processing, modeling, and deployment capabilities. Geographically, North America and Europe are currently leading the market, but the Asia-Pacific region, particularly China and India, is poised for significant growth due to expanding digital economies and increasing investments in data science initiatives. Competitive intensity is high, with established players like IBM, SAS, and Microsoft competing alongside innovative startups like DataRobot and Databricks. This competitive landscape fosters innovation and further accelerates market expansion. Recent developments include: November 2023 - Stagwell announced a partnership with Google Cloud and SADA, a Google Cloud premier partner, to develop generative AI (gen AI) marketing solutions that support Stagwell agencies, client partners, and product development within the Stagwell Marketing Cloud (SMC). The partnership will help in harnessing data analytics and insights by developing and training a proprietary Stagwell large language model (LLM) purpose-built for Stagwell clients, productizing data assets via APIs to create new digital experiences for brands, and multiplying the value of their first-party data ecosystems to drive new revenue streams using Vertex AI and open source-based models., May 2023 - IBM launched a new AI and data platform, watsonx, it is aimed at allowing businesses to accelerate advanced AI usage with trusted data, speed and governance. IBM also introduced GPU-as-a-service, which is designed to support AI intensive workloads, with an AI dashboard to measure, track and help report on cloud carbon emissions. With watsonx, IBM offers an AI development studio with access to IBMcurated and trained foundation models and open-source models, access to a data store to gather and clean up training and tune data,. Key drivers for this market are: Rapid Increase in Big Data, Emerging Promising Use Cases of Data Science and Machine Learning; Shift of Organizations Toward Data-intensive Approach and Decisions. Potential restraints include: Lack of Skillset in Workforce, Data Security and Reliability Concerns. Notable trends are: Small and Medium Enterprises to Witness Major Growth.

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

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

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

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

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

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The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request F

https://github.com/yexijoe/HKDD

@ARTICLE{10042021, author={Zheng, Shilian and Zhou, Xiaoyu and Zhang, Luxin and Qi, Peihan and Qiu, Kunfeng and Zhu, Jiawei and Yang, Xiaoniu}, journal={IEEE Transactions on Cognitive Communications and Networking}, title={Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification}, year={2023}, volume={}, number={}, pages={1-1}, doi={10.1109/TCCN.2023.3243899}}

Here we publish the first part of the dataset HKDD_AMC36 used in the paper "Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification". Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledgebased method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.