Data Preparation Tools Market Outlook

The global data preparation tools market size was valued at USD 3.5 billion in 2023 and is projected to reach USD 12.8 billion by 2032, exhibiting a CAGR of 15.5% during the forecast period. The primary growth factors driving this market include the increasing adoption of big data analytics, the rising significance of data-driven decision-making, and growing technological advancements in AI and machine learning.

The surge in data-driven decision-making across various industries is a significant growth driver for the data preparation tools market. Organizations are increasingly leveraging advanced analytics to gain insights from massive datasets, necessitating efficient data preparation tools. These tools help in cleaning, transforming, and structuring raw data, thereby enhancing the quality of data analytics outcomes. As the volume of data generated continues to rise exponentially, the demand for robust data preparation tools is expected to grow correspondingly.

The integration of AI and machine learning technologies into data preparation tools is another crucial factor propelling market growth. These technologies enable automated data cleaning, error detection, and anomaly identification, thereby reducing manual intervention and increasing efficiency. Additionally, AI-driven data preparation tools can adapt to evolving data patterns, making them highly effective in dynamic business environments. This trend is expected to further accelerate the adoption of data preparation tools across various sectors.

As the demand for efficient data handling grows, the role of Data Infrastructure Construction becomes increasingly crucial. This involves building robust frameworks that support the seamless flow and management of data across various platforms. Effective data infrastructure construction ensures that data is easily accessible, securely stored, and efficiently processed, which is vital for organizations leveraging big data analytics. With the rise of IoT and cloud computing, constructing a scalable and flexible data infrastructure is essential for businesses aiming to harness the full potential of their data assets. This foundational work not only supports current data needs but also prepares organizations for future technological advancements and data growth.

The growing emphasis on regulatory compliance and data governance is also contributing to the market expansion. Organizations are required to adhere to strict regulatory standards such as GDPR, HIPAA, and CCPA, which mandate stringent data handling and processing protocols. Data preparation tools play a vital role in ensuring that data is compliant with these regulations, thereby minimizing the risk of data breaches and associated penalties. As regulatory frameworks continue to evolve, the demand for compliant data preparation tools is likely to increase.

Regionally, North America holds the largest market share due to the presence of major technology players and early adoption of advanced analytics solutions. Europe follows closely, driven by stringent data protection regulations and a strong focus on data governance. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid industrialization, increasing investments in big data technologies, and the growing adoption of IoT. Latin America and the Middle East & Africa are also anticipated to experience steady growth, supported by digital transformation initiatives and the expanding IT infrastructure.

Platform Analysis

The platform segment of the data preparation tools market is categorized into self-service data preparation, data integration, data quality, and data governance. Self-service data preparation tools are gaining significant traction as they empower business users to prepare data independently without relying on IT departments. These tools provide user-friendly interfaces and drag-and-drop functionalities, enabling users to quickly clean, transform, and visualize data. The rising need for agile and faster data preparation processes is driving the adoption of self-service platforms.

Data integration tools are essential for combining data from disparate sources into a unified view, facilitating comprehensive data analysis. These tools support the extraction, transformation, and loading (ETL) processes, ensuring data consistency and accuracy. With the increasing complexity of data environments and the need f

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

The Data Preparation Platform market is experiencing robust growth, driven by the exponential increase in data volume and the rising need for high-quality data for advanced analytics and AI initiatives. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This growth is fueled by several key factors. Large enterprises are heavily investing in data preparation solutions to streamline their data pipelines and improve operational efficiency. Simultaneously, the increasing adoption of cloud-based solutions, offering scalability and cost-effectiveness, is significantly contributing to market expansion. The demand for self-service data preparation tools, empowering business users to directly access and prepare data, is also a major driver. While the on-premise segment still holds a considerable share, cloud-based solutions are rapidly gaining traction due to their flexibility and accessibility. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific and South America, presents lucrative opportunities for market players. However, several restraints are also impacting market growth. The complexity of integrating data preparation tools with existing IT infrastructure, high initial investment costs for on-premise solutions, and the need for skilled professionals to manage and utilize these platforms are significant challenges. Furthermore, data security and privacy concerns associated with handling sensitive data remain a primary obstacle. Despite these challenges, the long-term outlook remains positive, with the market poised for sustained growth driven by the continuous advancements in data analytics technologies and the increasing recognition of the crucial role of data preparation in generating business insights. Competition within the market is intense, with established players like Microsoft, Tableau, and IBM competing with emerging innovative companies. This competitive landscape fosters innovation and drives the development of more efficient and user-friendly data preparation platforms.

Utilize our machine learning datasets to develop and validate your models. Our datasets are designed to support a variety of machine learning applications, from image recognition to natural language processing and recommendation systems. You can access a comprehensive dataset or tailor a subset to fit your specific requirements, using data from a combination of various sources and websites, including custom ones. Popular use cases include model training and validation, where the dataset can be used to ensure robust performance across different applications. Additionally, the dataset helps in algorithm benchmarking by providing extensive data to test and compare various machine learning algorithms, identifying the most effective ones for tasks such as fraud detection, sentiment analysis, and predictive maintenance. Furthermore, it supports feature engineering by allowing you to uncover significant data attributes, enhancing the predictive accuracy of your machine learning models for applications like customer segmentation, personalized marketing, and financial forecasting.

The Data Preparation Tools market is experiencing robust growth, projected to reach a significant market size by 2033. Driven by the exponential increase in data volume and variety across industries, coupled with the rising need for accurate, consistent data for effective business intelligence and machine learning initiatives, this sector is poised for continued expansion. The 18.5% Compound Annual Growth Rate (CAGR) signifies strong market momentum, fueled by increasing adoption across diverse sectors like IT and Telecom, Retail & E-commerce, BFSI (Banking, Financial Services, and Insurance), and Manufacturing. The preference for self-service data preparation tools empowers business users to directly access and prepare data, minimizing reliance on IT departments and accelerating analysis. Furthermore, the integration of data preparation tools with advanced analytics platforms and cloud-based solutions is streamlining workflows and improving overall efficiency. This trend is further augmented by the growing demand for robust data governance and compliance measures, necessitating sophisticated data preparation capabilities. While the market shows significant potential, challenges remain. The complexity of integrating data from multiple sources and maintaining data consistency across disparate systems present hurdles for many organizations. The need for skilled data professionals to effectively utilize these tools also contributes to market constraints. However, ongoing advancements in automation and user-friendly interfaces are mitigating these challenges. The competitive landscape is marked by established players like Microsoft, Tableau, and IBM, alongside innovative startups offering specialized solutions. This competitive dynamic fosters innovation and drives down costs, benefiting end-users. The market segmentation by application and tool type highlights the varied needs and preferences across industries, and understanding these distinctions is crucial for effective market penetration and strategic planning. Geographical expansion, particularly within rapidly developing economies in Asia-Pacific, will play a significant role in shaping the future trajectory of this thriving market.

The MLCommons Dollar Street Dataset is a collection of images of everyday household items from homes around the world that visually captures socioeconomic diversity of traditionally underrepresented populations. It consists of public domain data, licensed for academic, commercial and non-commercial usage, under CC-BY and CC-BY-SA 4.0. The dataset was developed because similar datasets lack socioeconomic metadata and are not representative of global diversity.

This is a subset of the original dataset that can be used for multiclass classification with 10 categories. It is designed to be used in teaching, similar to the widely used, but unlicensed CIFAR-10 dataset.

These are the preprocessing steps that were performed:

1. Only take examples with one imagenet_synonym label
2. Use only examples with the 10 most frequently occuring labels
3. Downscale images to 64 x 64 pixels
4. Split data in train and test
5. Store as numpy array

This is the label mapping:

Checkout https://github.com/carpentries-lab/deep-learning-intro/blob/main/instructors/prepare-dollar-street-data.ipynb" target="_blank" rel="noopener">this notebook to see how the subset was created.

The original dataset was downloaded from https://www.kaggle.com/datasets/mlcommons/the-dollar-street-dataset. See https://mlcommons.org/datasets/dollar-street/ for more information.

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

The data preparation analytics industry is projected to grow at a CAGR of 18.74% from 2025 to 2033, reaching a market size of $6.74 billion by 2033. The market growth is primarily driven by the increasing adoption of cloud-based and on-premise data preparation tools, the rising demand for data-driven insights, and the growing need for data governance and compliance. Cloud-based solutions offer flexibility, cost-effectiveness, and scalability, making them attractive to businesses of all sizes. Key trends shaping the market include the rise of artificial intelligence (AI) and machine learning (ML) for data preparation automation, increased demand for self-service data preparation tools, and the growing adoption of agile development methodologies. AI and ML algorithms can automate time-consuming and error-prone data preparation tasks, such as data cleaning, transformation, and feature engineering. Self-service data preparation tools empower business users to prepare data without the need for IT support. Agile methodologies promote rapid iterative development, requiring faster and more efficient data preparation processes. The industry is expected to witness continued growth in the coming years, driven by these factors. The data preparation analytics industry is a rapidly growing market, driven by the increasing need for businesses to make sense of their data. According to a report by Grand View Research, the global data preparation analytics market size was valued at USD 8.3 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 12.5% from 2021 to 2028. Recent developments include: December 2022: Alteryx, Inc., the Analytics Automation company, announced a strategic investment in MANTA, the data lineage company. MANTA enables businesses to achieve complete visibility into the most complex data environments. With this investment from Alteryx Ventures, the company can bolster product innovation, expand its partner ecosystem, and grow in key markets., November 2022: Amazon Web Services (AWS) announced a series of new features for Amazon QuickSight, the cloud computing giant's analytics platform. The update includes new query, forecasting, and data preparation features, adding functionality to QuickSight Q, a natural language query (NLQ) tool.. Key drivers for this market are: Demand for Self-service Data Preparation Tools, Increasing Demand for Data Analytics. Potential restraints include: Limited Budgets and Low Investments owing to Complexities and Associated Risks.. Notable trends are: IT and Telecom Segment is Expected to Hold a Significant Market Share.

The global data preparation market is estimated to reach $1978 million in 2033, growing at a CAGR of 13.7% from 2025 to 2033. The increasing volume and complexity of data, along with the need for data-driven decision-making, are driving the growth of the market. Organizations are looking for ways to make their data more usable and accessible, and data preparation tools can help them do just that. Key trends in the market include the rise of self-service data preparation tools, the adoption of cloud-based data preparation platforms, and the increasing use of artificial intelligence (AI) and machine learning (ML) in data preparation. Data Curation, Data Cataloging, and Data Quality are the major types of data preparation tools, and Hosted and On-premises are the two main deployment modes. North America is the largest region in the market, followed by Europe and Asia Pacific. The market is highly competitive, with a number of vendors offering data preparation tools. Key vendors in the market include Alteryx, Inc, Informatica, IBM, Tibco Software Inc., Microsoft, SAS Institute, Datawatch Corporation, Tableau Software, Qlik Technologies Inc., SAP SE., Talend, Microstrategy Incorporated, among others.

Developments in Artificial Intelligence (AI) have had an enormous impact on scientific research in recent years. Yet, relatively few robust methods have been reported in the field of structure-based drug discovery. To train AI models to abstract from structural data, highly curated and precise biomolecule-ligand interaction datasets are urgently needed. We present MISATO, a curated dataset of almost 20000 experimental structures of protein-ligand complexes, associated molecular dynamics traces, and electronic properties. Semi-empirical quantum mechanics was used to systematically refine protonation states of proteins and small molecule ligands. Molecular dynamics traces for protein-ligand complexes were obtained in explicit water. The dataset is made readily available to the scientific community via simple python data-loaders. AI baseline models are provided for dynamical and electronic properties. This highly curated dataset is expected to enable the next-generation of AI models for structure-based drug discovery. Our vision is to make MISATO the first step of a vibrant community project for the development of powerful AI-based drug discovery tools.

The Data Preparation Tools market is experiencing robust growth, projected to reach a value of $4.5 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 32.14% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and velocity of data generated by organizations necessitate efficient and automated data preparation processes. Businesses are increasingly adopting cloud-based solutions for data preparation, driven by scalability, cost-effectiveness, and enhanced collaboration capabilities. Furthermore, the rise of self-service data preparation tools empowers business users to directly access and prepare data, reducing reliance on IT departments and accelerating data analysis. The growing adoption of advanced analytics and machine learning initiatives also contributes to market growth, as these technologies require high-quality, prepared data. While the on-premise deployment model still holds a significant share, the cloud segment is expected to witness faster growth due to its inherent advantages. Within the platform segment, both data integration and self-service tools are experiencing strong demand, reflecting the diverse needs of various users and business functions. The competitive landscape is characterized by a mix of established players like Informatica, IBM, and Microsoft, and emerging innovative companies specializing in specific niches. These companies employ various competitive strategies, including product innovation, strategic partnerships, and mergers and acquisitions, to gain market share. Industry risks include the complexity of integrating data preparation tools with existing IT infrastructure, the need for skilled professionals to effectively utilize these tools, and the potential for data security breaches. Geographic growth is expected to be significant across all regions, with North America and Europe maintaining a strong presence due to high adoption rates of advanced technologies. However, the Asia-Pacific region is poised for substantial growth due to rapid technological advancements and increasing data volumes. The historical period (2019-2024) shows a steady increase in market size, providing a strong foundation for the projected future growth. The market is segmented by deployment (on-premise, cloud) and platform (data integration, self-service), reflecting the various approaches to data preparation.

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights. Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required. Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units. Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable. Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates. Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

Data Preparation Tools and Software Market Outlook

The global data preparation tools and software market size was valued at USD 3.5 billion in 2023 and is projected to reach USD 11.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.6% during the forecast period. This impressive growth can be attributed to the increasing need for data-driven decision-making, the rising adoption of big data analytics, and the growing importance of business intelligence across various industries.

One of the key growth factors driving the data preparation tools and software market is the exponential increase in data volume generated by both enterprises and consumers. With the proliferation of IoT devices, social media, and digital transactions, organizations are inundated with vast amounts of data that need to be processed and analyzed efficiently. Data preparation tools help in cleaning, transforming, and structuring this raw data, making it usable for analytics and business intelligence, thereby enabling companies to derive actionable insights and maintain a competitive edge.

Another significant driver for the market is the rising complexity of data sources and types. Organizations today deal with diverse datasets coming from various sources such as relational databases, cloud storage, APIs, and even machine-generated data. Data preparation tools and software provide automated and scalable solutions to handle these complex datasets, ensuring data consistency and accuracy. The tools also facilitate seamless integration with various data sources, enabling organizations to create a unified view of their data landscape, which is crucial for effective decision-making.

The growing adoption of advanced technologies such as AI and machine learning is also boosting the demand for data preparation tools and software. These technologies require high-quality, well-prepared data to function efficiently and generate reliable outcomes. Data preparation tools that incorporate AI capabilities can automate many of the repetitive and time-consuming tasks involved in data cleaning and transformation, thereby improving productivity and reducing human error. This, in turn, accelerates the implementation of AI-driven solutions across different sectors, further propelling market growth.

Regionally, North America currently holds the largest share of the data preparation tools and software market, driven by the presence of leading technology companies and a robust infrastructure for data analytics and business intelligence. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digitization, increasing adoption of cloud-based solutions, and significant investments in big data and AI technologies. Europe is also a key market, with growing awareness about data governance and privacy regulations driving the adoption of data preparation tools.

Component Analysis

When analyzing the data preparation tools and software market by component, it is broadly categorized into software and services. The software segment is further divided into standalone data preparation tools and integrated solutions that come as part of larger analytics or business intelligence platforms. Standalone data preparation tools offer specialized functionalities such as data cleaning, transformation, and enrichment, catering to specific data preparation needs. These tools are particularly popular among organizations that require high levels of customization and flexibility in their data preparation processes.

On the other hand, integrated solutions are gaining traction due to their ability to provide end-to-end capabilities, from data preparation to visualization and analytics, all within a single platform. These solutions typically offer seamless integration with other business intelligence tools, enabling users to move from data preparation to analysis without switching between different software. This integrated approach is particularly beneficial for enterprises looking to streamline their data workflows and improve operational efficiency.

The services segment includes professional services such as consulting, implementation, and training, as well as managed services. Professional services are crucial for organizations that lack in-house expertise in data preparation and need external assistance to set up and optimize their data preparation processes. These services help organizations effectively leverage data preparation tools, ensuring that they achieve maximum ROI. Managed services, on the other hand, are

three of these models are available:

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

We have an in-house team of Data Scientists & Data Engineers along with sophisticated data labeling, data pre-processing, and data wrangling tools to speed up the process of data management and ML model development. We have an AI-enabled platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place. ADVIT simplifies the working of your DL Application development.

This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.

The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.

The deposited data includes:

krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.

krkCMd_images.zip: archive containing source image files in folders by test series: - stacked images of cracks in subsequent stages of self-healing (.tif files),- zip archives assigned to image stacks and containing sets of ImageJ data files .roi,- ImageJ .roi files specifying the locations of grid lines in the images.

krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.

For details please see the data descriptor [1]. When referring to the data in publications please cite [1].

[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. Sci Data 12, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z

[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. Constr. Build. Mater. 422, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768

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

Project Machine Learning

## Overview

Project Machine Learning is a dataset for object detection tasks - it contains Deteksi Rempah Rempah annotations for 1,270 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Introduction

Machine Learning Statistics: In recent years, machine learning has become a game-changer, transforming various industries by enabling systems to analyze data, detect patterns, and make decisions with minimal human input. Central to machine learning are statistical methods, which form the backbone of data analysis, model creation, and prediction.

By leveraging algorithms that learn from data, machine learning facilitates the development of predictive models that continuously improve. The incorporation of statistics into machine learning not only supports data-driven decision-making but also improves the precision and clarity of models.

This powerful combination of machine learning and statistics has driven significant progress in fields such as healthcare, finance, marketing, and technology, enabling organisations to unlock valuable insights from vast datasets and foster innovation.