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

Dataset Card for Data-Centric-Visual-AI-Train-Set

This is a FiftyOne dataset with 30,000 samples.

  Installation

If you haven't already, install FiftyOne: pip install -U fiftyone

  Usage

import fiftyone as fo import fiftyone.utils.huggingface as fouh

Load the dataset

Note: other available arguments include 'max_samples', etc

dataset = fouh.load_from_hub("Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set")

Launch the App

session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set.

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

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 the processed integrated traffic data with work zone and incident information. Attached below are the number of lanes data and impacted work zone .pkl file.

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

Data Centric Security Market Outlook

In 2023, the global data-centric security market size was valued at approximately $3.8 billion. The market is expected to grow at a robust CAGR of 18.5% from 2024 to 2032, reaching a forecasted value of nearly $14.5 billion by 2032. The primary growth factor driving this market is the increasing awareness and adoption of data protection measures in response to rising cyber threats and stringent regulatory requirements.

The growth of the data-centric security market is significantly influenced by the escalating instances of cyber-attacks and data breaches, which have become increasingly sophisticated. Organizations across various sectors are recognizing the need for robust security measures that focus on data protection rather than just perimeter defenses. This shift in focus towards securing the data itself, regardless of its location, is a crucial driver for the market. Additionally, regulatory frameworks such as GDPR, CCPA, and HIPAA are compelling businesses to adopt data-centric security solutions to ensure compliance and avoid hefty penalties.

Another pivotal growth factor is the rapid digital transformation and the consequent rise in the volume of data generated and stored by organizations. With the proliferation of IoT devices, cloud computing, and mobile technologies, the amount of sensitive data being processed and transferred across various platforms has increased exponentially. This surge necessitates advanced security solutions that can safeguard data across different environments, thereby boosting the demand for data-centric security tools and services. Companies are increasingly investing in encryption, data masking, and tokenization technologies to secure their critical information assets.

The integration of artificial intelligence (AI) and machine learning (ML) in data-centric security solutions is also fueling market growth. These advanced technologies enhance the capabilities of security systems by enabling real-time threat detection, predictive analytics, and automated responses to potential security incidents. AI-driven security solutions can analyze vast amounts of data, identify anomalies, and mitigate risks more efficiently than traditional methods. The continuous advancements in AI and ML are expected to drive the adoption of data-centric security solutions further, as they provide more robust and adaptive protection mechanisms.

In the realm of data-centric security, Data Center Logical Security plays a pivotal role in safeguarding information assets. As organizations increasingly rely on data centers to store and manage vast amounts of sensitive data, logical security measures become crucial. These measures include access controls, authentication protocols, and network security policies designed to protect data from unauthorized access and cyber threats. By implementing robust logical security frameworks, data centers can ensure the integrity and confidentiality of their data, thereby enhancing overall data protection strategies. As the demand for secure data management solutions grows, the emphasis on logical security within data centers is expected to rise, driving further innovation and investment in this area.

Regionally, North America holds a significant share of the data-centric security market, driven by the high adoption rate of advanced technologies and stringent regulatory compliance requirements. The presence of major market players and the increasing number of cyber threats in the region are also contributing to the market's growth. Europe follows closely, with regulations like GDPR pushing organizations to prioritize data security. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digitalization, increasing cyber-attacks, and growing awareness about data protection among enterprises.

Component Analysis

The data-centric security market is segmented by component into software, hardware, and services. The software segment encompasses various solutions such as encryption, data masking, data tokenization, and others. Encryption software is particularly significant in this segment, as it provides a robust method for protecting data at rest and in transit. The growing adoption of cloud services and the rising need to secure data in multi-cloud environments are propelling the demand for encryption solutions. Data masking and tokenization are also gaining traction, especially in industries like fi

According to a global survey among marketing professionals in January 2025, approximately ** percent reported using artificial intelligence (AI) extensively in their data-driven marketing efforts. Around ** percent said they integrated AI in select areas, whereas ** percent were exploring AI, but have not implemented the technology. Some ** percent reported not having plans to use AI.

Artificial Intelligence (AI) Training Dataset Market Outlook

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

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

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

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

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

Data Type Analysis

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

Dataset Card for Homework Training Set for Coursera MOOC - Hands Data Centric Visual AI

This dataset is the training dataset for the homework assignments of the Hands-on Data Centric AI Coursera course. This is a FiftyOne dataset with 18287 samples.

  Installation

If you haven't already, install FiftyOne: pip install -U fiftyone

  Usage

import fiftyone as fo import fiftyone.utils.huggingface as fouh

Load the dataset

Note: other available arguments include… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Coursera_homework_dataset_train.

The Data-Centric Security market, valued at $11.1 billion in 2025, is experiencing robust growth driven by the escalating volume and sensitivity of data across various sectors. The increasing frequency and sophistication of cyberattacks, coupled with stringent data privacy regulations like GDPR and CCPA, are compelling organizations to prioritize data security beyond traditional perimeter-based approaches. This shift is fueling the adoption of data-centric security solutions that focus on protecting data wherever it resides—whether in the cloud, on-premises, or in transit. Key drivers include the rise of cloud adoption, the growing adoption of big data analytics, and the increasing need to protect sensitive data from insider threats and external attacks. The market is segmented by type (professional services and managed services) and application (BFSI, IT & Telecommunications, Retail, Government & Public Sector, Healthcare, and Others), with BFSI and IT & Telecommunications currently dominating due to their high reliance on sensitive customer and operational data. The North American market holds a significant share, driven by early adoption of advanced security technologies and strong regulatory frameworks. However, the Asia-Pacific region is poised for substantial growth due to increasing digitalization and expanding data volumes. The market's growth trajectory is expected to continue, driven by technological advancements such as AI-powered threat detection and automation in security operations. The increasing integration of data-centric security with other security solutions, like data loss prevention (DLP) and encryption, is further enhancing its effectiveness. While market restraints include the high cost of implementation and the complexity of integrating these solutions into existing IT infrastructures, the long-term benefits of enhanced data protection and regulatory compliance outweigh these challenges. The competitive landscape is characterized by a mix of established players and emerging vendors, with ongoing innovation and consolidation shaping the market dynamics. The forecast period (2025-2033) anticipates a sustained CAGR, though a specific figure requires further market analysis. Assuming a conservative CAGR of 15% based on industry trends, we can project substantial growth throughout the forecast period, making data-centric security a high-growth and attractive market segment.

The Data Loss Prevention (DLP) tools market is experiencing robust growth, driven by the increasing volume and sensitivity of data generated and processed by businesses across all sectors. 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 the end of the forecast period. This growth is fueled by several key factors, including the rising prevalence of data breaches and stringent regulatory compliance requirements like GDPR and CCPA, pushing organizations to adopt advanced DLP solutions. The shift towards cloud-based services and remote work further accelerates market expansion as businesses seek to protect data across increasingly distributed environments. Major trends shaping the market include the integration of Artificial Intelligence (AI) and Machine Learning (ML) for improved threat detection and automated response, the rise of data-centric security strategies, and the increasing demand for solutions that address both structured and unstructured data loss. Market segmentation reveals a significant share held by large enterprises, reflecting their greater data volumes and higher risk profiles. Cloud-based DLP solutions are also gaining prominence due to their scalability and ease of deployment. However, factors such as high initial investment costs for comprehensive DLP implementations and the complexities of integrating DLP solutions with existing IT infrastructure pose challenges to wider market adoption. The competitive landscape is highly fragmented, with a mix of established cybersecurity vendors (e.g., Broadcom, McAfee, Trend Micro) and specialized DLP providers (e.g., Forcepoint, Digital Guardian, Code42). These companies are engaged in continuous innovation, introducing new features and capabilities to enhance data protection. Geographic analysis shows strong market presence in North America and Europe, driven by higher levels of digitalization and stricter data privacy regulations. However, the Asia-Pacific region is also demonstrating significant growth potential, driven by rising digital adoption and increasing awareness of data security risks. Future market growth will depend on the continued evolution of DLP technologies to address emerging threats, the development of more user-friendly and cost-effective solutions, and the expansion of market awareness and adoption across various industry sectors and geographical regions. Overall, the DLP tools market is poised for substantial expansion, driven by an ever-increasing need to secure sensitive data in a rapidly evolving digital landscape.

The AI Basic Data Service market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across diverse sectors. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated market size of $75 billion by 2033. This expansion is fueled by several key factors: the burgeoning demand for high-quality data to train and improve AI models across applications like autonomous driving, smart security, and finance; the rise of data-centric businesses reliant on readily available, accurate datasets; and the ongoing development of innovative data collection, processing, and annotation services. The market's segmentation reveals significant opportunities within customized data services, catering to the specific needs of individual businesses, and data set products, offering pre-packaged solutions for broader applications. Key players, including Baidu, Alibaba, Tencent, and several specialized data providers, are actively shaping market dynamics through strategic partnerships, acquisitions, and technological advancements. Geographic distribution indicates strong growth across North America and Asia Pacific, fueled by significant investments in AI infrastructure and technological innovation within these regions. Market restraints include concerns surrounding data privacy and security, the high cost of data acquisition and processing, and the need for robust data governance frameworks to ensure data quality and ethical AI development. Nevertheless, the substantial investments in AI infrastructure, coupled with continuous improvements in data annotation and processing technologies, are poised to mitigate these challenges. The market's future trajectory will likely be shaped by advancements in synthetic data generation, the increasing adoption of cloud-based AI solutions, and the emergence of innovative business models that address data accessibility and affordability. The continued growth in applications of AI across various industries will further fuel the demand for basic data services, ensuring sustained market expansion in the coming decade.

Data Management Platforms Market Outlook

The global Data Management Platforms (DMP) market size is projected to witness substantial growth from 2023, when it was valued at approximately USD 3.5 billion, to an estimated USD 11.8 billion by 2032, at a commendable compound annual growth rate (CAGR) of 14.7% during the forecast period. This growth is primarily driven by the increasing demand for data-driven decision-making across multiple sectors, enhancing operational efficiency, customer engagement, and overall business intelligence. The integration of advanced data management solutions is becoming crucial as businesses worldwide strive to harness the power of big data analytics and artificial intelligence to gain competitive advantages and streamline their operations.

One of the primary growth factors in the data management platforms market is the exponential rise in data generation from various sources, including social media, IoT devices, and enterprise applications. Organizations are keen on leveraging this vast amount of data to gain insights into customer behaviors and preferences, optimize marketing strategies, and improve product offerings. The ability of DMPs to aggregate, segment, and analyze data from various sources allows businesses to target specific customer segments more effectively. Furthermore, as industries become more data-centric, there is a growing need for robust data management solutions to ensure data accuracy, security, and compliance with global data protection regulations.

Another significant growth driver is the increasing adoption of cloud-based data management solutions, which offer scalability, flexibility, and cost-effectiveness. Cloud deployment allows businesses to rapidly integrate DMPs without the need for heavy infrastructure investments, making it particularly attractive for small and medium enterprises (SMEs). Moreover, the cloud-based approach facilitates real-time data processing and analytics, enabling businesses to make timely and informed decisions. As organizations continue to digitize their operations, the demand for cloud-based DMPs is expected to surge, propelling the market growth further.

The integration of artificial intelligence (AI) and machine learning (ML) technologies with data management platforms is also playing a pivotal role in market growth. AI-driven DMPs enhance data processing capabilities by automating data categorization, analysis, and reporting processes. These advancements enable organizations to derive deeper insights from their data, predict trends, and personalize customer experiences. The continuous evolution of AI and ML technologies is expected to drive innovation in the DMP market, leading to more sophisticated and efficient solutions that cater to the dynamic needs of businesses across various industry verticals.

Data Monetization is becoming an increasingly important strategy for businesses seeking to leverage their data assets for additional revenue streams. As organizations collect vast amounts of data from various sources, the ability to effectively monetize this data can provide significant competitive advantages. By transforming raw data into valuable insights, companies can create new products and services, enhance customer experiences, and optimize operational efficiencies. Moreover, data monetization enables businesses to unlock new market opportunities and drive innovation, ultimately contributing to sustainable growth and profitability.

The regional outlook for the data management platforms market reveals a strong potential for growth across several key regions. North America is a leading market, driven by high adoption rates of advanced technologies and a strong presence of market players. The region's robust IT infrastructure and focus on data-driven strategies position it for sustained growth. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate, fueled by rapid digital transformation, increasing internet penetration, and a growing number of SMEs adopting data management solutions. Europe also presents significant opportunities, particularly with stringent data privacy regulations like GDPR driving the need for effective data management strategies.

Component Analysis

The data management platforms market is segmented into two primary components: software and services. The software segment is poised for significant growth, driven by the increasing demand for advanced analytics tools and data integration solutions. DMP so

The Data Annotation and Labeling Tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $10 billion by 2033. This expansion is fueled by several key factors. The automotive industry leverages data annotation for autonomous driving systems development, while healthcare utilizes it for medical image analysis and diagnostics. Financial services increasingly adopt these tools for fraud detection and risk management, and retail benefits from enhanced product recommendations and customer experience personalization. The prevalence of both supervised and unsupervised learning techniques necessitates diverse data annotation solutions, fostering market segmentation across manual, semi-supervised, and automatic tools. Market restraints include the high cost of data annotation and the need for skilled professionals to manage the annotation process effectively. However, the ongoing advancements in automation and the decreasing cost of computing power are mitigating these challenges. The North American market currently holds a significant share, with strong growth also expected from Asia-Pacific regions driven by increasing AI adoption. Competition in the market is intense, with established players like Labelbox and Scale AI competing with emerging companies such as SuperAnnotate and Annotate.io. These companies offer a range of solutions catering to varying needs and budgets. The market's future growth hinges on continued technological innovation, including the development of more efficient and accurate annotation tools, integration with existing AI/ML platforms, and expansion into new industry verticals. The increasing adoption of edge AI and the growth of data-centric AI further enhance the market potential. Furthermore, the growing need for data privacy and security is likely to drive demand for tools that prioritize data protection, posing both a challenge and an opportunity for providers to offer specialized solutions. The market's success will depend on the ability of vendors to adapt to evolving needs and provide scalable, cost-effective, and reliable annotation solutions.

Dataset Card for Lecture Training Set for Coursera MOOC - Hands Data Centric Visual AI

This dataset is the training dataset for the in-class lectures of the Hands-on Data Centric Visual AI Coursera course. This is a FiftyOne dataset with 16638 samples.

  Installation

If you haven't already, install FiftyOne: pip install -U fiftyone

  Usage

import fiftyone as fo import fiftyone.utils.huggingface as fouh

Load the dataset

Note: other available arguments include… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Coursera_lecture_dataset_train.

Background Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis. This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death.

The most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy. The highly granular synthetic data for over 900 patients with ACS provide the following key parameters to support critical research into this condition:

1. Patient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities)
2. Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness)
3. Muscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase)
4. Blood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers)

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details.

Available supplementary data: ACS cohort, Matched controls; ambulance, OMOP data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Data Science Platform Market Outlook

The global data science platform market size was valued at approximately USD 49.3 billion in 2023 and is projected to reach USD 174.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.1% during the forecast period. This exponential growth can be attributed to the increasing demand for data-driven decision-making processes, the surge in big data technologies, and the need for more advanced analytics solutions across various industries.

One of the primary growth factors driving the data science platform market is the rapid digital transformation efforts undertaken by organizations globally. Companies are shifting towards data-centric business models to gain a competitive edge, improve operational efficiency, and enhance customer experiences. The proliferation of IoT devices and the subsequent explosion of data generated have further propelled the need for sophisticated data science platforms capable of analyzing vast datasets in real-time. This transformation is not only seen in large enterprises but also increasingly in small and medium enterprises (SMEs) that recognize the potential of data analytics in driving business growth.

Moreover, the advancements in artificial intelligence (AI) and machine learning (ML) technologies have significantly augmented the capabilities of data science platforms. These technologies enable the automation of complex data analysis processes, allowing for more accurate predictions and insights. As a result, sectors such as healthcare, finance, and retail are increasingly adopting data science solutions to leverage AI and ML for personalized services, fraud detection, and supply chain optimization. The integration of AI/ML into data science platforms is thus a critical factor contributing to market growth.

Another crucial factor is the growing regulatory and compliance requirements across various industries. Organizations are mandated to ensure data accuracy, security, and privacy, necessitating the adoption of robust data science platforms that can handle these aspects efficiently. The implementation of regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has compelled organizations to invest in advanced data management and analytics solutions. These regulatory frameworks are not only a challenge but also an opportunity for the data science platform market to innovate and provide compliant solutions.

Regionally, North America dominates the data science platform market due to the early adoption of advanced technologies, a strong presence of key market players, and significant investments in research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the increasing digitalization initiatives, a growing number of tech startups, and the rising demand for analytics solutions in countries like China, India, and Japan. The competitive landscape and economic development in these regions are creating ample opportunities for market expansion.

Component Analysis

The data science platform market, segmented by components, includes platforms and services. The platform segment encompasses software and tools designed for data integration, preparation, and analysis, while the services segment covers professional and managed services that support the implementation and maintenance of these platforms. The platform component is crucial as it provides the backbone for data science operations, enabling data scientists to perform data wrangling, model building, and deployment efficiently. The increasing demand for customized solutions tailored to specific business needs is driving the growth of the platform segment. Additionally, with the rise of open-source platforms, organizations have more flexibility and control over their data science workflows, further propelling this segment.

On the other hand, the services segment is equally vital as it ensures that organizations can effectively deploy and utilize data science platforms. Professional services include consulting, training, and support, which help organizations in the seamless integration of data science solutions into their existing IT infrastructure. Managed services provide ongoing support and maintenance, ensuring data science platforms operate optimally. The rising complexity of data ecosystems and the shortage of skilled data scientists are factors contributing to the growth of the services segment, as organizations often rely on external expert

The Artificial Intelligence (AI) Data Management market is poised for remarkable growth in the coming decade, with a CAGR of 23% projected from 2025 to 2033. This growth is fueled by the surging demand for data-driven insights and advanced analytics, particularly in sectors like BFSI, retail, manufacturing, and healthcare, where data is paramount for competitive advantage. Cloud-based solutions are gaining traction due to their scalability, flexibility, and cost-effectiveness, while on-premises deployments still hold prominence in certain segments. Major players in the AI Data Management market include Microsoft, AWS, IBM, Google, and Oracle. These companies are investing heavily in innovation and partnerships to offer comprehensive solutions that cater to the evolving needs of data-centric organizations. North America remains the largest regional market, with significant contributions from the United States and Canada. Asia Pacific is expected to witness promising growth, driven by the rapidly expanding digital economies of China and India. The increasing adoption of AI-powered data management tools by enterprises seeking efficiency, automation, and enhanced decision-making will continue to drive the market's expansion.

The Artificial Intelligence For IT Operations Platform Market size was valued at USD 11.76 billion in 2023 and is projected to reach USD 36.15 billion by 2032, exhibiting a CAGR of 17.4 % during the forecasts period. The Artificial Intelligence for IT Operations (AIOps) Platform Market refers to the AI solutions aimed at improving IT operations’ control and efficiency. These platforms are based on the use of machine learning, big data processing, and automation to track, assess, and manage IT facilities and applications. It is applied in capacity planning, real-time monitoring of the IT systems, proactive intervention as well as maintenance of performance levels. Applications cover broad sectors including; banking and finance, health, retailing/Commercial services, telecommunications, and many others where IT reliability and productivity are paramount. Some of the trends that are witnessed in the market include; using AIOps in the hybrid and multi-cloud assist, IT operation and the DevOps, CI/CD assist, and incorporating AIOps with cybersecurity. The upswing of data-centric AIOps platforms, and how AI is used in expediting organizational transformation to achieve the needed IT resilience. Recent developments include: In June 2023, Saama, an AI- and ML-based solutions provider, unveiled an AI-driven data platform to expedite clinical advancements. This new platform leverages the power of artificial intelligence and machine learning to automate crucial processes in clinical development. This innovative solution empowers clinical development teams to make informed decisions, optimize trial efficiency, and enhance research outcomes. , In May 2023, DataStax, a leading real-time AI company, introduced Luna ML, a support service designed specifically for Kaskada Open Source. Luna ML offers customers comprehensive assistance in leveraging modern, open-source event processing for machine learning (ML), empowering them to implement Kaskada with expert support from DataStax. , In May 2023, NTT, a Japan-based IT infrastructure and services company, launched SPEKTRA (Sentient Platform for Network Transformation), its global services platform for NTT-managed network solutions. This platform leverages NTT's extensive expertise in managed services, technical resources, and cutting-edge technologies such as AIOps, automation techniques, and predictive analytics. It enhances network performance, expands monitoring capabilities, and significantly improves operational efficiency to deliver better customer solutions. , In May 2023, IBM introduced Watsonx, an innovative AI and data platform to empower enterprises to expand and expedite cutting-edge AI technologies' transformative potential while leveraging reliable and trustworthy data. By providing a comprehensive suite of tools and capabilities, Watsonx enables AI builders to test, train, fine-tune, and deploy various machine learning models, including the latest generative AI capabilities powered by foundation models. , In May 2023, Infosys, a digital services and consulting company, introduced Infosys Topaz, a comprehensive suite of services, solutions, and platforms designed using generative AI technologies. By integrating the capabilities of Infosys Cobalt cloud and data analytics, Infosys Topaz harnesses AI's potential to empower businesses to deliver cognitive solutions. , In May 2023, Tata Consultancy Services unveiled a collaboration with Google Cloud and introduced its latest offering. By harnessing the capabilities of generative AI, TCS aims to support clients in accelerating their growth and transformation endeavors. TCS Pace Ports will facilitate these collaborative initiatives, which serve as co-innovation hubs in strategic locations such as Pittsburgh, New York, Toronto, Amsterdam, and Tokyo. , In April 2023, Hewlett Packard Enterprise (HPE) introduced the next generation of HPE Aruba Networking Central. This next-generation release of HPE Aruba Networking Central offers many valuable business outcomes, including streamlined factory operations, personalized customer experiences, reduced environmental impact, and enhanced omnichannel retail operations. With its advanced capabilities, businesses can benefit from automated processes, cater to individual customer preferences, contribute to sustainability efforts, and provide a seamless shopping experience across in-store, mobile, and online channels. , In March 2023, Zenoss Inc., a leading AI-driven full-stack monitoring company, unveiled a new offering: streaming data monitoring for Kubernetes. This cutting-edge solution allows for real-time monitoring of Kubernetes streaming data and forms part of a comprehensive suite of initiatives centered around cloud-based monitoring. By providing visibility into ephemeral systems that are challenging to monitor using conventional tools, Zenoss empowers organizations to monitor and manage their cloud-based environments effectively. , In January 2023, DataStax acquired Kaskada and released the Kaskada code as open source in March. This strategic move provided customers access to open capabilities well-suited for real-time AI and ML applications. As the interest and adoption of Kaskada Open Source continue to evolve, the Luna ML support service from DataStax will serve as a valuable resource, offering users the necessary guidance and expertise to deploy Kaskada technology confidently. , In April 2022, Moogsoft, a prominent player in the AIOps solutions market, made notable advancements by introducing cutting-edge features and integrations. By integrating these new features, Moogsoft aims to elevate the user experience, empowering customers with valuable and actionable insights within a remarkably short timeframe, typically within a few days after implementation. These developments highlight Moogsoft's commitment to continuous innovation and dedication to delivering enhanced value to its customers in the dynamic field of AIOps. .