PromptCloud offers cutting-edge data extraction services that empower businesses with real-time, actionable intelligence from the vast expanses of the online marketplace. We are committed to putting data at the heart of your business. Reach out for a no-frills PromptCloud experience- professional, technologically ahead and reliable.

Our Amazon Best Seller Products Dataset is a key tool for businesses looking to understand and capitalize on market trends. It allows you to identify top-selling products and sellers, and track their performance across various categories and subcategories. This dataset is invaluable for competitive intelligence, monitoring trending products, and understanding customer sentiment. It also plays a crucial role in monitoring competitor prices and enhancing product inventory, ensuring that your business stays relevant and competitive.

Beyond Amazon, PromptCloud offers access to a diverse range of Ecommerce Product Data from various e-commerce websites. PromptCloud is a leading provider of advanced web scraping services, uniquely tailored to meet the dynamic needs of modern businesses. Our services are fully customizable, allowing clients to specify source websites, data collection frequencies, data points, and delivery mechanisms to fit their unique requirements​​. The data aggregation feature of our web crawler enables the extraction of data from multiple sources in a single stream, catering to a diverse range of ecommerce clients.

PromptCloud is a leading provider of advanced web scraping services, uniquely tailored to meet the dynamic needs of modern businesses. Our services are fully customizable, allowing clients to specify source websites, data collection frequencies, data points, and delivery mechanisms to fit their unique requirements​​. The data aggregation feature of our web crawler enables the extraction of data from multiple sources in a single stream, catering to a diverse range of clients, from news aggregators to job boards​​.

With over a decade of experience in extracting web data from any e-commerce website, PromptCloud stands as a seasoned veteran in the field. This extensive experience translates into high-quality, reliable data extraction, making PromptCloud your ideal product web data extraction partner. The reliability of our data is uncompromised, with a 100% verification process that ensures accuracy and trustworthiness.

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost 638 billion U.S. dollars, up from 575 billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over 185 billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately 353 billion U.S. dollars was earned in North America compared to only roughly 131 billion U.S. dollars internationally.

Amazon made $35.22 billion from memberships and subscriptions in 2022.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

About This Data

This is a list of over 34,000 consumer reviews for Amazon products like the Kindle, Fire TV Stick, and more provided by Datafiniti's Product Database. The dataset includes basic product information, rating, review text, and more for each product.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to analyze Amazon’s most successful consumer electronics product launches; discover insights into consumer reviews and assist with machine learning models. E.g.:

• What are the most reviewed Amazon products?
• What are the initial and current number of customer reviews for each product?
• How do the reviews in the first 90 days after a product launch compare to the price of the product?
• How do the reviews in the first 90 days after a product launch compare to the days available for sale?
• Map the keywords in the review text against the review ratings to help train sentiment models.

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

You can access the full dataset by running the following query with Datafiniti’s Product API.

{ "query": "dateUpdated:[2017-09-01 TO *] AND brand:Amazon* AND categories:* AND primaryCategories:* AND name:* AND reviews:*", "format": "csv", "download": true }

**The total number of results may vary.*

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset contains modeled hourly streamflow at each about eighteen thousand selected operational and water quality stream gage locations. It was assembled from publicly available retrospective and operational V1.2 National Water Model outputs. The streamflow variable was extracted from model output files and the data were reshaped to optimize read performance. The stream gage locations were derived from several ongoing USGS projects using gages for evaluation of streamflow, water quality, and real-time monitoring however only National Water Model identifiers and NHDPlus catchment outlet locations are used to identify model results. Relationships between NWIS gages and National Water Model prediction locations were not reviewed for release at the time of publication of this data. Please contact the author for up to date information. All processing resources used for data reformatting and extraction can be found in this repository: https://code.usgs.gov/water/nwm_subset. This supporting code also contains Docker images that are capable of processing real-time National Water Model outputs into similar formats as are in this dataset and providing data services via the THREDDS Data Server. The retrospective is available in one NetCDF file. The operational model run archives are available in .tar.gz archives that contain daily Forecast Model Run Collection files. These data conform as much as possible to the NetCDF-CF Discrete Sampling Geometry conventions and are designed to be aggregated along the reference-time dimension allowing creation of a complete collection of forecast model runs with the THREDDS data server. The retrospective data are available from public cloud data outlets (Such as: https://registry.opendata.aws/nwm-archive/) and the operational outputs were retrieved from an archive maintained at the NOAA National Water Center in Tuscaloosa, AL. Note that some data were missing from this archive, on 5-8-2018 for all operational outputs and 7-17-2017 for long range outputs. The operational model runs include analysis and assimilation, short range, medium range, and an ensemble of four long range model runs. More information on these data are available from the National Water Model v1.2 release notes here. http://www.nws.noaa.gov/os/notification/scn18-16national_water_model.htm. The contents of that html page have been archived with this dataset. The retrospective model run is available from: https://docs.opendata.aws/noaa-nwm-pds/readme.html. The precise data included here should match that exactly but was sourced from NOAA-OWP systems at the National Water Center prior to availability of data from Amazon Public Datasets.

Visual storytelling refers to the manner of describing a set of images rather than a single image, also known as multi-image captioning. Visual Storytelling Task (VST) takes a set of images as input and aims to generate a coherent story relevant to the input images. In this dataset, we bridge the gap and present a new dataset for expressive and coherent story creation. We present the Sequential Storytelling Image Dataset (SSID), consisting of open-source video frames accompanied by story-like annotations. In addition, we provide four annotations (i.e., stories) for each set of five images. The image sets are collected manually from publicly available videos in three domains: documentaries, lifestyle, and movies, and then annotated manually using Amazon Mechanical Turk. In summary, SSID dataset is comprised of 17,365 images, which resulted in a total of 3,473 unique sets of five images. Each set of images is associated with four ground truths, resulting in a total of 13,892 unique ground truths (i.e., written stories). And each ground truth is composed of five connected sentences written in the form of a story.

Amazon Mechanical Turk (AMT) is used to collect annotations on HowTo100M videos. 30k 60-second clips are randomly sampled from 9,421 videos and present each clip to the turkers, who are asked to select a video segment containing a single, self-contained scene. After this segment selection step, another group of workers are asked to write descriptions for each displayed segment. Narrations are not provided to the workers to ensure that their written queries are based on visual content only. These final video segments are 10-20 seconds long on average, and the length of queries ranges from 8 to 20 words. From this process, 51,390 queries are collected for 24k 60-second clips from 9,371 videos in HowTo100M, on average 2-3 queries per clip. The video clips and its associated queries are split into 80% train, 10% val and 10% test.

MSR-VTT (Microsoft Research Video to Text) is a large-scale dataset for the open domain video captioning, which consists of 10,000 video clips from 20 categories, and each video clip is annotated with 20 English sentences by Amazon Mechanical Turks. There are about 29,000 unique words in all captions. The standard splits uses 6,513 clips for training, 497 clips for validation, and 2,990 clips for testing.

Netflix's global subscriber base has reached an impressive milestone, surpassing 300 million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly 20 million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over one million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting 70 million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around 70.1 million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

In March 2022, Amazon Prime Video had over 141 million unique visitors in India, a significant increase from the previous year. The global player had partnerships with Yash Raj Films, Dharma Productions, and T-Series.

This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10? N to 25? S, 30? to 85? W).

The global river discharge data set (Coe and Olejniczak 1999), formerly known as the "Climate, People, and Environment Program (CPEP) Global River Discharge Database," is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.

The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.

More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.

LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.