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[global-research-report]

How Data Annotation Tools Are Paving the Way for Advanced AI and Autonomous Systems

The global data annotation tools market size was estimated at USD 1.02 billion in 2023 and is anticipated to grow at a CAGR of 26.3% from 2024 to 2030. The growth is majorly driven by the increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors. The data annotation tools enable users to enhance the value of data by adding attribute tags to it or labeling it. The key benefit of using annotation tools is that the combination of data attributes enables users to manage the data definition at a single location and eliminates the need to rewrite similar rules in multiple places.

The rise of big data and a surge in the number of large datasets are likely to necessitate the use of artificial intelligence technologies in the field of data annotations. The data annotation industry is also expected to have benefited from the rising demands for improvements in machine learning as well as in the rising investment in advanced autonomous driving technology.

Technologies such as the Internet of Things (IoT), Machine Learning (ML), robotics, advanced predictive analytics, and Artificial Intelligence (AI) generate massive data. With changing technologies, data efficiency proves to be essential for creating new business innovations, infrastructure, and new economics. These factors have significantly contributed to the growth of the industry. Owing to the rising potential of growth in data annotation, companies developing AI-enabled healthcare applications are collaborating with data annotation companies to provide the required data sets that can assist them in enhancing their machine learning and deep learning capabilities.

For instance, in November 2022, Medcase, a developer of healthcare AI solutions, and NTT DATA, formalized a legally binding agreement. Under this partnership, the two companies announced their collaboration to offer data discovery and enrichment solutions for medical imaging. Through this partnership, customers of Medcase will gain access to NTT DATA's Advocate AI services. This access enables innovators to obtain patient studies, including medical imaging, for their projects.

However, the inaccuracy of data annotation tools acts as a restraint to the growth of the market. For instance, a given image may have low resolution and include multiple objects, making it difficult to label. The primary challenge faced by the market is issues related to inaccuracy in the quality of data labeled. In some cases, the data labeled manually may contain erroneous labeling and the time to detect such erroneous labels may vary, which further adds to the cost of the entire annotation process. However, with the development of sophisticated algorithms, the accuracy of automated data annotation tools is improving thus reducing the dependency on manual annotation and the cost of the tools.

Global Data Annotation Tools Market Report Segmentation

Grand View Research has segmented the global data annotation tools market report based on type, annotation type, vertical, and region:

Type Outlook (Revenue, USD Million, 2017 - 2030)

Text

Image/Video

Audio

Annotation Type Outlook (Revenue, USD Million, 2017 - 2030)

Manual

Semi-supervised

Automatic

Vertical Outlook (Revenue, USD Million, 2017 - 2030)

IT

Automotive

Government

Healthcare

Financial Services

Retail

Others

Regional Outlook (Revenue, USD Million, 2017 - 2030)

North America

US

Canada

Mexico

Europe

Germany

UK

France

Asia Pacific

China

Japan

India

South America

Brazil

Middle East and Africa (MEA)

Key Data Annotation Tools Companies:

The following are the leading companies in the data annotation tools market. These companies collectively hold the largest market share and dictate industry trends.

Annotate.com

Appen Limited

CloudApp

Cogito Tech LLC

Deep Systems

Labelbox, Inc

LightTag

Lotus Quality Assurance

Playment Inc

Tagtog Sp. z o.o

CloudFactory Limited

ClickWorker GmbH

Alegion

Figure Eight Inc.

Amazon Mechanical Turk, Inc

Explosion AI GMbH

Mighty AI, Inc.

Trilldata Technologies Pvt Ltd

Scale AI, Inc.

Google LLC

Lionbridge Technologies, Inc

SuperAnnotate LLC

Recent Developments

In November 2023, Appen Limited, a high-quality data provider for the AI lifecycle, chose Amazon Web Services (AWS) as its primary cloud for AI solutions and innovation. As Appen utilizes additional enterprise solutions for AI data source, annotation, and model validation, the firms are expanding their collaboration with a multi-year deal. Appen is strengthening its AI data platform, which serves as the bridge between people and AI, by integrating cutting-edge AWS services.

In September 2023, Labelbox launched Large Language Model (LLM) solution to assist organizations in innovating with generative AI and deepen the partnership with Google Cloud. With the introduction of large language models (LLMs), enterprises now have a plethora of chances to generate new competitive advantages and commercial value. LLM systems have the ability to revolutionize a wide range of intelligent applications; nevertheless, in many cases, organizations will need to adjust or finetune LLMs in order to align with human preferences. Labelbox, as part of an expanded cooperation, is leveraging Google Cloud's generative AI capabilities to assist organizations in developing LLM solutions with Vertex AI. Labelbox's AI platform will be integrated with Google Cloud's leading AI and Data Cloud tools, including Vertex AI and Google Cloud's Model Garden repository, allowing ML teams to access cutting-edge machine learning (ML) models for vision and natural language processing (NLP) and automate key workflows.

In March 2023, has released the most recent version of Enlitic Curie, a platform aimed at improving radiology department workflow. This platform includes Curie|ENDEX, which uses natural language processing and computer vision to analyze and process medical images, and Curie|ENCOG, which uses artificial intelligence to detect and protect medical images in Health Information Security.

In November 2022, Appen Limited, a global leader in data for the AI Lifecycle, announced its partnership with CLEAR Global, a nonprofit organization dedicated to ensuring access to essential information and amplifying voices across languages. This collaboration aims to develop a speech-based healthcare FAQ bot tailored for Sheng, a Nairobi slang language.

 Order a free sample PDF of the Market Intelligence Study, published by Grand View Research.

[gts6465]

Best Image Annotation Companies Compared: Features, Pricing, and Accuracy

Introduction

As applications powered by artificial intelligence, such as self-driving cars, healthcare diagnostics, and online retail, expand, image annotation has emerged as a crucial component in developing effective machine learning models. However, with numerous providers offering annotation services, how can one select the most suitable Image Annotation Companies for their requirements? In this article, we evaluate several leading image annotation companies in 2025, considering their features, pricing, and accuracy, to assist you in identifying the best match for your project.

1. GTS.AI – Enterprise-Grade Accuracy with Custom Workflows

GTS.AI is renowned for its flexible annotation pipelines, stringent enterprise security standards, and its ability to cater to various sectors such as the automotive, healthcare, and retail industries.

Key Features:

Supports various annotation types including bounding boxes, polygons, keypoints, segmentation, and video annotation.

Offers a scalable workforce that includes human validation.

Integrates seamlessly with major machine learning tools.

Adheres to ISO-compliant data security protocols.

Pricing:

Custom pricing is determined based on the volume of data, type of annotation, and required turnaround time.

Offers competitive rates for datasets requiring high accuracy.

Accuracy:

Achieves over 98% annotation accuracy through a multi-stage quality control process.

Provides annotator training programs and conducts regular audits.

Best for: Companies in need of scalable, highly accurate annotation services across various industries.

2. Labelbox – Platform Flexibility and AI-Assisted Tools

Labelbox provides a robust platform for teams seeking to manage their annotation processes effectively, featuring capabilities that cater to both internal teams and external outsourcing.

Key Features

Include a powerful data labeling user interface and software development kits,

Automation through model-assisted labeling,

Seamless integration with cloud storage and machine learning workflows.

Pricing

Options consist of a freemium tier,

Custom pricing for enterprises,

Pay-per-usage model for annotations.

Accuracy

May vary based on whether annotators are in-house or outsourced, with strong quality

Control tools that necessitate internal supervision.

This platform is ideal for machine learning teams in need of versatile labeling tools and integration possibilities.

3. Scale AI – Enterprise-Level Services for Complex Use Cases

Scale AI is a leading provider in the market for extensive and complex annotation tasks, such as 3D perception, LiDAR, and autonomous vehicle data.

Key Features:

Offers a wide range of annotation types, including 3D sensor data.

Utilizes an API-first platform that integrates with machine learning.

Provides dedicated project managers for large clients.

Pricing

Premium pricing, particularly for high-complexity data.

Offers project-based quotes.

Accuracy:

Renowned for top-tier annotation accuracy.

Implements multi-layered quality checks and human review.

Best for: Projects in autonomous driving, defense, and robotics that require precision and scale.

4. CloudFactory – Human-Centric Approach with Ethical Sourcing

CloudFactory offers a unique blend of skilled human annotators and ethical AI practices, positioning itself as an excellent choice for companies prioritizing fair labor practices and high data quality.

Key Features:

The workforce is trained according to industry-specific guidelines.

It supports annotation for images, videos, audio, and documents.

There's a strong focus on data ethics and the welfare of the workforce.

Pricing

Pricing is based on volume and is moderately priced compared to other providers.

Contracts are transparent.

Accuracy

There are multiple stages of human review.

Continuous training and feedback loops are implemented.

Best for: Companies looking for socially responsible and high-quality annotation services.

5. Appen – Global Crowd with AI Integration

Appen boasts one of the largest international crowds for data annotation, offering extensive support for various AI training data types, such as natural language processing and computer vision.

Key Features

Include a diverse global crowd with multilingual capabilities,

Automated workflows, and data validation tools,

As well as high data throughput suitable for large-scale projects.

Pricing

Appen provides competitive rates for bulk annotation tasks,

With options for pay-as-you-go and contract models.

Accuracy

The quality of data can fluctuate based on project management,

Although the workflows are robust, necessitating a quality control setup.

Best for: This service is ideal for global brands and research teams that need support across multiple languages and domains.

Conclusion: Choosing the Right Partner

The ideal image annotation company for your project is contingent upon your specific requirements:

If you require enterprise-level quality with adaptable services, Globose Technology Solution.AI is recommended.

For those seeking comprehensive control over labeling processes, Labelbox is an excellent choice.

If your project involves intricate 3D or autonomous data, Scale AI is specifically designed for such tasks.

If ethical sourcing and transparency are priorities, CloudFactory should be considered.

For multilingual and scalable teams, Appen may be the right fit.

Prior to selecting a vendor, it is essential to assess your project's scale, data type, necessary accuracy, and compliance requirements. A strategic partner will not only assist in labeling your data but also enhance your entire AI development pipeline.

[cybersecurityict]

The Data Collection And Labeling Market was valued at USD 3.0 Billion in 2023 and is expected to reach USD 29.2 Billion by 2032, growing at a CAGR of 28.54% from 2024-2032.

The data collection and labeling market is witnessing transformative growth as artificial intelligence (AI), machine learning (ML), and deep learning applications continue to expand across industries. As organizations strive to unlock the value of big data, the demand for accurately labeled datasets has surged, making data annotation a critical component in developing intelligent systems. Companies in sectors such as healthcare, automotive, retail, and finance are investing heavily in curated data pipelines that drive smarter algorithms, more efficient automation, and personalized customer experiences.

Data Collection and Labeling Market Fueled by innovation and technological advancement, the data collection and labeling market is evolving to meet the growing complexities of AI models. Enterprises increasingly seek comprehensive data solutions—ranging from image, text, audio, and video annotation to real-time sensor and geospatial data labeling—to power mission-critical applications. Human-in-the-loop systems, crowdsourcing platforms, and AI-assisted labeling tools are at the forefront of this evolution, ensuring the creation of high-quality training datasets that minimize bias and improve predictive performance.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/5925 

Market Keyplayers:

Scale AI – Scale Data Engine

Appen – Appen Data Annotation Platform

Labelbox – Labelbox AI Annotation Platform

Amazon Web Services (AWS) – Amazon SageMaker Ground Truth

Google – Google Cloud AutoML Data Labeling Service

IBM – IBM Watson Data Annotation

Microsoft – Azure Machine Learning Data Labeling

Playment (by TELUS International AI) – Playment Annotation Platform

Hive AI – Hive Data Labeling Platform

Samasource – Sama AI Data Annotation

CloudFactory – CloudFactory Data Labeling Services

SuperAnnotate – SuperAnnotate AI Annotation Tool

iMerit – iMerit Data Enrichment Services

Figure Eight (by Appen) – Figure Eight Data Labeling

Cogito Tech – Cogito Data Annotation Services

Market Analysis The market's growth is driven by the convergence of AI deployment and the increasing demand for labeled data to support supervised learning models. Startups and tech giants alike are intensifying their focus on data preparation workflows. Strategic partnerships and outsourcing to data labeling service providers have become common approaches to manage scalability and reduce costs. The competitive landscape features a mix of established players and emerging platforms offering specialized labeling services and tools, creating a highly dynamic ecosystem.

Market Trends

Increasing adoption of AI and ML across diverse sectors

Rising preference for cloud-based data annotation tools

Surge in demand for multilingual and cross-domain data labeling

Expansion of video and 3D image annotation for autonomous systems

Growing emphasis on ethical AI and reduction of labeling bias

Integration of AI-assisted labeling to accelerate workflows

Outsourcing of labeling processes to specialized firms for scalability

Enhanced use of synthetic data for model training and validation

Market Scope The data collection and labeling market serves as the foundation for AI applications across verticals. From autonomous vehicles requiring high-accuracy image labeling to chatbots trained on annotated customer interactions, the scope encompasses every industry where intelligent automation is pursued. As AI maturity increases, the need for diverse, structured, and domain-specific datasets will further elevate the relevance of comprehensive labeling solutions.

Market Forecast The market is expected to maintain strong momentum, driven by increasing digital transformation initiatives and investment in smart technologies. Continuous innovation in labeling techniques, enhanced platform capabilities, and regulatory compliance for data privacy are expected to shape the future landscape. Organizations will prioritize scalable, accurate, and cost-efficient data annotation solutions to stay competitive in an AI-driven economy. The role of data labeling is poised to shift from a support function to a strategic imperative.

Access Complete Report: https://www.snsinsider.com/reports/data-collection-and-labeling-market-5925 

Conclusion The data collection and labeling market is not just a stepping stone in the AI journey—it is becoming a strategic cornerstone that determines the success of intelligent systems. As enterprises aim to harness the full potential of AI, the quality, variety, and scalability of labeled data will define the competitive edge. Those who invest early in refined data pipelines and ethical labeling practices will lead in innovation, relevance, and customer trust in the evolving digital world.

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[gtsconsultantin]

Struggling with Data Labeling? Try These Image Annotation Services

Introduction:

In the era of artificial intelligence and machine learning,Image Annotation Services data is the driving force. However, raw data alone isn’t enough; it needs to be structured and labeled to be useful. For businesses and developers working on AI models, especially those involving computer vision, accurate image annotation is crucial. But data labeling is no small task. It’s time-consuming, resource-intensive, and requires a meticulous approach.

If you’ve been struggling with data labeling, you’re not alone. The good news is that professional image annotation services can make this process seamless and efficient. Here’s a closer look at why data labeling is challenging, the importance of image annotation, and the best services to help you get it done.

The Challenges of Data Labeling

Time-Consuming Process

Labeling thousands or even millions of images can take an enormous amount of time, delaying project timelines and slowing innovation.

High Cost of In-House Teams

Building and maintaining an in-house team for data labeling can be costly, especially for small and medium-sized businesses.

Need for Precision

AI models require accurate and consistent labels. Even minor errors in annotation can significantly impact the performance of your AI systems.

Scaling Issues As your dataset grows, so do the challenges of managing, labeling, and ensuring quality control at scale.

The Importance of Image Annotation

Image annotation involves adding metadata or labels to images, helping AI systems understand what’s in a picture. These annotations are used to train models for tasks such as:

Object detection

Image segmentation

Facial recognition

Autonomous driving systems

Medical imaging analysis

Without proper annotation, AI models cannot interpret visual data effectively, leading to inaccurate predictions and unreliable outputs.

Top Image Annotation Services to Streamline Your Projects

If you’re ready to take your AI projects to the next level, here are some top-notch image annotation services to consider:

Offers a range of high-quality image and video annotation services tailored to various industries, including healthcare, retail, and automotive. With a focus on precision and scalability, they ensure your data labeling needs are met efficiently.

Key Features:

Bounding boxes, polygons, and semantic segmentation

Annotation for 2D and 3D data

Scalable solutions for large datasets

Affordable pricing plans

Scale AI

Scale AI provides a comprehensive suite of data annotation services, including image, video, and text labeling. Their platform combines human expertise with machine learning tools to deliver high-quality annotations.

Key Features:

Rapid turnaround times

Detailed quality assurance

Customizable annotation workflows

Labelbox

Labelbox is a popular platform for managing and annotating datasets. Its intuitive interface and robust toolset make it a favorite for teams working on complex computer vision projects.

Key Features:

Integration with ML pipelines

Flexible annotation tools

Collaboration-friendly platform

CloudFactory

CloudFactory specializes in combining human intelligence with automation to deliver precise image annotations. Their managed workforce is trained to handle intricate labeling tasks with accuracy.

Key Features:

Workforce scalability

Specialized training for annotators

Multilingual support

Amazon SageMaker Ground Truth

Amazon’s SageMaker Ground Truth is a powerful tool for building labeled datasets. It uses machine learning to automate annotation and reduce manual effort.

Key Features:

Active learning integration

Pay-as-you-go pricing

Automated labeling workflows

Why Choose Professional Image Annotation Services?

Outsourcing your image annotation tasks offers several benefits:

Expertise: Professionals have the tools and experience to deliver precise annotations.

Efficiency: Save time and focus on your core business activities while experts handle the data labeling.

Scalability: Easily scale your annotation efforts as your dataset grows.

Cost-Effectiveness: Eliminate the need for in-house teams and costly software investments.

Conclusion

Data labeling doesn’t have to be a bottleneck for your AI projects. By leveraging professional image annotation services like Globose Technology Solutions and others, you can ensure your models are trained on high-quality, accurately labeled datasets. This not only saves time and resources but also enhances the performance of your AI systems.

So, why struggle with data labeling when you can rely on experts to do it for you? Explore the services mentioned above and take the first step toward seamless, efficient, and accurate image annotation today.

[techtired]

The world of AI depends heavily on data annotation, the process that turns raw data into labeled information that AI models can understand and learn from. As data labeling fuels everything from medical diagnostics to driverless cars, ethical considerations in this field have become a topic of discussion. Companies like Innovatiana and CloudFactory are leading by example, prioritizing ethical practices that respect both the data itself and the workforce behind the annotations. Why ethical data annotation matters When performed ethically, data annotation drives technological advancements that benefit society. However, data labeling also brings challenges, especially concerning workforce treatment, data privacy, and the quality of labeling processes. Unethical practices in data labeling have led to incidents of exploitation, unregulated labor conditions, and privacy breaches. These examples highlight the need for ethical and transparent approaches. The Role of workforce well-being in data labeling In the push to label data faster and cheaper, some companies have overlooked the needs of the annotators who do the work. In many cases, data labeling tasks are outsourced to workers in developing countries, where labor laws may be less stringent, leading to low wages and inadequate working conditions. Innovatiana, a company that specializes in data annotation, prioritizes the well-being of its workforce by implementing fair labor standards and creating supportive work environments. Innovatiana believes that valuing its annotators’ contributions results in higher-quality data, benefiting both the clients and the annotators. Regulatory landscape for data labeling Regulations surrounding data labeling aim to protect both workers and data privacy. Some critical regulatory frameworks include: General Data Protection Regulation (GDPR): Enforced in the EU, GDPR protects personal data and privacy. For data labeling, GDPR mandates that companies have a legal basis for data collection and that annotators understand the privacy implications of the data they handle. ILO standards on decent work: The International Labour Organization (ILO) advocates for fair wages, safe working conditions, and reasonable working hours. Companies operating in the data annotation industry, including Innovatiana, align with these standards to promote ethical and fair working conditions. In truth, the regulatory landscape is evolving to ensure ethical practices and high-quality AI systems. In the United States, the National Institute of Standards and Technology (NIST) has developed the AI Risk Management Framework (AI RMF), which emphasizes the importance of data quality and integrity in AI development. The AI RMF outlines best practices for data labeling, including ensuring that datasets are accurate, representative, and free from biases. These guidelines aim to promote trustworthy AI systems by addressing potential risks associated with data annotation processes. In the European Union, the Artificial Intelligence Act (EU AI Act) establishes a comprehensive framework for AI development and deployment. The Act mandates that datasets used for AI training must be accurate, representative, and free from biases to ensure the reliability and fairness of AI systems. This legislation directly impacts data annotation practices, emphasizing the need for high-quality data in training AI models. These regulations set important standards, but companies must take extra steps to ensure ethical practices in their labeling workforces. Examples of unethical data labeling practices Several incidents have revealed unethical practices in the data labeling industry, prompting calls for more rigorous ethical standards: Exploitation of clickworkers: In some cases, companies used a “clickworker” model, paying workers minimal fees for each annotation without fair pay or benefits. Many of these workers reported poor working conditions, with long hours and low pay. Misuse of personal data: Privacy concerns surfaced when workers were asked to label personal data without proper safeguards. This data, often anonymized insufficiently, exposed individuals’ identities, violating privacy laws and ethical standards. Invisible workforce in data factories: Some tech companies were found to have outsourced data labeling to "data factories" where workers had little job security, low wages, and minimal worker rights. Clients refusing to pay workers: in some cases, workers would not be paid by buyers, despite the low payment they demand for this type of tedious work. Innovatiana’s ethical approach to data annotation Innovatiana operates under the principle that ethical data annotation requires treating annotators with dignity and respect. From ensuring fair wages and proper working hours to providing a supportive and transparent environment, Innovatiana exemplifies how data annotation can be performed ethically and responsibly. Learn more about Innovatiana's approach to ethical data annotation, where the emphasis is on quality, privacy, and workforce wellbeing. How Ethical Data Labeling Benefits Everyone Ethical data labeling practices offer long-term benefits for companies and society: Higher quality data: Annotators who feel respected and valued are more likely to produce high-quality, accurate data, which in turn improves the performance of AI systems. Enhanced public trust: Transparent and ethical data practices build trust with consumers, particularly as awareness grows around issues like data privacy and workforce exploitation. Reduced legal risks: Compliance with labor laws and data privacy regulations minimize the risk of fines, lawsuits, and reputational damage. Companies that uphold ethical standards, like Innovatiana, set a powerful example. By putting workforce wellbeing and data privacy first, they contribute to an AI ecosystem that serves society responsibly. Moving forward: a call for ethical data annotation practices Ethics in data annotation isn’t just a nice-to-have—it’s a necessity. As AI continues to integrate into our lives, the data that powers it must be handled with care and responsibility. Companies like Innovatiana are proving that ethical data annotation is possible by creating conditions that prioritize both data quality and the people behind it. By supporting businesses that lead with integrity, we can all contribute to a more ethical and sustainable AI landscape. Sources Innovatiana - data annotation guide: Innovatiana provides a comprehensive guide to ethical data annotation, discussing essential topics such as data quality, privacy, and workforce wellbeing. Their approach emphasizes the importance of annotator dignity and respect, ensuring high-quality output and ethical standards. Innovatiana’s practices reflect a commitment to building datasets responsibly, contributing to a more ethical AI landscape. CloudFactory - ethical data labeling: CloudFactory is a global leader in providing data labeling services with a strong focus on ethical labor practices. Their model advocates for fair wages, safe working conditions, and training opportunities, especially for workers in developing regions. CloudFactory sets an example in the industry by aligning business goals with positive social impact, demonstrating how data annotation can foster both technological advancement and social good. International Labour Organization (ILO) - decent work standards: The ILO’s standards serve as a global benchmark for fair labor practices, emphasizing the need for adequate wages, secure working conditions, and workers' rights. These standards are crucial in the data labeling industry, where fair treatment of workers and adherence to labor laws are essential. By advocating for decent work, the ILO supports the ethical development of industries worldwide, including data annotation. Read the full article

[thedatagroupnewsservice]

ICYMI: CloudFactory Strengthens Leadership Team with Three C-Level Appointments http://dlvr.it/TB0pk9

[wildbeautifuldamned]

Meissen Porcelain Figurine Man Woman Flower Basket Bowl 7.5 High ebAY CloudFactory

[meow-mellow]

Strongbow: Nature's Dream from Cloudfactory on Vimeo.

Apple rivers, grass roads, giant cats made of apples, trees replacing streetlights...they say dreaming is for dreamers, but we dare to disagree. We decided to instead believe in the imaginations of those who can still dream of a world where we can bring Nature back to the cities. Strongbow decided to make these dreams come to life. Follow Mary and Monsieur Plant as they jam how Nature could pimp up every corner. Witness one dream wilder than the other, as they invite the city people to surf on an apple river and skate off boards made of bark.

MARY MATTINGLY Mary Mattingly is an artist who builds ecosystems and mobile environments. An avid gardener, she began creating interdependent living systems as a way to reimagine public areas, the place where we’ll spend our present and future. One of her most famous projects is the Swale, a floating orchard that will bring the spark of Nature back to the New York. “What if free healthy food what a public service and not an expensive commodity?” asks Mattingly in her video about Swale, “That’s the question we really want to ask with this mobile structure.”

MONSIEUR PLANT French artist Christophe Guinet, best known as Monsieur Plant, is a passionate aesthete of the plant world and green visionary, whose work aims to revolutionize the way we interact with products. Having spent his entire life between towns and the countryside, he likes to mix elements from both worlds to create something completely new, like a skateboard made of tree bark. He likes to “Go back to the source: man is spiritually and artistically nourished by nature.” His projects inspire you to rediscover beauty in nature and learn to appreciate again the energy it gives to everything.

Creative Agency: Cloudfactory Director: Fredrik Bond Production company:  Sonny London DOP:  Hoyte van Hoytema Post production:  The Mill - Los Angeles Edit House:  Marshall Street Editors, London Music Track:  "It's Oh So Quiet" (Hans Lang / Bert Reisfeld) Recording Artist:  Davina Sowers Sound:  Ambassadors Amsterdam

[philipbyersart]

What do you do to experience tranquility? I painted this piece near the end of my tenure in graduate school, when my stress level had reached a fever-pitch and I was looking for a reprieve. I intentionally looked for colors and motifs I found soothing to incorporate into a piece: icy blues, pinks, bright yellows, undulating curves, pearlescent shell-forms and languid cloud blankets all embodied the feeling I was looking for. #art #artist #painting #digitalpainting #digitalart #landscapepainting #landscapeart #surreal #surrealism #fantasy #fantasyart #magic #scifi #scifiart #originalart #surrealartist #sky_clouds #sky_brilliance #artlife #cloudfactory #artdreams #magicalart #otherworlds #mysticart #mysticalart #visionaryartist #otherworldly #heaven #heavenly #glacier https://www.instagram.com/p/B84eEhWjxKd/?igshid=qmpgy4idg8tg

[sweet-like-cottoncandy]

Ordered and consumed at CloudFactory in Siouxfalls 💗

Five stars~ ⭐️⭐️⭐️⭐️⭐️

[theglutenfreezookeeper]

I guess it’s been quite a bit since I’ve last done any sort of #CrossStitchProject update. So many projects. So little time. This one is from my @Cloudfactory huge #HarryPotter project. Cameos by @subversivecrossstitch, @silvermoonsewing and @thefrostedpumpkinstitchery #crossstitch #crossstitchersofinstagram #crossstitcher #crossstitching #MinervaMcGonagall #HoraceSlughorn #SybillTrelawney #RemusLupin #harrypotter #HarryPotterCrossStitch #cloudfactory #subversivecrossstitch #alohomora (at West New York, New Jersey) https://www.instagram.com/p/CXsYp1LrZPF/?utm_medium=tumblr

[vapedawg979]

Humble #vapeon #vape #vapormax #vapetricks #vaporwave #vapenation #vapeporn #vaping #vapefam #vapelyfe #vapeaddict #vapepix #vapestyle #vapeworld #vapedaily #vapehappy #vape💨 #vapor #vape4you #vapworld #vape #ecig #cloudsporn #vapefam #vapefriends #smokingisdeadvapingisthefuturethefutureisnow #cloudfactory #vapepic #vaporhub #vapehard #vapestagram #vapingworld # #cloudbeast #humbleejuice #humblevape (at Copperas Cove, Texas)

[jasonmayes]

Just finished my 2nd #presentation on #MachineLearning at #CloudFactory in Nepal! Great crowd with lots of questions. Wonderful time meeting you all, stay in touch.

#ml #ai #artificialIntelligence #engineer #crowd #present #google #googler #wednesday #nepal #Kathmandu #business #educate #teach #travel #computerScience #tech #technology #cloud #conference #datascience #fun

[backtogarage]

*Un grand merci à @cloudfactory__ , @fotomatic_band et @lune.froide pour très belle soirée ! **Prochain RDV ce Samedi chez @monsieurmachinnantes de 18h a 22h ! ***Prochain concert le Samedi 2 Octobre avec @territoryband et Renard Empaillé (si tout se passe bien...) ! . .. ... #nantes #concert #livemusic #backtigarage #cloudfactory #fotomatic #howlinbanana #lunefroide #monsieurmachin #cafeconcert (à Lune Froide) https://www.instagram.com/p/CFZTghmpgkc/?igshid=1ujb2iuu1k7qv

[thedatagroupnewsservice]

CloudFactory Strengthens Leadership Team with Three C-Level Appointments http://dlvr.it/T9yRL4

[wildbeautifuldamned]

Meissen Porcelain Large Figurine Dance Party Multiple Figures EBAY CloudFactory