Requesters are individuals or entities limited by the computing power at their disposal, thus requiring substantial power for Data Labeling, AI model training, or 3D rendering.
Data Labeling Requester
Data Labeling Requesters are typically organizations, research institutions, and AI-driven businesses that need accurately labeled data to train, test, and improve machine learning models. As AI and machine learning continue to expand into various fields, the demand for high-quality labeled data grows.
Data Labeling Requesters come from diverse industries, including:
- Technology Companies: Businesses that develop AI products, such as image recognition, natural language processing, and predictive analytics, rely heavily on labeled data to train their algorithms. They need consistent, high-quality labels to ensure their models perform accurately and reliably in real-world applications.
- Healthcare Organizations: Medical research institutions and healthcare providers often use labeled medical images, such as X-rays or MRI scans, to train diagnostic AI systems. Accurate labeling in these applications is critical for identifying conditions and supporting medical decisions, ultimately improving patient care.
- Financial Services: Financial institutions use labeled transaction data, customer behavior insights, and sentiment analysis to power fraud detection systems, algorithmic trading models, and customer service AI. Precise labeling helps ensure these models are both accurate and compliant with regulatory standards.
- Retail and E-commerce: Companies in this sector use labeled images, text, and video data to enhance product recommendations, manage inventory, and improve customer experiences through visual search and product categorization.
- Agriculture and Environmental Science: Researchers in these fields require labeled satellite imagery, crop data, and environmental signals to monitor changes in ecosystems, optimize crop yields, and assess climate impacts.
- Autonomous Vehicles: Self-driving car manufacturers require vast amounts of labeled data, including images, LiDAR data, and GPS information, to train vehicle perception systems. Labeled data helps autonomous vehicles accurately detect and respond to obstacles, traffic signals, and pedestrians.
- Educational Institutions: Universities and research labs also require labeled datasets for developing AI and machine learning projects across a wide range of disciplines, from language processing to computer vision.
Data Labeling Pipeline
In the AI/ML pipeline, data labeling is a crucial step in preparing data for model training.
- Data Collection and Preprocessing: Raw data is gathered from diverse sources such as sensors, databases, and APIs. Since this data often lacks structure or format and may contain inconsistencies, it undergoes preprocessing, where it’s cleaned, formatted, and transformed to ensure consistency and compatibility for labeling.
- Data Labeling: After preprocessing, the data is labeled or annotated to provide the AI/ML model with the necessary information for learning. Labeling techniques vary depending on the data type, such as image, text, audio, or video. After the data is labeled, the Consensus and Quality Assurance (QA) stage ensures the accuracy, consistency, and completeness of the labels.
- Model Training and Fine-Tuning: The labeled data is then used to train the model, where it learns patterns between inputs and labels. The model is tested with unseen labeled data, and its performance is measured through metrics like accuracy, precision, and recall. If the model’s performance is suboptimal, adjustments are made before retraining, which may involve improving the quality of the labeled data.
- Deployment: Finally, the model is deployed into production, ready to interact with real-world data and generate insights or predictions.
Data labeling is essential in this pipeline, enabling models to learn effectively from high-quality, consistent, and structured data.
How Rynus fits:
- Data Labeling Workforce: Rynus can scale up to millions of Workers participating in data labeling tasks on the Telegram Mini App via any device (mobile, tablet, PC, laptop). This offers unprecedented scalability for data labeling projects.
- Labeling Quality: Rynus implements a credit scoring mechanism using error-trap data and the Rynus Consensus Zero Knowledge Proof. This approach helps verify and maintain high labeling accuracy by assessing and improving Worker reliability over time.
- Data Security: To protect sensitive data, Rynus employs a cut-and-mix data mechanism that allows Requesters to choose their preferred security level when uploading data.
AI Requester
In the field of AI, the Requesters using Rynus’s services encompass a broad spectrum of individuals and organizations that require substantial cloud computing power to train their AI models. These include:
- AI Researchers and Scientists: These professionals work in academic institutions, research labs, and tech companies, focusing on advancing the state-of-the-art in artificial intelligence. They require extensive computational resources to experiment with novel algorithms, perform large-scale simulations, and validate their hypotheses through rigorous testing.
- Machine Learning Engineers: Employed in industries ranging from finance to healthcare, these engineers develop and deploy machine learning models for various applications. They need scalable and powerful computing infrastructure to train models efficiently, handle large datasets, and iterate quickly on their designs to optimize performance.
- Data Scientists: Working in diverse sectors like retail, telecommunications, and marketing, data scientists use cloud computing resources to analyze vast amounts of data, extract insights, and build predictive models. Rynus provides them with the necessary computational power to perform complex data processing and machine learning tasks.
- Startups and Tech Companies: Many startups and tech companies, particularly those focusing on AI-driven products and services, rely on cloud computing to scale their operations without significant upfront investment in hardware. These companies leverage Rynus’s decentralized network to access cost-effective and high-performance computing resources.
- Developers of Large Language Models (LLMs): Teams working on LLMs such as GPT, BERT, and others require immense computational power to train and fine-tune these models. Rynus offers a scalable solution to meet their high demands for GPU resources, enabling the development of sophisticated natural language processing tools.
- Businesses Implementing AI Solutions: Companies across various industries are integrating AI to enhance their services and products. From recommendation systems in e-commerce to fraud detection in banking, these businesses utilize Rynus to gain access to the necessary computing power for deploying and maintaining their AI models.
Diverse Pipelines, Outsourcing, and Remote Working
The AI field is characterized by diverse and complex pipelines that encompass various stages, from data collection and preprocessing to model training, validation, and deployment. AI projects often require collaboration among geographically dispersed teams, which can introduce significant challenges in terms of resource management and operational efficiency. Outsourcing and remote working have become prevalent, as organizations seek to leverage specialized skills from around the world, but these practices also bring about challenges related to infrastructure accessibility and resource allocation.
Rynus addresses these challenges effectively through three key strategies:
- Support for All AI SDKs and Libraries: Rynus provides extensive support for a wide range of AI software development kits (SDKs) and libraries, ensuring compatibility with popular tools and frameworks used by AI professionals. This includes support for TensorFlow, PyTorch, Keras, and other major libraries, allowing developers to seamlessly integrate their existing workflows into the Rynus ecosystem. By accommodating diverse toolsets, Rynus simplifies the process of transitioning to their platform and enhances productivity for AI teams working with various technologies.
- Provision of Unlimited Powerful GPUs: One of the core strengths of Rynus is its ability to offer unlimited access to powerful GPUs. This scalability ensures that AI projects, regardless of their size and complexity, can be executed without constraints on computational resources. Whether it’s training large-scale deep learning models or performing intensive data analysis, Rynus’s infrastructure can handle the demands, providing the necessary computational power to meet project deadlines and performance benchmarks.
- Seamless Global Accessibility: Rynus ensures seamless global accessibility, enabling AI professionals to access high-performance computing resources from anywhere in the world. This global reach is crucial for teams that operate remotely or collaborate across different time zones. By eliminating geographical barriers, Rynus facilitates real-time collaboration and resource sharing, making it easier for remote teams to work together efficiently on AI projects.
Flexible Pricing Models to Serve Both Enterprises and Individuals
To cater to a broad spectrum of users, from large enterprises to individual developers, Rynus offers two flexible pricing models:
- Pay-As-You-Go: This model allows users to pay only for the computing power they actually use. It’s an ideal option for individuals or organizations with variable workloads, as it provides cost-efficiency by aligning expenses with actual usage. Users can scale their computing resources up or down based on project requirements without being locked into long-term commitments.
- Rental Plan: For users with more consistent and predictable workloads, Rynus offers rental plans. These plans provide access to computing resources at a fixed cost, making it easier to budget for long-term projects. This model is particularly beneficial for enterprises that need to maintain continuous operations and require a steady supply of computational power.
In summary, Rynus meets the diverse needs of these Requesters, enabling them to harness AI’s full potential while optimizing costs and efficiency.
3D Rendering Requester
3D rendering is the process of creating a photorealistic 2D image from 3D models using specialized rendering software. It is the final step in the process of 3D visualization, which involves creating models of objects, texturing those objects, and adding lighting to the scene, then generating the final photorealistic images based on the way the rendering software interprets the information.
3D rendering is an essential step used in many many fields:
- Architecture
- Film and Animation
- Motion Graphics
- Visual Effects (VFX)
- Product Design and Visualization
- 3D Games
- Metaverse: Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI)
- Automotive and Aerospace
- Robotics
The power of 3D rendering can bring even the most ambitious creative visions to life. However, the computing resources required can be a significant challenge, especially for large-scale projects.
That’s where Rynus comes in. Our decentralized GPU network provides independent professionals and large studios alike with unlimited graphics processing power, enabling creatives to push the boundaries of 3D rendering without limitations. From graphics artists to architects, studio modelers to mechanical engineers, and AR/VR/XR visionaries, our platform is designed for anyone who brings digital visuals to life. Whether you’re a student or a professional, working solo or as part of a small studio or large studio spanning every corner of the globe, if you rely on 3D rendering to turn your creative vision into reality, you’re our ideal requester.
Our platform is designed to support two key groups of 3D rendering requesters – Studios and Individuals.
1. Studios
Diverse Pipelines
In the 3D rendering industry, every studio has its own unique pipeline with varying degrees of complexity, shaped by its specific needs, workflows, and software preferences.
Some may rely on a single software like Blender. For example, Blender is the core and the only software used for all the steps in their pipeline from Modeling, Texturing, Shading, Rigging, Animating, Rendering to Final Editing.
A solely Blender production pipeline
While others may use a multitude of tools, with each stage utilizing a different software.
For example,
- 3D Modeling using Autodesk 3dsMax, Autodesk Fusion
- UV Unwrapping using RizomUV
- Texture Painting using Foundry Mari
- Creating Materials using Adobe Substance 3D
- Setting Dresing & Props using Blender
- Rigging using Autodesk Maya
- Animating using Maxon Cinema 4D
- to Rendering using Chaos V-Ray
- …..
- Color Grading using DaVinci Resolve
A complex rendering pipeline (not 100% practical)
This diversity in the pipeline makes it challenging to find a rendering solution that can accommodate the varying requirements of each studio.
How we fit: Our platform is designed to accommodate this complexity. We support all rendering engines regardless of what DCC application is used. Additionally, by providing a SaaS service, we handle all the software licenses as well as software compatibility seamlessly. This means that you can continue to use your studio’s preferred software and renderer, without having to adapt to a new system or compromise on your creative vision.
Outsourcing and remote working
The COVID-19 pandemic has accelerated the trend of decentralization in studios, leading to a more dispersed workforce and a greater reliance on remote collaboration. It’s likely that this trend will continue even after the pandemic has passed. As studios adapt to this trend, they are outsourcing talent and services from a wide range of locations. This means that instead of having a centralized team working together in a single location, studios are now relying on a dispersed workforce, with team members working from various locations around the world.
The workload can be divided globally, for example:
- USA: Project management, creative direction
- UK: Storyboarding, concept art, and character design
- India: 3D modeling, texturing, and lighting
- Brazil: Animation, rigging, and simulation
- South Korea: Compositing, visual effects, and final rendering
Remote working in 3D Rendering
However, this outsourcing model can present some challenges, particularly in the rendering step. Ensuring that rendering software and its version are compatible with previous modeling, animation, rigging, and simulation software and their version can be a significant hurdle. Moreover, this outsourcing model also means limited investment in powerful hardware, which can limit the studio’s ability to scale their rendering capabilities.
How we fit: We address the challenges mentioned above in three key ways:
- Software compatibility: We support all software versions and handle all their licenses, so you don’t have to worry about compatibility or license costs.
- Powerful hardware: Our platform provides access to countless powerful GPUs, optimized for 3D rendering, which increases rendering speed and eliminates hardware concerns.
- Global accessibility: As a decentralized platform with a network of workers covering the globe, you can access our services smoothly, without worrying about lag, no matter where you are in the world.
Using our services, you can focus on what matters most – creating high-quality content.
2. Individuals
In addition to studios, we also cater to individual creatives who require flexible and affordable 3D rendering solutions. Individual creatives include:
- Students: Looking for a rendering solution to complete their graduation project, enhance their skills, and build a portfolio, but may not have access to high-performance hardware or expensive software licenses.
- Freelancers: Working on projects that require occasional rendering, with varying requirements. Depending on project requirements, freelancers may use various software like Autodesk Maya, 3ds Max, Cinema 4D, Blender, etc. They often work under tight deadlines and budgets, but don’t have powerful hardware, making it challenging to deliver high-quality work on time.
- Indie artists: Creating personal projects or passion projects that require 3D rendering, but may not have the budget for traditional rendering services.
These individuals often face big challenges when it comes to accessing 3D rendering capabilities. Traditional rendering services can be expensive, while many Web3 platforms only support specific software. For example, the Render Network currently only supports Octane. This leaves many individual users without a viable solution.
To address these challenges, we provide access to unlimited powerful GPUs and a wide range of rendering software.
- Pay-as-you-go model: Only pay for the rendering power they need, when they need it. This means that users can easily adapt to changing project requirements, without being locked into expensive hardware investments.
- Choose their software: Select from a variety of rendering software, including those not supported by other Web3 platforms. This ensures that users can work with the engine they’re familiar with without worrying about its expensive license.
- Focus on creativity: Without worrying about hardware or software limitations, individual creatives can focus on bringing their ideas to life. Rynus handles the technical heavy lifting, so users can concentrate on the artistic and creative aspects of their project.
By democratizing access to 3D rendering, Rynus empowers individual creatives to produce high-quality work, regardless of their budget or technical expertise. With Rynus, the barriers to entry are lowered, and the possibilities for creative expression are endless.