Alternatives to Automaton AI

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality. Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset. Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.

Harness the power of AutoML Vision or leverage pre-trained Vision API models to extract meaningful insights from images stored in the cloud or at the network's edge, allowing for emotion detection, text interpretation, and much more. Google Cloud presents two advanced computer vision solutions that utilize machine learning to provide top-notch prediction accuracy for image analysis. You can streamline the creation of bespoke machine learning models by simply uploading your images, using AutoML Vision's intuitive graphical interface to train these models, and fine-tuning them for optimal performance in terms of accuracy, latency, and size. Once perfected, these models can be seamlessly exported for use in cloud applications or on various edge devices. Additionally, Google Cloud’s Vision API grants access to robust pre-trained machine learning models via REST and RPC APIs. You can easily assign labels to images, categorize them into millions of pre-existing classifications, identify objects and faces, interpret both printed and handwritten text, and enhance your image catalog with rich metadata for deeper insights. This combination of tools not only simplifies the image analysis process but also empowers businesses to make data-driven decisions more effectively.

The training data platform for AI teams. A machine learning model can only be as good as the training data it uses. Labelbox is an integrated platform that allows you to create and manage high quality training data in one place. It also supports your production pipeline with powerful APIs. A powerful image labeling tool for segmentation, object detection, and image classification. You need precise and intuitive image segmentation tools when every pixel is important. You can customize the tools to suit your particular use case, including custom attributes and more. The performant video labeling editor is for cutting-edge computer visual. Label directly on the video at 30 FPS, with frame level. Labelbox also provides per-frame analytics that allow you to create faster models. It's never been easier to create training data for natural language intelligence. You can quickly and easily label text strings, conversations, paragraphs, or documents with fast and customizable classification.

Manage unstructured data to develop AI solutions in record time. Enterprise-grade data platform with vision AI. Dataloop offers a single-stop-shop for building and deploying powerful data pipelines for computer vision, data labeling, automation of data operations, customizing production pipelines, and weaving in the human for data validation. Our vision is to make machine-learning-based systems affordable, scalable and accessible for everyone. Explore and analyze large quantities of unstructured information from diverse sources. Use automated preprocessing to find similar data and identify the data you require. Curate, version, cleanse, and route data to where it's required to create exceptional AI apps.

V7 Darwin is a data labeling and training platform designed to automate and accelerate the process of creating high-quality datasets for machine learning. With AI-assisted labeling and tools for annotating images, videos, and more, V7 makes it easy for teams to create accurate and consistent data annotations quickly. The platform supports complex tasks such as segmentation and keypoint labeling, allowing businesses to streamline their data preparation process and improve model performance. V7 Darwin also offers real-time collaboration and customizable workflows, making it suitable for enterprises and research teams alike.

The premier platform designed for the complete computer vision process allows you to evolve from image annotation to precise neural networks at speeds up to ten times quicker. Utilizing our exceptional data labeling tools, you can convert your images, videos, and 3D point clouds into top-notch training data. This enables you to train your models, monitor experiments, visualize results, and consistently enhance model predictions, all while constructing custom solutions within a unified environment. Our self-hosted option ensures data confidentiality, offers robust customization features, and facilitates seamless integration with your existing technology stack. This comprehensive solution for computer vision encompasses multi-format data annotation and management, large-scale quality control, and neural network training within an all-in-one platform. Crafted by data scientists for their peers, this powerful video labeling tool draws inspiration from professional video editing software and is tailored for machine learning applications and beyond. With our platform, you can streamline your workflow and significantly improve the efficiency of your computer vision projects.

A powerful labeling platform for all stages and types of ML development. We leverage a suite of industry-leading computer vision algorithms to automatically detect and classify the content of your images and videos. Creating detailed segmentation information is a time-consuming process. Machine assistance speeds up task completion by as much as 70%, saving you both time and money. We leverage ML to propose labels that accelerate human labeling. This includes computer vision models to automatically detect, localize, and classify entities in your images and videos before handing off the task to our workforce. Automatic labelling reduces workforce costs and allows annotators to spend their time on the more complicated steps of the annotation process. Our video annotation tool is built to handle 4K resolution and long-running videos natively and provides innovative features like interpolation, object proposal, and entity resolution.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

Appen combines the intelligence of over one million people around the world with cutting-edge algorithms to create the best training data for your ML projects. Upload your data to our platform, and we will provide all the annotations and labels necessary to create ground truth for your models. An accurate annotation of data is essential for any AI/ML model to be trained. This is how your model will make the right judgments. Our platform combines human intelligence with cutting-edge models to annotation all types of raw data. This includes text, video, images, audio and video. It creates the exact ground truth for your models. Our user interface is easy to use, and you can also programmatically via our API.

The entire process of machine learning and computer vision is streamlined and expedited through a single no-code platform. Sense empowers users to create and implement AI IoT solutions across various environments, whether in the cloud, at the edge, or on-premises. Discover how Sense delivers ease, consistency, and transparency for AI/ML teams, providing robust capabilities for machine learning engineers while remaining accessible for subject matter experts. With Sense Data Annotation, you can enhance your machine learning models by efficiently labeling video and image data, ensuring the creation of high-quality training datasets. The platform also features one-touch labeling integration, promoting ongoing machine learning at the edge and simplifying the management of all your AI applications, thereby maximizing efficiency and effectiveness. This comprehensive approach makes Sense an invaluable tool for a wide range of users, regardless of their technical background.

The best data will help you achieve peak model performance. Create and manage training data for any visual modality. Debug models, boost performance and make foundation models yours. Expert review, QA, and QC workflows will help you deliver better datasets to your artificial-intelligence teams, improving model performance. Encord's Python SDK allows you to connect your data and models, and create pipelines that automate the training of ML models. Improve model accuracy by identifying biases and errors in your data, labels, and models.

Intel® Geti™ software streamlines the creation of computer vision models through efficient data annotation and training processes. It offers features such as intelligent annotations, active learning, and task chaining, allowing users to develop models for tasks like classification, object detection, and anomaly detection without needing to write extra code. Furthermore, the platform includes optimizations, hyperparameter tuning, and models that are ready for production and optimized for Intel’s OpenVINO™ toolkit. Intended to facilitate teamwork, Geti™ enhances collaboration by guiding teams through the entire model development lifecycle, from labeling data to deploying models effectively. This comprehensive approach ensures that users can focus on refining their models while minimizing technical hurdles.

Revolutionary machine teaching is here with an exceptionally efficient annotation tool driven by active learning. Prodigy serves as a customizable annotation platform so effective that data scientists can handle the annotation process themselves, paving the way for rapid iteration. The advancements in today's transfer learning technologies allow for the training of high-quality models using minimal examples. By utilizing Prodigy, you can fully leverage contemporary machine learning techniques, embracing a more flexible method for data gathering. This will enable you to accelerate your workflow, gain greater autonomy, and deliver significantly more successful projects. Prodigy merges cutting-edge insights from the realms of machine learning and user experience design. Its ongoing active learning framework ensures that you only need to annotate those examples the model is uncertain about. The web application is not only powerful and extensible but also adheres to the latest user experience standards. The brilliance lies in its straightforward design: it encourages you to concentrate on one decision at a time, keeping you actively engaged – akin to a swipe-right approach for data. Additionally, this streamlined process fosters a more enjoyable and effective annotation experience overall.

Labellerr is a data annotation platform aimed at streamlining the creation of top-notch labeled datasets essential for AI and machine learning applications. It accommodates a wide array of data formats, such as images, videos, text, PDFs, and audio, addressing various annotation requirements. This platform enhances the labeling workflow with automated features, including model-assisted labeling and active learning, which help speed up the process significantly. Furthermore, Labellerr includes sophisticated analytics and intelligent quality assurance tools to maintain the precision and dependability of annotations. For projects that demand specialized expertise, Labellerr also provides expert-in-the-loop services, granting access to professionals in specialized domains like healthcare and automotive, thereby ensuring high-quality results. This comprehensive approach not only facilitates efficient data preparation but also builds trust in the reliability of the labeled datasets produced.

Your AI warrants top-notch data. With an impressive accuracy rate of 99%, you can eliminate the hassle of acquiring machine learning datasets using Tictag's innovative mobile data platform along with Truetag's rigorous quality control. Tictag’s pioneering mobile data platform integrates a user-friendly design with engaging, gamified features to generate high-quality datasets, all supported by our unique Truetag quality assurance system. This represents the pinnacle of technology-driven labeling. Tictag adeptly gathers and annotates even the most complex datasets with exceptional accuracy for AI and ML applications, ensuring rapid turnaround times. The process of data labeling has reached unprecedented levels of speed and simplicity. Complete it once and do it correctly; Tictag's technologically enhanced Truetag quality control guarantees that your data meets your specific requirements. Additionally, through Tictag, your data demands create opportunities for individuals seeking alternative income sources or aspiring to acquire new skills. Thus, Tictag not only enhances your AI capabilities but also contributes to skill development in the community.

Develop training datasets for computer vision models using our comprehensive management solution. We are convinced that the quality of data labeling plays a crucial role in crafting successful deep learning models. Our mission is to establish ourselves as the foremost data labeling platform in the industry, enabling businesses to fully leverage the potential of AI technology. Organize your media assets into distinct categories for better management. Highlight specific items of interest using one or multiple bounding boxes to enhance detection accuracy. Utilize bounding boxes with added precision for more detailed annotations. Provide accurate measurements of width, depth, and height for various objects. Classify every pixel in an image for fine-grained analysis. Identify and mark individual points to capture specific details within images. Annotate straight lines to assist in geometric assessments. Measure critical attributes like yaw, pitch, and roll for items of interest. Keep track of timestamps in both video and audio content for synchronization purposes. Additionally, annotate freeform lines in images to capture more complex shapes and designs, enhancing the depth of your data labeling efforts.

Introducing the ultimate data annotation tool that offers unparalleled flexibility and ease of installation. Users can create customized user interfaces or opt for ready-made labeling templates tailored to their specific needs. The adaptable layouts and templates seamlessly integrate with your dataset and workflow requirements. It supports various object detection methods in images, including boxes, polygons, circles, and key points, and allows for the segmentation of images into numerous parts. Additionally, machine learning models can be utilized to pre-label data and enhance efficiency throughout the annotation process. Features such as webhooks, a Python SDK, and an API enable users to authenticate, initiate projects, import tasks, and manage model predictions effortlessly. Save valuable time by leveraging predictions to streamline your labeling tasks, thanks to the integration with ML backends. Furthermore, users can connect to cloud object storage solutions like S3 and GCP to label data directly in the cloud. The Data Manager equips you with advanced filtering options to effectively prepare and oversee your dataset. This platform accommodates multiple projects, diverse use cases, and various data types, all in one convenient space. By simply typing in the configuration, you can instantly preview the labeling interface. Live serialization updates at the bottom of the page provide a real-time view of what Label Studio anticipates as input, ensuring a smooth user experience. This tool not only improves annotation accuracy but also fosters collaboration among teams working on similar projects.

Amazon SageMaker enables the identification of various types of unprocessed data, including images, text documents, and videos, while also allowing for the addition of meaningful labels and the generation of synthetic data to develop high-quality training datasets for machine learning applications. The platform provides two distinct options, namely Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which grant users the capability to either leverage a professional workforce to oversee and execute data labeling workflows or independently manage their own labeling processes. For those seeking greater autonomy in crafting and handling their personal data labeling workflows, SageMaker Ground Truth serves as an effective solution. This service simplifies the data labeling process and offers flexibility by enabling the use of human annotators through Amazon Mechanical Turk, external vendors, or even your own in-house team, thereby accommodating various project needs and preferences. Ultimately, SageMaker's comprehensive approach to data annotation helps streamline the development of machine learning models, making it an invaluable tool for data scientists and organizations alike.

Klatch Technologies is a global provider of data services that helps companies and institutions collect and annotate data. We support Artificial Intelligence companies, research institutes, Machine Learning and Computer Vision projects in data labeling. Our specialists provide high-quality data security, rapid scalability and accuracy, as well as multilingual capability and quick turnaround time. Data Annotation Services Image Annotation Video Annotation Search Relevance Annotation for Text NLP Text classification Sentiment Analysis Image Segmentation LIDAR Annotation - Data collection services: Healthcare Training Data Chatbot Training Data All other data collection requirements IT Managed Services Moderation of Content Ecommerce Data Categorization

Anolytics specializes in providing data annotation services for images, videos, and text, specifically tailored for machine learning and AI-driven computer vision applications. Their offerings include an economical annotation service aimed at facilitating the development of machine learning and artificial intelligence models. By utilizing various annotation techniques, Anolytics ensures that the data is accurately and precisely annotated, whether in text, image, or video formats. The company excels in Image Annotation, Video Annotation, and Text Annotation, maintaining high standards of accuracy throughout the process. Anolytics delivers a comprehensive range of data annotation services essential for training in both machine learning and deep learning environments. Their services encompass Bounding Boxes, Semantic Segmentation, 3D Point Cloud Annotation, and 3D Cuboid Annotation, catering to diverse industries such as healthcare, autonomous driving, drone operations, retail, security surveillance, and agriculture. With a focus on scalability, Anolytics ensures its solutions are available with rapid turnaround times and competitive pricing for clients around the world, thereby enhancing their accessibility and effectiveness in various applications. This commitment to quality and efficiency positions Anolytics as a leader in the data annotation industry.

We guarantee top-notch service level agreements (SLAs) exceeding 95%, even for the most intricate workflows. Our dedicated team is on hand to assist with everything, from establishing a solid quality evaluation framework to addressing unique edge cases. As a socially responsible AI organization, we have created economic opportunities for more than 52,000 individuals from underrepresented and disadvantaged backgrounds. Through machine learning-assisted annotation, we achieve efficiency improvements of up to four times for single-class tasks. Our agile approach allows us to swiftly adjust to changes in project demands, focus shifts, and unforeseen challenges. Our ISO-certified delivery centers, along with biometric and two-factor authentication, ensure a secure operational environment. We facilitate the seamless reorganization of tasks, offer constructive feedback, and oversee models in active use. Our services encompass all data types, enabling you to achieve more with fewer resources. By integrating machine learning with human oversight, we meticulously filter data and curate images that align with your specific requirements. You will receive example results that adhere to your initial criteria, and we will collaborate with you to pinpoint edge cases while suggesting optimal annotation practices. Additionally, our commitment to quality ensures that every step of the process enhances the overall effectiveness of your project.

SuperAnnotate is the best platform to build high-quality training datasets for NLP and computer vision. We enable machine learning teams to create highly accurate datasets and successful pipelines of ML faster with advanced tooling, QA, ML, and automation features, data curation and robust SDK, offline accessibility, and integrated annotation services. We have created a unified annotation environment by bringing together professional annotators and our annotation tool. This allows us to provide integrated software and services that will lead to better quality data and more efficient data processing.

Effectively oversee the complete AI pipeline, beginning with raw data and culminating in its deployment. Develop tailored models that align closely with your operational needs, providing a strategic edge in the marketplace. Foster a varied AI team capable of constructing and launching AI solutions effortlessly, without requiring coding skills. Implement AI solutions in diverse settings, whether on a manufacturing floor, in space exploration, or anywhere else. Commit to a reliable platform that has been successfully utilized in highly sensitive data environments. Utilize guided workflows to assist you in creating your initial model. Instead of separating enterprise data across various cloud services and hardware, consolidate all assets into a single, well-organized library that enhances ease of discovery for users. This holistic approach not only streamlines processes but also maximizes the potential for innovation and efficiency across your organization.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

We make data for the world's most valuable companies. Innodata solves your most difficult data engineering problems using artificial intelligence and human expertise. Innodata offers the services and solutions that you need to harness digital information at scale and drive digital disruption within your industry. We secure and efficiently collect and label sensitive data. This provides ground truth that is close to 100% for AI and ML models. Our API is simple to use and ingests unstructured data, such as contracts and medical records, and generates structured XML that conforms to schemas for downstream applications and analytics. We make sure that mission-critical databases are always accurate and up-to-date.

Deepen AI provides cutting-edge tools and services for multi-sensor data labeling and calibration, aimed at enhancing the training process for computer vision applications in autonomous vehicles, robotics, and beyond. Their annotation suite addresses numerous critical use cases, which include 2D and 3D bounding boxes, semantic and instance segmentation, polylines, and key points. Powered by artificial intelligence, the platform boasts pre-labeling features that can automatically tag up to 80 commonly used classes, resulting in a productivity boost of seven times. Additionally, it incorporates machine learning-assisted segmentation, enabling users to segment objects effortlessly with minimal clicks, alongside precise object detection and tracking across frames to eliminate redundancy and conserve time. Furthermore, Deepen AI’s calibration suite accommodates all essential sensor types, such as LiDAR, cameras, radar, IMUs, and vehicle sensors. These sophisticated tools facilitate seamless visualization and inspection of the integrity of multi-sensor data, while also allowing for the rapid calculation of intrinsic and extrinsic calibration parameters in mere seconds. By streamlining these processes, Deepen AI empowers developers to focus more on innovation and less on manual data handling.

Enhance the platform to incorporate annotation capabilities specifically for segmentation tasks. Within the Zastra repository, innovative algorithms will facilitate segmentation processes to bolster active learning for various datasets. Comprehensive end-to-end ML operations will be implemented, complete with version control for datasets and experiments, alongside templated pipelines that enable model deployment across standard cloud environments and edge devices. By integrating advancements in Bayesian deep learning into the active learning framework, we aim to elevate the overall performance. Moreover, we will refine the accuracy of annotations using specialized architectures, such as Bayesian CNNs, ensuring superior results. Our dedicated team has invested extensive time and effort into developing this groundbreaking solution tailored for your needs. Though we are continuously enhancing the platform with new features, we eagerly invite you to experience a trial run! Zastra boasts a range of core functionalities, including active learning for object classification, detection, localization, and segmentation, applicable across various formats like images, videos, audio, text, and point cloud data. This versatility positions Zastra as a comprehensive tool to tackle diverse data challenges effectively.

Your AI Data Platform High Quality Training Data for Enterprise Data Labeling Software for Machine Learning Your Kubernetes Cluster up to 3 users is free TRUSTED BY 5,000 HAPPY UBERS WORLDWIDE Images, Video, and Text Spatial Tools Quadratic Curves and Cuboids, Segmentation Box, Polygons and Lines, Keypoints, Classification tags, and More You can use the exact spatial tool that you need. All tools are easy-to-use, editable, and offer powerful ways to present your data. All tools are available as Video. Attribute Tools More Meaning. More freedom through: Radio buttons Multiple selection. Date pickers. Sliders. Conditional logic. Directional vectors. Plus, many more! Complex knowledge can be captured and encoded into your AI. Streaming Data Automation Manual labeling can be up to 10x faster than automated labeling

Our AI Training Data Platform combines the best of data annotators and computer vision capabilities, with the power of the AI Community of professional annotations. GT Manage: Our proprietary platform manager for our 1M+ community. GT Annotate is our proprietary data annotation software. GT Data: Our global expertise for data collection and creation. Human-powered AI is the foundation of all AI. Our fully automated platform allows sophisticated data annotation across data types, all within the same software. It also provides seamless project and AI Community Management. Ground Truth (GT Annotate) is our proprietary software for data annotation. It was carefully designed to allow teams to create quality AI training datasets quickly and accurately. Below are some examples of how the technology is used.

For tasks such as transcription, data validation, classification, sentiment analysis, and more, UHRS offers comprehensive solutions tailored to your needs. We leverage human intelligence to enhance machine learning models, aiding you in overcoming some of your toughest challenges. Judges can conveniently access UHRS from anywhere at any time with just an internet connection. This streamlined access allows for quick engagement with tasks like video annotation within minutes. With UHRS, managing the classification of thousands of images becomes a straightforward and efficient process. Our platform enables the training of your products and tools through high-quality annotated image data, enhancing capabilities like image detection and boundary recognition. You can efficiently classify images, conduct semantic segmentation, and implement object detection. In addition, we facilitate audio-to-text validation, conversation analysis, and relevance checks. Furthermore, our services extend to sentiment identification for tweets, document classification, and various ad hoc data collection tasks, including information correction, moderation, and conducting surveys. With UHRS, you gain a versatile partner in navigating a wide range of data-related challenges.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

Innotescus is an image and video annotation platform that enables collaboration and data handling. It streamlines Computer Vision development through intuitive collaboration features, smart annotation tools and seamless data handling. Its data visualization tools and cross functional collaboration features help to identify data bias early and improve data accuracy. This allows for faster and more cost-efficient deployments of high-performance Artificial Intelligence.

Neuralhub is a platform designed to streamline the process of working with neural networks, catering to AI enthusiasts, researchers, and engineers who wish to innovate and experiment in the field of artificial intelligence. Our mission goes beyond merely offering tools; we are dedicated to fostering a community where collaboration and knowledge sharing thrive. By unifying tools, research, and models within a single collaborative environment, we strive to make deep learning more accessible and manageable for everyone involved. Users can either create a neural network from the ground up or explore our extensive library filled with standard network components, architectures, cutting-edge research, and pre-trained models, allowing for personalized experimentation and development. With just one click, you can construct your neural network while gaining a clear visual representation and interaction capabilities with each component. Additionally, effortlessly adjust hyperparameters like epochs, features, and labels to refine your model, ensuring a tailored experience that enhances your understanding of neural networks. This platform not only simplifies the technical aspects but also encourages creativity and innovation in AI development.

Keylabs.ai is an image and video annotation platform built by annotation experts to deliver high-performance data annotation and management features and unique operations management. Its tools have a proven track record of handling large datasets efficiently and accurately. Trusted by global technology leaders, Keylabs.ai combines innovative technology with user-focused design to deliver solutions to projects of any type and size.

The NVIDIA GPU-Optimized AMI serves as a virtual machine image designed to enhance your GPU-accelerated workloads in Machine Learning, Deep Learning, Data Science, and High-Performance Computing (HPC). By utilizing this AMI, you can quickly launch a GPU-accelerated EC2 virtual machine instance, complete with a pre-installed Ubuntu operating system, GPU driver, Docker, and the NVIDIA container toolkit, all within a matter of minutes. This AMI simplifies access to NVIDIA's NGC Catalog, which acts as a central hub for GPU-optimized software, enabling users to easily pull and run performance-tuned, thoroughly tested, and NVIDIA-certified Docker containers. The NGC catalog offers complimentary access to a variety of containerized applications for AI, Data Science, and HPC, along with pre-trained models, AI SDKs, and additional resources, allowing data scientists, developers, and researchers to concentrate on creating and deploying innovative solutions. Additionally, this GPU-optimized AMI is available at no charge, with an option for users to purchase enterprise support through NVIDIA AI Enterprise. For further details on obtaining support for this AMI, please refer to the section labeled 'Support Information' below. Moreover, leveraging this AMI can significantly streamline the development process for projects requiring intensive computational resources.

Artificio is a groundbreaking automation solution created by Artificio Products Inc, aimed at transforming the process of data management and removing the need for manual data entry. This advanced software leverages the latest advancements in AI and machine learning technologies to extract, categorize, verify, and merge unstructured data from various formats such as text documents, PDFs, and images. By turning unstructured information into structured formats, Artificio enables organizations to fully harness the capabilities of digital intelligence, ultimately enhancing decision-making and operational efficiency. As businesses increasingly rely on data-driven insights, tools like Artificio become essential for staying competitive in a rapidly evolving landscape.

GPUs excel at swiftly transferring data but suffer from limited locality of reference due to their relatively small caches, which makes them better suited for scenarios that involve heavy computation on small datasets rather than light computation on large ones. Consequently, the networks optimized for GPU architecture tend to run in layers sequentially to maximize the throughput of their computational pipelines (as illustrated in Figure 1 below). To accommodate larger models, given the GPUs' restricted memory capacity of only tens of gigabytes, multiple GPUs are often pooled together, leading to the distribution of models across these units and resulting in a convoluted software framework that must navigate the intricacies of communication and synchronization between different machines. In contrast, CPUs possess significantly larger and faster caches, along with access to extensive memory resources that can reach terabytes, allowing a typical CPU server to hold memory equivalent to that of dozens or even hundreds of GPUs. This makes CPUs particularly well-suited for a brain-like machine learning environment, where only specific portions of a vast network are activated as needed, offering a more flexible and efficient approach to processing. By leveraging the strengths of CPUs, machine learning systems can operate more smoothly, accommodating the demands of complex models while minimizing overhead.

Mindkosh is your premier data management platform, streamlining the curation, tagging, and verification of datasets for AI initiatives. Our top-tier data annotation platform merges team-oriented functionalities with AI-enhanced annotation tools, delivering an all-encompassing toolkit for categorizing diverse data types, including images, videos, and 3D point clouds from Lidar. For images, Mindkosh offers advanced semi-automated segmentation, pre-labeling of bounding boxes, and completely automatic OCR capabilities. For video annotation, Mindkosh's automated interpolation significantly reduces the need for manual labeling. And for Lidar data, single-click annotation enables swift cuboid generation with just one click. If you are simply looking to get your data labeled, our high quality data annotation services combined with an easy to use Python SDK and web-based review platform, provide an unmatched experience.

Toloka AI was founded in 2014 after years spent research and experimentation. It is an open platform for collecting data and annotating it. There are over 20 000+ active monthly performers in over 100+ countries. They speak 40+ languages and generate approximately 80 million data annotations each week. Toloka is used by R&D, banking and autonomous vehicles as well as other organizations to generate machine-learning data at scale. It also harnesses the wisdom of the crowd from all over the globe. Gartner ranked Toloka as one of the most notable data labeling solutions in the market in its Hype Cycle for Data Science & ML report.

Our company is the premier provider of data labeling services in AI. We cover industries like Automotive, Robotics, Fintech, Healthcare, E-commerce, Manufacturing, and Insurance. With over 500+ annotation gurus and multilingual support, we provide efficient and accurate services for both enterprise and R&D projects. Our range of services includes Computer Vision and NLP annotation, data processing, and more. Choose Label Your Data for unparalleled quality of your training datasets. Our top priority is to ensure the security and quality of client's data. To ensure the confidentiality of client data, our facilities and teams have been certified by ISO 27001 and PCI DSS (level 1). We adhere to the most important industry security standards, such as GDPR and CCPA.

Enhance your annotation process with CVAT, the premier data engine for machine learning that is relied upon by teams of all sizes and for datasets of any magnitude. Featuring a lightning-fast and user-friendly interface crafted in collaboration with actual teams tackling real-world challenges, CVAT is the go-to choice for ambitious AI innovators across diverse sectors, including healthcare, retail, and autonomous driving. Each day, leading AI teams integrate CVAT into their workflows, ensuring their projects run smoothly regardless of the nature of the input data or the outcomes they aim to achieve. CVAT excels with a variety of formats such as images, videos, and 3D models, offering a wide array of annotation tools including bounding boxes, polygons, points, skeletons, cuboids, and trajectories. Boost your productivity with advanced interactive algorithms like intelligent scissors and histogram equalization, designed to streamline the annotation experience. Additionally, CVAT provides valuable metrics such as annotator working hours and objects annotated per hour, enabling teams to optimize their efforts and achieve better results. With its comprehensive features and flexibility, CVAT stands out as an essential asset for any AI-driven project.

clickworker is globally the largest open crowd sourcing provider. The company has a huge number of services using a "one to many" approach where your company can use many Clickworkers to achieve the outcome you desire. Most frequently, clickworker provides customized data collection, categorization, evaluation, tagging and annotation services to create AI/ML training data for Data Scientists, and also provides SEO texts, product tags, categories and surveys for online businesses and retailers. clickworker serves most industries and applications using the skills of their 4.0M+ Clickworkers. This crowd gathers data through a wide range of micro-tasks, utilizing a sophisticated crowd-sourcing platform and fully featured mobile app.

Deep learning frameworks like TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have significantly enhanced the accessibility of deep learning by simplifying the design, training, and application of deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) offers a standardized method for deploying these deep-learning frameworks as a service on Kubernetes, ensuring smooth operation. The architecture of FfDL is built on microservices, which minimizes the interdependence between components, promotes simplicity, and maintains a stateless nature for each component. This design choice also helps to isolate failures, allowing for independent development, testing, deployment, scaling, and upgrading of each element. By harnessing the capabilities of Kubernetes, FfDL delivers a highly scalable, resilient, and fault-tolerant environment for deep learning tasks. Additionally, the platform incorporates a distribution and orchestration layer that enables efficient learning from large datasets across multiple compute nodes within a manageable timeframe. This comprehensive approach ensures that deep learning projects can be executed with both efficiency and reliability.

Effortlessly create, refine, and deploy high-performing, precise models using Deci’s deep learning development platform, which utilizes Neural Architecture Search. Achieve superior accuracy and runtime performance that surpass state-of-the-art models for any application and inference hardware in no time. Accelerate your path to production with automated tools, eliminating the need for endless iterations and a multitude of libraries. This platform empowers new applications on devices with limited resources or helps reduce cloud computing expenses by up to 80%. With Deci’s NAS-driven AutoNAC engine, you can automatically discover architectures that are both accurate and efficient, specifically tailored to your application, hardware, and performance goals. Additionally, streamline the process of compiling and quantizing your models with cutting-edge compilers while quickly assessing various production configurations. This innovative approach not only enhances productivity but also ensures that your models are optimized for any deployment scenario.

Your A.I. can be created using the Edgecase Platform In less than one day, your A.I. team can create 100k labeled photos -Data accuracy is guaranteed to be perfect because it is generated from 3D models and real life blended imagery. Data accuracy is no longer a concern -Each model can be modified, including the camera angle. You can change lighting, textures, camera angles, scene types, and more. All accessible via the cloud - Your A.I. Your existing data can be used to create your own datasets. We also have a large library of 3d hyper-realistic models that you can use to create your own.

Superb AI introduces a cutting-edge machine learning data platform designed to empower AI teams to develop superior AI solutions more efficiently. The Superb AI Suite functions as an enterprise SaaS platform tailored for ML engineers, product developers, researchers, and data annotators, facilitating streamlined training data workflows that conserve both time and financial resources. Notably, a significant number of ML teams allocate over half of their efforts to managing training datasets, a challenge that Superb AI addresses effectively. Customers utilizing our platform have experienced an impressive 80% reduction in the time required to commence model training. With a fully managed workforce, comprehensive labeling tools, rigorous training data quality assurance, pre-trained model predictions, advanced auto-labeling capabilities, and efficient dataset filtering and integration, Superb AI enhances the data management experience. Furthermore, our platform offers robust developer tools and seamless ML workflow integrations, making training data management simpler and more efficient than ever before. With enterprise-level features catering to every aspect of an ML organization, Superb AI is revolutionizing the way teams approach machine learning projects.

The NVIDIA Deep Learning GPU Training System (DIGITS) empowers engineers and data scientists by making deep learning accessible and efficient. With DIGITS, users can swiftly train highly precise deep neural networks (DNNs) tailored for tasks like image classification, segmentation, and object detection. It streamlines essential deep learning processes, including data management, neural network design, multi-GPU training, real-time performance monitoring through advanced visualizations, and selecting optimal models for deployment from the results browser. The interactive nature of DIGITS allows data scientists to concentrate on model design and training instead of getting bogged down with programming and debugging. Users can train models interactively with TensorFlow while also visualizing the model architecture via TensorBoard. Furthermore, DIGITS supports the integration of custom plug-ins, facilitating the importation of specialized data formats such as DICOM, commonly utilized in medical imaging. This comprehensive approach ensures that engineers can maximize their productivity while leveraging advanced deep learning techniques.

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

DeepCube is dedicated to advancing deep learning technologies, enhancing the practical application of AI systems in various environments. Among its many patented innovations, the company has developed techniques that significantly accelerate and improve the accuracy of training deep learning models while also enhancing inference performance. Their unique framework is compatible with any existing hardware, whether in data centers or edge devices, achieving over tenfold improvements in speed and memory efficiency. Furthermore, DeepCube offers the sole solution for the effective deployment of deep learning models on intelligent edge devices, overcoming a significant barrier in the field. Traditionally, after completing the training phase, deep learning models demand substantial processing power and memory, which has historically confined their deployment primarily to cloud environments. This innovation by DeepCube promises to revolutionize how deep learning models can be utilized, making them more accessible and efficient across diverse platforms.