Alternatives to Nomic Atlas

Transparency, choice and control are key to trust. Organizations have the opportunity to leverage these moments to build trust, and provide more valuable experiences. People expect greater control over their data. We offer privacy and data governance automation to help organizations better understand and comply with regulatory requirements. We also operationalize risk mitigation to ensure transparency and choice for individuals. Your organization will be able to achieve data privacy compliance quicker and build trust. Our platform helps to break down silos between processes, workflows, teams, and people to operationalize regulatory compliance. It also allows for trusted data use. Building proactive privacy programs that are rooted in global best practice and not just reacting to individual regulations is possible. To drive mitigation and risk-based decision-making, gain visibility into unknown risks. Respect individual choice and integrate privacy and security by default in the data lifecycle.

Artificial intelligence long-term memory The Pinecone vector database makes building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

Manta is a unified data lineage platform that serves as the central hub of all enterprise data flows. Manta can construct lineage from report definitions, custom SQL code, and ETL workflows. Lineage is analyzed based on actual code, and both direct and indirect flows can be visualized on the map. Data paths between files, report fields, database tables, and individual columns are displayed to users in an intuitive user interface, enabling teams to understand data flows in context.

Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values.

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

Chroma is an AI-native, open-source embedding system. Chroma provides all the tools needed to embeddings. Chroma is creating the database that learns. You can pick up an issue, create PRs, or join our Discord to let the community know your ideas.

One tool allows you to operationalize your embeddings. A comprehensive database that provides embedding functionality previously unavailable on multiple platforms is now available to you. Embeddinghub makes it easy to accelerate your machine learning. Embeddings are dense numerical representations of real world objects and relationships. They can be expressed as vectors. They are often created by first defining an unsupervised machine learning problem, also known as a "surrogate issue". Embeddings are intended to capture the semantics from the inputs they were derived. They can then be shared and reused for better learning across machine learning models. This is possible with Embeddinghub in an intuitive and streamlined way.

Weaviate is an open source vector database. It allows you to store vector embeddings and data objects from your favorite ML models, and scale seamlessly into billions upon billions of data objects. You can index billions upon billions of data objects, whether you use the vectorization module or your own vectors. Combining multiple search methods, such as vector search and keyword-based search, can create state-of-the art search experiences. To improve your search results, pipe them through LLM models such as GPT-3 to create next generation search experiences. Weaviate's next generation vector database can be used to power many innovative apps. You can perform a lightning-fast, pure vector similarity search on raw vectors and data objects. Combining keyword-based and vector search techniques will yield state-of the-art results. You can combine any generative model with your data to do Q&A, for example, over your dataset.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

AtlasMap is a data mapping tool with an interactive web-based user interface. It makes it easy to integrate Java, CSV, and JSON data sources. AtlasMap Data Mapper Canvas allows you to design your data mapping and then run it via runtime engine. Apache Camel route also includes a camel-atlasmap Component that allows data mapping to be performed in plain Java API. Camel Quarkus is also available. You can use AtlasMap Data Mapper User Interface in its standalone mode. You can also use it with a VS Code plug-in. The AtlasMap Data Mapper UI was originally designed to work with Syndesis UI. It's still the best way to enjoy all the benefits of integrated typed data maps with UI. Follow the Syndesis Developer Handbook to install and run Syndesis. After selecting or adding a Data Mapper integration, you will find the AtlasMap Data Mapper User Interface under the Integrations Panel.

Use user feedback and semantic relevance to reliably retrieve optimal document chunks for your retrieval-augmented generation system. In your search system, combine semantic relevance with document freshness because recent results are more accurate. Create a personalized ecommerce feed in real-time using user vectors based on the SKU embeddings that were viewed by the user. A vector index in your warehouse can be used to discover behavioral clusters among your customers. Use spaces to build your indices, and run queries all within a Python Notebook.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Marqo is a complete vector search engine. It's more than just a database. A single API handles vector generation, storage and retrieval. No need to embed your own embeddings. Marqo can accelerate your development cycle. In just a few lines, you can index documents and start searching. Create multimodal indexes, and search images and text combinations with ease. You can choose from a variety of open-source models or create your own. Create complex and interesting queries with ease. Marqo allows you to compose queries that include multiple weighted components. Marqo includes input pre-processing and machine learning inference as well as storage. Marqo can be run as a Docker on your laptop, or scaled up to dozens GPU inference nodes. Marqo is scalable to provide low latency searches on multi-terabyte indices. Marqo allows you to configure deep-learning models such as CLIP for semantic meaning extraction from images.

Self-service operational analytics and ready to-run reports across core processes empower your business to answer the most important questions. Imagine if you could get a better understanding of your organization's activities. You could not only report on the events but also react immediately to insights from your supply chain, finance, and manufacturing processes. You can adapt to the changing business landscape by changing the way you react. Magnitude Angles enables you to uncover hidden insights in your SAP ERP system or Oracle ERP system. It also streamlines the data analysis process. Traditional BI tools can only understand rows, columns, and tables, but not orders, cash, or materials. Angles is built upon a context-aware and process-rich business model that transforms complex ERP data architectures into self service business analytics. This allows data to be closer to decision making and helps turn insight into action.

Feedonomics is the #1 full-service product feed platform. Easily publish optimized product listings on hundreds of ad channels and marketplaces, such as Google Shopping, Facebook, Amazon, eBay, Walmart, and more. As a leading product feed management platform, Feedonomics partners with agencies, brands, and retailers to optimize and list their products on the top ecommerce shopping destinations around the world. What makes us different from other SaaS companies in the space? We manage everything from integration and full-service onboarding to ongoing feed maintenance for our clients, enabling them to expand their ecommerce business more efficiently. Since its inception in 2014, Feedonomics has developed an incredible reputation for our full-service approach to feed management, our inclusive culture, and our industry-leading technology. With principles that go beyond optics, Feedonomics looks to deliver the same people-first strategy internally as we do externally.

Aggua is an AI platform with augmented data fabric that gives data and business teams access to their data. It creates Trust and provides practical Data Insights for a more holistic and data-centric decision making. With just a few clicks, you can find out what's happening under the hood of your data stack. You can access data lineage, cost insights and documentation without interrupting your data engineer's day. With automated lineage, data engineers and architects can spend less time manually tracing what data type changes will break in their data pipelines, tables, and infrastructure.

Talend Data Integration allows you to connect and manage all of your data regardless of where it is located. Connect virtually any data source to any data environment using over 1,000 connectors and component. Drag-and-drop interface makes it easy to create and deploy reusable data pipes. It's 10x faster than hand-coding. Talend has been a leader in scaling large data sets to advanced data analytics and Spark platforms. We partner with top cloud service providers, data warehouses and analytics platforms such as Amazon Web Services, Microsoft Azure and Google Cloud Platform, Snowflake and Databricks. Talend ensures data quality at every stage of data integration. Before inconsistencies disrupt or impact critical decisions, you can identify, highlight, and fix them as data moves through your systems. Connect to data wherever it is, and use it where you want it.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

MyScale is a cutting-edge AI database that combines vector search with SQL analytics, offering a seamless, fully managed, and high-performance solution. Key features of MyScale include: - Enhanced data capacity and performance: Each standard MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, delivering over 150 QPS. - Swift data ingestion: Ingest up to 5 million data points in under 30 minutes, minimizing wait times and enabling faster serving of your vector data. - Flexible index support: MyScale allows you to create multiple tables, each with its own unique vector indexes, empowering you to efficiently manage heterogeneous vector data within a single MyScale cluster. - Seamless data import and backup: Effortlessly import and export data from and to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, you can harness the power of advanced AI database capabilities for efficient and effective data analysis.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

Vald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage.

End-users often use PST files as personal email archives. They are often scattered across end-user devices and network storage which makes it difficult to find and manage them consistently and effectively. Your users no longer have to save data locally in PST files thanks to Microsoft Exchange and Office 365. You will still need to manage legacy PST files, which contain important data. Barracuda PST Enterprise was created to address this problem. Many terabytes may be stored on PST files on end-user devices or on network servers. These files are notoriously unstable and easy to misplace, and are often corrupted. These large files can be costly in terms of both IT administrative overhead and system resources.

In less than 2 minutes, you can create a backend. Get a Postgres database, authentication and instant APIs to start your project. Real-time subscriptions are also available. You can build faster and concentrate on your products. Every project is a Postgres database, the most trusted relational database in the world. You can add user sign-ups or logins to secure your data with Row Level Security. Large files can be stored, organized and served. Any media, including images and videos. Without the need to deploy or scale servers, you can write custom code and cron jobs. There are many starter projects and example apps to help you get started. We will instantly inspect your database and provide APIs. Stop creating repetitive CRUD endpoints. Instead, focus on your product. Type definitions directly from your database schema. Supabase can be used in the browser without a build. You can develop locally and push to production as soon as you are ready. You can manage Supabase projects on your local machine.

Your AI deserves only the best data. Our unique mobile data platform, Truetag quality control and industry-leading 99% accuracy will take the stress out getting your machine learning datasets onto Tictag. Tictag’s mobile data platform is the first of its kind. It combines a user friendly interface with gamified features to produce high-quality datasets. This is technology enhanced labeling at its finest. Tictag collects and labels complex and intricate datasets with near-100% precision for AI and ML in short turnaround times. Data labeling is now faster and easier than ever. Do it right the first time. Truetag's technology-assisted quality control by Tictag ensures that your data is exactly what you need. Tictag helps people who are looking for a new source of income or a way to improve their skills by supplying them with the data they need.

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

Superb AI offers a new generation of machine learning data platform to AI team members so they can create better AI in a shorter time. The Superb AI Suite, an enterprise SaaS platform, was created to aid ML engineers, product teams and data annotators in creating efficient training data workflows that save time and money. Superb AI can help ML teams save more than 50% on managing training data. Our customers have averaged a 80% reduction in the time it takes for models to be trained. Fully managed workforce, powerful labeling and training data quality control tools, pre-trained models predictions, advanced auto-labeling and filtering your datasets, data source and integration, robust developer tools, ML work flow integrations and many other benefits. Superb AI makes it easier to manage your training data. Superb AI provides enterprise-level features to every layer of an ML organization.

Adeptia Connect assists enterprises in streamlining and speeding up their data onboarding processes by up to 80%. This makes it easy for them to do business with. Adeptia Connect allows business users to access data through a self-service model, accelerating service delivery, and boosting revenues.

The WSO2 integration engine can play multiple roles in your enterprise architecture. It can be used as an ESB and a microservices integrator. It can be deployed as an ESB to cater to your message routing and transformation, message mediation, service orchestration, and service and API hosting requirements. You can use header-based and content-based, rule-based and priority-based routing. Implement Enterprise Integration Patterns (EIPs), data integration, and event stream integrations. Transform messages using XSLT 1.0/2.0 and XPath. Visual data mapping, CSV and JSON, as well as XML transformation connectors. Any RDBMS, CSV and Excel, ODS Cassandra, Google spreadsheets, and ODS. OData v4 protocol supports any RDBMS or Cassandra data source. Supports MSSQL, DB2, Oracle, OpenEdge, TerraData, MySQL, PostgreSQL/EnterpriseDB, H2, Derby or any database with a JDBC driver. Support for nested queries across data sources.

Data comes from all directions, increasing in complexity and scale. Data can contain knowledge and insight that are full of potential. This potential can only be fully realized when it is integrated into every decision and action taken by the organization, second by second. Data changes with the business, which leads to new insights and knowledge. It is a cycle. Learn and adapt. Industries such as entertainment, financial services, healthcare, telecommunications and manufacturing have all recognized the potential. It is both challenging as well as exciting to get there. It takes new levels of speed, agility, and speed to understand, manage, and process vast amounts of constantly changing data. Complex organizations need a high-performance data platform that can automate and provide self-service. It can also thrive in change and adapt to new realities.

Our fully managed solution makes it easy to create training datasets for computer-vision models. Data labeling is a key factor in creating effective deep learning models. We aim to be the industry's most trusted data labeling platform, helping companies fully take advantage of AI's potential. You can organize your media using discrete categories. You can identify items of interest using one or more bounding boxes. Similar to bounding boxes but with more precision. You can annotate objects with precise width, depth, height. Each pixel in an image should be classified. Each point in an image should be marked. Annotate straight lines within an image. Measure, yaw and pitch the item of interest. Annotate timestamps in audio and video content. Annotate lines that are not defined in an image.

Your company can think data automation "first" to make digital transformation a reality. Data Automation allows you to better serve your customers. Data Automation allows you to work more efficiently. It frees employees from repetitive, mundane work and allows them to concentrate on solving problems and creating value. You can accelerate data projects by allowing non-IT data experts and data specialists to consolidate, categorize and master data. Digital transformations can be made easier by automating large amounts of data management via human-guided machinelearning. You can increase the speed of business outcomes by leveraging accurate, complete data that is automatically curated for data visualization tools or analytic insights. It integrates with your company's core applications and existing applications and automates data management and communication. Data Automation by Io-Tahoe automates repetitive, high-volume data tasks quickly once it is in place.

Liquid Data Mapper, a flexible data transformation and map tool, allows you to create any combination of mapping. It can even combine data from multiple sources. This tool allows you to visualize data mapping between multiple data sources. You can either execute transform within Liquid Studio, or generate mapping code to use in your own applications. Step through the debugger with breakpoints and stack trace, state, variable watch windows, and state. Liquid Studio Data Mapper is an intuitive interface that allows you to design data transforms. Transforms can be used to map multiple data sources to multiple data output targets. They also perform conditional logic, filtering and string manipulation.

A subscription includes everything needed for high-volume data replication or integration. Log-Based Change Data Capture and a unique compression algorithm ensure low-impact data movement, even at high volumes. RESTful APIs allow workflow automation, streamlining and time savings. HVR offers a variety security features. It also allows data routing through a firewall proxy for hybrid environments. Multi- and bidirectional data movement is supported, giving you the freedom and flexibility to optimize your data flows. All you need to complete your data replication project are included in one license. To ensure customer success, we provide in-depth training, support and documentation. With our Data Validation feature and Live Compare, you can be sure that your data is accurate. All you need to complete your data replication project are included in one license.

Opsware is being used by legal and engineering teams to embed privacy workflows throughout their organizations in order to simplify compliance with the latest privacy laws. No programming is required. You can eliminate the tedious work of managing vendors and building data maps. Our data discovery and classification tools make it easy to discover data sources. Our automation technology can complete data requests from customers and consent changes across all data systems and vendors. Your teams can use the same tool and workflow. To ensure compliance and visibility into risk, you can track the flow of personal information within your company. With a single click, we cover all the processes required to create processing records. You have complete control over your company data. You can install our virtual appliance on-premise to ensure that your personal data does not leave your secure data context. Every company collects personal information.

As part of a 360-degree view, healthcare organizations can consolidate vital patient, administrative, and clinical data. They can also safeguard that information and make it accessible to staff. You can eliminate data silos by aggregating administrative and patient information across your healthcare enterprise. Optimize healthcare data storage by making use of all your storage assets and reducing your total cost of ownership. Protect your healthcare data and ensure a quick recovery in the event that it is lost, stolen, or corrupted. Your staff will be able to view the patient's entire history 360 degrees, which can help improve care and outcomes. You can eliminate obsolete or legacy applications and still have access to their data, saving time, money, and resources. Digitally transform your clinical testing processes and accelerate your testing operations.

KDB.AI, a powerful knowledge based vector database, is a powerful search engine and knowledge-based vector data base that allows developers to create scalable, reliable, and real-time AI applications. It provides advanced search, recommendation, and personalization. Vector databases are the next generation of data management, designed for applications such as generative AI, IoT or time series. Here's what makes them unique, how they work and the new applications they're designed to serve.

We help, You comply. Complyon's risk management, governance and compliance software can make compliance a competitive advantage for your business. Our tools ensure your compliance. Data mapping Save time and protect your data by optimizing, connecting and reusing your dataflows. Reporting. In seconds, generate up-to-date, protocol-ready reports that cover everything from systems to risk. Decentralizing compliance. Centralizing compliance allows management to trust your compliance. It is easy to update, validate, and manage. Our tailored workflows will improve compliance. Central governance. Business unit input and central governance provide all the data necessary to ensure compliance with GDPR and other regulations. Data flow analysis. Data flow analysis. Get a complete overview of your data by analyzing the interconnections of activities, systems, and processes. This includes everything from third parties and policies to legal bases and retention rules.

Data labeling software that makes your AI smart -- Data labeling tool to identify various data types Using Machine Learning and Active Learning, automatically label up to 95% of your data. • Manage all your training data from one place You can control quality and privacy

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

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.

A platform for ML engineers that provides synthetic data to facilitate the development of better AI models. Simple APIs allow for the instant generation of photorealistic, well-labeled images. Cloud-based generation platform that is highly scalable delivers millions of perfectly labeled photos. On-demand data allows for new data-centric approaches to create more efficient models. A broader range of pixel-perfect labels, including segmentation maps and dense 2D/3D landmarks as well as depth maps, surface norms, depth maps, and more, is now available. You can quickly design, test, refine, and revise your products before you build hardware. To optimize your system, prototype different imaging modalities, camera placements, lens types, and other lens types. Your models will be less biased if there are misbalanced data sets. Privacy will still be protected. Equal representation across identities, facial attributes and poses, as well as lighting and camera settings. We have worked with top-notch customers in many different use cases.

The most flexible data annotation software. Quickly installable. Create custom UIs, or use pre-built labeling template. Layouts and templates that can be customized to fit your dataset and workflow. Detect objects in images. Supported are boxes, polygons and key points. Partition an image into multiple segments. Use ML models to optimize and pre-label the process. Webhooks, Python SDK and API allow you authenticate, create tasks, import projects, manage model predictions and more. ML backend integration allows you to save time by using predictions as a tool for your labeling process. Connect to cloud object storage directly and label data there with S3 and GCP. Data Manager allows you to manage and prepare your datasets using advanced filters. Support multiple projects, use-cases, and data types on one platform. You can preview the labeling interface as you type in the configuration. You can see live serialization updates at the bottom of the page.

Privacy engineering tools delivered as APIs. In minutes, you can synthesize and transform data. Trust your users and the community. Gretel's APIs allow you to instantly create anonymized or synthetic data sets so that you can safely work with data while protecting your privacy. Access to data must be faster in order to keep up with the development pace. Gretel's data privacy tools bypass blockers, and allow for Machine Learning and AI applications to access data faster. Gretel Cloud runners makes it easy to scale up your workloads to the cloud or keep your data safe by running Gretel containers within your own environment. Developers will find it much easier to train and create synthetic data using our cloud GPUs. Scale workloads instantly with no infrastructure required. Invite colleagues to collaborate on cloud projects, and share data between teams.

All large language models require high-quality training data, whether you create the data yourself or use existing models. A human-in the-loop labeling system provides real-time feedback to fine-tune datasets and build the most performant AI models. We offer precise data labeling and faster human input in order to improve the robustness of LLMs to better adapt to enterprise applications. Our labeler management allows for us to segment teams. You only pay for the experience and skill sets required by your data labelling projects. Sapien is able to scale up or down labeling operations for large and small annotation projects. Human intelligence at scale. We can customize our labeling models to meet your specific data formats, data types, and annotation requirements.

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

The platform is easy to use and quick to implement machine learning and computer vision workflows. Sense makes it easy to create and deploy AI IoT solutions on any cloud, edge or on-premise. Learn how Sense makes it easy for AI/ML teams to create and deploy AI IoT solutions to any cloud, the edge or on-premise. It is powerful enough for ML engineers but simple enough for subject matter experts. Sense Data Annotation maximizes the success of your machine-learning models by making it the easiest and fastest way to label image and video data for high-quality training datasets. The Sense platform provides one-touch labeling integration to enable continuous machine learning at edge for simplified management.

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.

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.