Alternatives to Syntheticus

Windocks provides on-demand Oracle, SQL Server, as well as other databases that can be customized for Dev, Test, Reporting, ML, DevOps, and DevOps. Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management. Databases can be delivered to conventional instances, Kubernetes or Docker containers. Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments. When combined with Docker containers, enterprises often see a 5:1 reduction of lower-level database VMs.

We can no longer rely upon real-life conversations as physical customer interactions shift to digital. Customers communicate their intentions and share their needs using data. Data is a key tool for understanding customers and testing our assumptions. Privacy regulations like GDPR and CCPA make deep understanding more difficult. This gap in customer understanding is bridged by the MOSTLY AI synthetic dataset platform. Businesses can benefit from a reliable, high-quality generator of synthetic data in many different applications. The story doesn't end there. MOSTLY AI's synthetic dataset platform is more versatile than any other synthetic data generator. MOSTLY AI's versatility makes it an indispensable tool for software development and testing. From AI training to explainability and bias mitigation, governance to realistic test data, with subsetting, referential integrity.

Mask, generate, subset, virtualize, and automate your test data with the DATPROF Test Data Management Suite. Our solution helps managing Personally Identifiable Information and/or too large databases. Long waiting times for test data refreshes are a thing of the past.

Create high-fidelity 3D environments and diverse synthetic data quickly and easily to improve model performance. Bifrost is the fastest and easiest way to create high-quality synthetic images to improve ML performance. By avoiding time-consuming and expensive real-world data collection, you can prototype and test up 30x faster. Generating data to account rare scenarios that are underrepresented in the real data will result in more balanced datasets. Manual annotation and labeling can be resource-intensive and prone to errors. Quickly and easily generate data that are pre-labeled, pixel-perfect. Real-world data can inherit biases from the conditions under which it was collected and generate data that solves for these instances.

Set your enterprise data free. Hazy can re-engineer enterprise data to make it easier, safer, and faster to use. Every enterprise can actually use its data. Data has never been more valuable. However, with increasing privacy requirements and tightening regulations, the majority of the world's data remains locked away and inaccessible. Hazy has developed a new approach that allows data to be used. So you can make better decisions and develop new technologies, and provide more value to your customers. Realistic test data can be created and deployed to quickly validate new technologies and systems. This will help accelerate your digital transformation. You must generate enough high-quality, safe data to train, improve, and build your AI applications. To improve decision-making, empower teams to share and generate accurate intelligence and analytics on products, customers, operations, and other factors.

With automated data quality profiling, and synthetic data generation, adopting data-centric AI is easier than ever. We help data scientists unlock the full potential of data. YData Fabric enables users to easily manage and understand data assets, synthetic data, for fast data access and pipelines, for iterative, scalable and iterative flows. Better data and more reliable models delivered on a large scale. Automated data profiling to simplify and speed up exploratory data analysis. Upload and connect your datasets using an easy-to-configure interface. Synthetic data can be generated that mimics real data's statistical properties and behavior. By replacing real data with synthetic data, you can enhance your datasets and improve your models' efficiency. Pipelines can be used to refine and improve processes, consume data, clean it up, transform your data and improve its quality.

Data is a valuable business asset. With the right AI model it is possible to use data in order to build and understand customers profiles, identify trends, and identify potential new business opportunities. It takes a lot of data to build accurate and robust AIs, which is a challenge from both a quality and quantity perspective. Stringent regulations, including GDPR, also restrict the use and sharing of sensitive data like customer data. It's time to change the way we do things. It's time for a new approach, especially in a software test environment where it is difficult to access good quality testing data. We often see actual customer data used, which can lead to GDPR non-compliance with heavy financial penalties. Artificial Intelligence is expected to increase productivity in businesses by at least 40%, but businesses struggle to deploy AI solutions or fully unlock them due to data-related issues. ADA generates artificial data using advanced deep-learning.

Accelerate data processing tasks such as structuring, labeling and preprocessing. Manage all your data on a central platform that is easy to integrate. Increase data accessibility quickly with privacy-protected synthetic data and user friendly exchange platforms. The Aindo platform for synthetic data allows you to securely share data between departments, with external providers, partners and the artificial-intelligence community. Synthetic data exchange and collaboration can help you discover new synergies. Securely and openly acquire missing data. Your clients and stakeholders will appreciate the comfort and trust you provide. Aindo's synthetic data platform eliminates data inaccuracies, implicit bias and provides fair and complete insights. Enhance information to make databases resilient to special events. Balance datasets which misrepresent the true population to get a fair and accurate depiction. Fill in data gaps with precision and accuracy.

Advanced technology and services that safely transform and enhance sensitive information into actionable insights can help drive innovation and open up new revenue streams. Mimic synthetic engine allows companies to safely synthesize data assets while protecting consumer privacy and maintaining statistical relevance. The synthetic data can be used to support internal initiatives such as analytics, machine-learning and AI, marketing, segmentation, and revenue generation through external data monetization. Mimic enables you to safely move statistically-relevant synthetic data to the cloud ecosystem of your choice to get the most out of your data. With the enhanced synthetic data in the cloud, you can do analytics, insights, product testing, third-party data sharing, and more. The data has been certified as being compliant with privacy and regulatory laws.

Overcome challenges when acquiring data to train AI and machine learning systems. Rendered.ai, a PaaS, is designed for data scientists and engineers. Create synthetic datasets to train and validate ML/AI. Experiment with scene content, sensor models, and post-processing. Catalogue and characterize real and synthetic datasets. Download or move data into your own cloud repositories to be processed and trained. Synthetic data can be used to boost innovation and productivity. Create custom pipelines for modeling diverse sensors and computer-vision inputs. Python sample code is available for free and can be customized to model SAR, RGB Satellite imagery, and other sensor types. Flexible licensing allows for almost unlimited content creation. Create labeled, high-performance computing content quickly in a hosted environment. No-code configuration allows data scientists and engineers to collaborate.

Describe your data model at an abstract level using XML. It is not necessary to have developer skills. Use a variety of function libraries to create realistic data. Write your own extensions using Javascript or Java. Integrate your data processing into Gitlab CI, Jenkins or Jenkins. Benerator's model driven data toolkit allows you to generate, anonymize and migrate data. Define processes for anonymizing or pseudonymizing data in plain XML without needing developer skills. Protect your customers' privacy and stay GDPR compliant. Mask and obfuscate data sensitive for BI, testing, development or training. Subsetting allows you to combine data from different sources while maintaining data integrity. Transform and migrate your data in multisystem landscapes. Reuse your test data models for migration to production environments. In a microsystems architecture, keep your data consistent and reliable.

Share your production data securely with ML, data scientists, and analytics teams, while maintaining customer trust. Stop wasting time with regexes and free models. Private AI anonymizes 50+ entities PII PCI and PHI in 49 languages, with unmatched accuracy, across GDPR, CPRA and HIPAA. Synthetic data can be used to replace PII, PCI and PHI text in order to create model training data that looks exactly like production data. This will not compromise customer privacy. Remove PII in 10+ file formats such as PDFs, DOCXs, PNGs, and audios to protect customer data and comply privacy regulations. Private AI uses the most advanced transformer architectures for remarkable accuracy right out of the box. No third-party processing required. Our technology outperforms every other redaction service available on the market. Please feel free to request a copy of the evaluation toolkit for you to test with your own data.

A self-service platform for synthetic data, with a focus on object and human data. The Datagen Platform gives you granular control over the data generation process. Analyzing your neural networks can help you understand what data is required to improve them. You can then easily generate the data that you need to train your network. Datagen is a powerful platform that can help you solve your problems. It allows you to generate high-quality, high-variety, domain-specific, simulated artificial data. Advanced capabilities include the ability to simulate dynamic people and objects within their context. Datagen gives CV teams unprecedented flexibility to control visual outcomes in a variety of 3D environments.

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.

Sixpack is an automated data management platform that streamlines synthetic data for testing. Sixpack, unlike traditional test data generation methods, provides an unlimited supply of synthetic data to help testers and automated testing avoid conflicts and resource bottlenecks. It focuses on flexibility, enabling data allocation, pooling and instant generation, while maintaining data quality and privacy. The key features are the ease of setup, seamless integration with APIs, and the support for complex test environments. Sixpack integrates directly into QA processes so that teams can save time managing data dependencies and minimize data overlap. Its dashboard provides a clear view into active data sets. Testers can also allocate or pool data based on project needs.

Amazon SageMaker lets you identify raw data, such as images, text files and videos. You can also add descriptive labels to generate synthetic data and create high-quality training data sets to support your machine learning (ML). SageMaker has two options: Amazon SageMaker Ground Truth Plus or Amazon SageMaker Ground Truth. These options allow you to either use an expert workforce or create and manage your data labeling workflows. data labeling. SageMaker GroundTruth allows you to manage and create your data labeling workflows. SageMaker Ground Truth, a data labeling tool, makes data labeling simple. It also allows you to use human annotators via Amazon Mechanical Turk or third-party providers.

Synthesized can help you unlock the full potential of your data projects and AI. We automate all stages of data preparation and provisioning with cutting-edge AI. The platform synthesizes data without exposing personal information or causing compliance issues. Software to prepare and provide accurate synthetic data for building better models at scale. Synthesized solves the problem of data sharing for businesses. 40% of companies that invest in AI can't report business benefits. Our easy-to-use platform allows data scientists, product, and marketing teams to focus on revealing critical insight. Without representative data, testing data-driven applications can be difficult. This can lead to problems when services go live.

K2View believes that every enterprise should be able to leverage its data to become as disruptive and agile as possible. We enable this through our Data Product Platform, which creates and manages a trusted dataset for every business entity – on demand, in real time. The dataset is always in sync with its sources, adapts to changes on the fly, and is instantly accessible to any authorized data consumer. We fuel operational use cases, including customer 360, data masking, test data management, data migration, and legacy application modernization – to deliver business outcomes at half the time and cost of other alternatives.

Our proprietary labeling platform allows us to label your data with higher quality standards in order to meet the requirements of complex AI projects. Now you can create the products that your customers love.

OneView creates next-generation virtual synthetic datasets for ML algorithm training. We provide ready-to-use datasets for all objects in any environment, with a focus on satellite and aerial imagery. VSD is computer-generated, real-like imagery. It can be used to replace real imagery. It is a cost-effective, scalable, and highly accurate training material for ML algorithm. OneView bridges the gap between increasing availability of earth observation data, and the limited ability to use it for intelligence gathering. We provide the tools to improve data analysis and enable geospatial imagery to yield new and valuable insights. It doesn't matter if you have long-tail or rare objects, or if you don't have enough data. We can generate a dataset for every object. We can generate any environment. High-detailed, customized environment for model training.

Statice is a data anonymization tool that draws on the most recent data privacy research. It processes sensitive data to create anonymous synthetic datasets that retain all the statistical properties of the original data. Statice's solution was designed for enterprise environments that are flexible and secure. It incorporates features that guarantee privacy and utility of data while maintaining usability.

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 MDClone ADAMS Platform provides a powerful self-service data analysis environment that enables collaboration, research and innovation in healthcare. Our pioneering platform allows you to access insights in real-time and securely. It also allows you to explore healthcare data in a dynamic, real-time manner. Put your organization on a path of continuous learning to improve care, streamline your operations, promote research, and drive innovations, ultimately empowering your entire healthcare ecosystem. Synthetic data can be used to enable collaboration between teams, organizations and even third parties. This will allow them to dig deeper into the information that they need, when they need it. Life science organizations can identify promising cohorts of patients for post-marketing analyses by accessing real-world, internal data. Discover a fundamentally new approach to unlocking healthcare information for life sciences.

Synth is a data-as code tool that offers a simple CLI workflow to generate consistent data in an scalable manner. Synth can be used to generate data that is correct and anonymized, but still looks and sounds like production. Create test data fixtures to support your continuous integration, testing and development. Create data that tells you the story you wish to tell. Specify constraints and relations. Seed development, environments and CI. Anonymize sensitive production data. Create realistic data according to your specifications. Synth's declarative configuration language allows you to specify the entire data model in code. Synth can import existing data and create accurate data models. Synth is database-agnostic and supports semistructured data. It works well with SQL and NoSQL. Synth can generate thousands of semantic types, such as email addresses, credit card numbers and more.

Tonic automatically creates mock datasets that preserve key characteristics of secure data sets so that data scientists, developers, and salespeople can work efficiently without revealing their identities. Tonic creates safe, de-identified data from your production data. Tonic models your production data from your production data to help tell a similar story in your testing environments. Safe and useful data that is scaled to match your real-world data. Safely share data across businesses, teams, and borders to create data that is identical to your production data. PII/PHI identification and obfuscation. Protect your sensitive data by proactive protection with automatic scanning, alerts and de-identification. Advanced subsetting across diverse database types. Fully automated collaboration, compliance, and data workflows.

Enterprise synthetic test data solutions. It is essential that test data accurately reflects the structure of your database or application. This means it must be easy for you to model and maintain each project. Respect the referential integrity of parent/child/sibling relations across data domains within an app database or across multiple databases used for multiple applications. Ensure consistency and integrity of synthetic attributes across applications, data sources, and targets. A customer name must match the same customer ID across multiple transactions simulated by real-time synthetic information generation. Customers need to quickly and accurately build their data model for a test project. GenRocket offers ten methods to set up your data model. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.

The Synthetic Data vault (SDV) was designed as a Python library that allows you to create tabular synthetic data. The SDV uses machine learning algorithms to emulate patterns in synthetic data. The SDV offers a variety of models, from classical statistical methods to deep learning methods. Create data for single tables or multiple connected tables. Compare the synthetic data with the real data using a variety measures. Diagnose problems and create a quality report for more insights. Control data processing to enhance the quality of synthetic information, choose different types of anonymization and define business rules as logical constraints. Use synthetic data to replace real data or as an enhancement. The SDV is a comprehensive ecosystem of synthetic data models, metrics, and benchmarks.

When capturing data in real-life situations is difficult, we create diverse, fully-labeled image datasets. Our procedural technology provides the highest-quality, unbiased, and labeled synthetic datasets to improve your computer vision model. AI Verse gives users full control over scene parameters. This allows you to fine-tune environments for unlimited image creation, giving you a competitive edge in computer vision development.

A flexible and accurate platform for the generation of synthetic data. Create the data you require for your perception system within minutes. Design scenarios with infinite variations for your use case. Create your datasets on the cloud. Anyverse is a scalable software platform that allows you to train, validate or fine-tune a perception system. It offers unparalleled computing power to generate all of the data you require in a fraction the time and cost as compared to other real-world workflows. Anyverse is a modular platform which enables efficient scene creation and dataset production. Anyverse™, Studio is a standalone application with a graphical interface that manages All Anyverse functions including scenario definition, variability setting, asset behavior, dataset settings and inspection. Data is stored on the cloud and the Anyverse cloud is responsible for scene generation, simulation and rendering.

Our AI-driven, low-code automation platform is designed for you to achieve the best quality result in the shortest time possible. Our Natural Language Processing (NLP-powered solution) allows you to generate automation scripts in plain English and allows your coders focus on innovation. Our autonomous discovery and current tracking of changes ensures that your application is high quality throughout its lifecycle. Our autonomous healing capability reduces risk in dynamic development environments and delivers flawless updates by keeping automation up-to-date. All regulatory requirements are met and security risks eliminated by using AI-generated synthetic data to automate your business processes. Multiple tests can be run simultaneously, you can determine the test frequency, keep up with browser updates, and execute across platforms and operating systems.

CloudTDMS, your one stop for Test Data Management. Discover & Profile your Data, Define & Generate Test Data for all your team members : Architects, Developers, Testers, DevOPs, BAs, Data engineers, and more ... Benefit from CloudTDMS No-Code platform to define your data models and generate your synthetic data quickly in order to get faster return on your “Test Data Management” investments. CloudTDMS automates the process of creating test data for non-production purposes such as development, testing, training, upgrading or profiling. While at the same time ensuring compliance to regulatory and organisational policies & standards. CloudTDMS involves manufacturing and provisioning data for multiple testing environments by Synthetic Test Data Generation as well as Data Discovery & Profiling. CloudTDMS is a No-code platform for your Test Data Management, it provides you everything you need to make your data development & testing go super fast! Especially, CloudTDMS solves the following challenges : -Regulatory Compliance -Test Data Readiness -Data profiling -Automation

Our AI-powered platform for data generation allows data analysts and machine-learning engineers to maximize their analytical data sets. Datomize allows users to create the exact analytical data they need by leveraging behavior extracted from existing data. With data that accurately reflects real-world scenarios and allows users to make better decisions, they can now get a more accurate picture of reality. Take advantage of your data to develop AI solutions that are state-of-the art. Datomize's AI powered, generative models create superior synthesized replicas by extracting behavior from your existing datasets. Advanced augmentation tools allow for unlimited resizing, while dynamic validation tools show the similarity of original and replicated data. Datomize's machine learning approach is data-centric and addresses the primary constraints of training high-performing ML model.

Create, transform, or analyze any text data within your spreadsheet. Automatically normalize addresses, separate column, extract entities, etc. Rewrite SEO content or blog posts. Generate product description variations. Create synthetic data such as first/last names and phone numbers. Create bullet-point summaries and rewrite content in fewer words. Sort product feedback into categories, prioritize sales leads, find new trends, etc. Charm provides several templates to help people complete common tasks faster. Use the Summarize With Bullet Points Template to create short bullet-point summaries for long content. Use the Translate Language Template to translate existing content in another language.

As enterprise data explodes and is scattered across multiple systems, the oversight of privacy, data security and governance has become a very difficult task. Businesses are exposed to significant risks, including data breaches, privacy suits, and penalties. It takes months to find data privacy risks within an organization. A team of data engineers is involved in the effort. Data breaches and privacy legislation are forcing companies to better understand who has access to data and how it is used. Enterprise data is complex. Even if a team works for months to isolate data privacy risks, they may not be able to quickly find ways to reduce them.

Synthetic data is the key to a leading-edge technology that allows for flawless retail execution. Vision technology for consumer packaged products. Choose from a catalog of over 100,000 SKUs on the Neurolabs platform, including top brands like P&G, Nestle Unilever, Coca-Cola and more. Our API will automatically stitch together multiple shelf images uploaded by your field agents from their mobile devices. SKU-level detection gives you detailed information that can be used to calculate retail execution KPIs, such as out-of shelf rate, shelf share percentage and competitor price comparison. Discover how our cutting edge image recognition technology can maximize store operations, improve customer experience, and boost profits. Implement a real deployment in less than a week. Access image recognition datasets covering over 100,000 SKUs.

SKY ENGINE AI is a simulation and deep learning platform that generates fully annotated, synthetic data and trains AI computer vision algorithms at scale. The platform is architected to procedurally generate highly balanced imagery data of photorealistic environments and objects and provides advanced domain adaptation algorithms. SKY ENGINE AI platform is a tool for developers: Data Scientists, ML/Software Engineers creating computer vision projects in any industry. SKY ENGINE AI is a Deep Learning environment for AI training in Virtual Reality with Sensors Physics Simulation & Fusion for any Computer Vision applications.

dbForge Data Generator is a powerful GUI tool that populates Oracle schemas with realistic test data. The tool has an extensive collection 200+ predefined and customizeable data generators for different data types. It delivers flawless and fast data generation, including random number generation, in an easy-to-use interface. The latest version of Devart's product is always available on their official website.

Datanamic Data Generator allows developers to quickly populate databases with thousands upon rows of meaningful, syntactically correct data for database testing purposes. A blank database is useless for testing your application. Test data is essential. It is difficult to create your own test data generators and scripts. Datanamic Data Generator can help. This tool is available for developers, DBAs, and testers who require sample data to test a database-driven app. Datanamic Data Generator makes it easy to generate database test data. It will read your database and display tables and columns according to their data generation settings. To generate complete (realistic) test data, only a few entries are required. This tool can be used to create test data from scratch, or from existing data.

MakerSuite simplifies this process. MakerSuite allows you to easily tune custom models, iterate on prompts and augment your data with synthetic data. MakerSuite allows you to export your prompts as code in your favorite languages, such as Python and Node.js, when you are ready to move on to code.

dbForge Data generator for MySQL is an advanced GUI tool that allows you to create large volumes of realistic test data. The tool contains a large number of predefined data generation tools with customizable configuration options. These allow you to populate MySQL databases with meaningful data.

KopiKat, a revolutionary tool for data augmentation, improves the accuracy and efficiency of AI models by modifying the network architecture. KopiKat goes beyond the standard methods of data enhancement by creating a photorealistic copy while preserving all data annotations. You can change the original image's environment, such as the weather, seasons, lighting, etc. The result is an extremely rich model, whose quality and variety are superior to those created using traditional data augmentation methods.

Ragas is a framework that allows you to test and evaluate applications that use the Large Language Model. It provides automatic metrics for assessing performance and robustness. Synthetic test data is generated according to specific requirements. Workflows are also available to ensure quality in development and production monitoring. Ragas integrates seamlessly into existing stacks and provides insights to enhance LLM application. The platform is maintained and developed by a passionate team of individuals who use cutting-edge engineering practices and cutting-edge research to empower visionaries to redefine LLM possibilities. Synthesize high-quality, diverse evaluation data tailored to your needs. Evaluation and quality assurance of your LLM application during production. Use insights to improve the application. Automatic metrics to help you understand performance and robustness of the LLM application.

RNDGen Random Data Generator, a user-friendly tool to generate test data, is free. The data creator customizes an existing data model to create a mock table structure that meets your needs. Random Data Generator is also known as dummy data, csv, sql, or mock data. Data Generator by RNDGen lets you create dummy data that is representative of real-world scenarios. You can choose from a variety of fake data fields, including name, email address, zip code, location and more. You can customize generated dummy information to meet your needs. With just a few mouse clicks, you can generate thousands of fake rows of data in different formats including CSV SQL, JSON XML Excel.

Azure Data Manager for Agriculture is an extension of the Microsoft Intelligent Data Platform that provides industry-specific data connectors, and connects farm data from different sources. Use high-quality datasets for the development of digital agricultural solutions. Access to high-quality farm information that is readily available will allow you to focus on innovation and reduce the challenges of data management. Applications based on agronomic information can provide predictive insights and promote sustainable agriculture practices. Utilize abundant farm data to create solutions that increase productivity, decrease risk, and improve decision-making across the agrifood value chain. Establish stakeholder confidence with high-quality data from the farm and leverage a cloud platform designed for security and compliance.

NVIDIA Nemotron, a family open-source models created by NVIDIA is designed to generate synthetic language data for commercial applications. The Nemotron-4 model 340B is an important release by NVIDIA. It offers developers a powerful tool for generating high-quality data, and filtering it based upon various attributes, using a reward system.

Secure sensitive data with Tanker is the best way to go. Integrate our end-to-end encryption platform into your application to build trust with your users. We can help you communicate your client's data effectively to ensure that they are secure. No data from your application will be accessible even if it is attacked. Tanker is a privacy by design solution that conforms to global regulations. We have already protected over 30M users, and encrypted more than 1 bln data. We adapt quickly to your growth. No changes to your application UX are necessary. The difference will not be noticed by your end-users, but it will affect their sensitive data. The platform can be integrated in three weeks with minimal cryptographic skills. We offer high-quality tutorials as well as reference documents. We offer several security options to allow our clients to choose which data they want encrypted and how. We adapt to your needs.

Scale Evaluation is a comprehensive evaluation tool for large language models. This platform addresses the current challenges in AI models assessment, including the scarcity and lack of high-quality evaluation datasets. Scale provides proprietary evaluation sets that cover a wide range of domains and capabilities. This ensures accurate model assessment without overfitting. The platform has a user-friendly interface that allows for the analysis and reporting of model performance. This allows for apples-to-apples comparisons. Scale's expert network of human raters also delivers reliable evaluations. This is supported by transparent metrics, and quality assurance mechanisms. The platform offers customized evaluations that focus on specific model concerns. This allows for precise improvements by using new training data.

Open source AI model that you can fine-tune and distill anywhere. Our latest instruction-tuned models are available in 8B 70B and 405B version. Our open ecosystem allows you to build faster using a variety of product offerings that are differentiated and support your use cases. Choose between real-time or batch inference. Download model weights for further cost-per-token optimization. Adapt to your application, improve using synthetic data, and deploy on-prem. Use Llama components and extend the Llama model using RAG and zero shot tools to build agentic behavior. Use 405B high-quality data to improve specialized model for specific use cases.

Customer data is everywhere: on our phones, in social media, in IoT, in CRM, ERP and marketing. The amount of data that companies collect is staggering... The data companies capture is overwhelming... Data that is uncontrolled and of low quality can cause a company to become disorganized, putting its growth at risk. All processes in a business should be synchronized around customer data. It is vital to ensure that the data is always reliable and accessible. DQE One is a solution for all departments that leverage customer data. Data of high quality ensures that every decision is made with confidence. Contact information from different sources is piled up in the company's database. The customer database needs to be maintained and qualified for use as a reliable repository, even if it contains data entry errors, incorrect information or gaps.

NVIDIA's Morpheus AI framework is GPU-accelerated and allows developers to create applications that are optimized for filtering, classifying, and processing large volumes of cybersecurity data. Morpheus uses AI to reduce time and costs associated with identifying and capturing threats and taking action. This brings a new level to security to data centers, clouds, and the edge. Morpheus extends the capabilities of human analysts with generative AI, automating real-time analyses and responses. It produces synthetic data for AI models to train that accurately identify risks and run what-if scenario. Morpheus can be downloaded as open-source software from GitHub by developers who are interested in the latest prerelease features and want to build their own. NVIDIA AI enterprise offers unlimited usage across all clouds, access NVIDIA AI experts and long-term support.