Alternatives to MakerSuite

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

syntheticAIdata will help you create diverse datasets at scale and with ease. Our solution is not only a cost-saving tool, but also ensures privacy, regulatory compliance and accelerates the journey of your AI products to market. Let syntheticAIdata transform your AI aspirations to achievements. Synthetic data can be generated in large quantities and cover a wide range of scenarios when real data are not available. Automatically generated annotations include a variety of options. This reduces the time required for data collection and tagging. Generating synthetic data at a large-scale will reduce costs for data collection, tagging and data storage. Our user-friendly, no-code solution allows even those with little technical knowledge to generate synthetic data. Our solution is the easiest to use in the market, with seamless integration via one-click with leading cloud platforms.

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.

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-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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

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.

Rockfish Data is a synthetic data generation platform that unlocks the true value of operational information. Rockfish Data helps enterprises use siloed datasets to train ML/AI, create compelling datasets for demos, and much more. The platform intelligently adjusts and optimizes diverse data sets, seamlessly adapting to different data types, sources and structures, for maximum efficiency. It is designed to deliver specific, measurable business results, while ensuring data integrity and privacy. Rockfish's operationalization of synthetic data allows organizations to overcome data-silos, enhance machine-learning and artificial intelligence workflows and generate high quality datasets for different applications.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Prompt Flow, a set of development tools, is designed to streamline end-to-end AI development cycles based on LLM, from ideation and prototyping to testing and evaluation, to production deployment and monitoring. It simplifies prompt engineering and allows you to create LLM apps of production quality. Prompt flow allows you to create flows that connect LLMs, Python code and other tools in an executable workflow. It is easy to debug and iterate flows, tracing interactions between LLMs in particular. You can evaluate flows, calculate performance and quality metrics with larger datasets and integrate the testing into your CI/CD to ensure quality. It is easy to deploy flows on the platform of your choosing or integrate them into your app code base. The cloud version of Azure AI's Prompt flow facilitates collaboration with your team.

Syntheticus®, a data exchange platform, overcomes limitations such as scarcity and bias in data, while enabling data exchange at scale. Our synthetic data platform allows you to generate high-quality, compliant data samples that are tailored to your business goals and analytics needs. Synthetic data allows you to tap into a variety of high-quality resources that aren't always available in real life. You can conduct more reliable research by using high-quality data that is consistent. This will lead to better products, business decisions, and services. You can accelerate product development cycles by having reliable, fast data at your fingertips. Synthetic data is designed as a private and secure data source by default. This protects sensitive data and maintains compliance with privacy laws.

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.

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.

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.

Subsalt is a platform that allows anonymous data to be used at enterprise scale. Subsalt’s Query Engine dynamically optimizes tradeoffs between data security and fidelity to source data. The queries return fully-synthetic results that preserve row-level granularity, data formats and data formats. Subsalt offers compliance guarantees backed by third-party audits which satisfy HIPAA’s Expert Determination Standard. Subsalt offers multiple deployment models that can be tailored to the privacy and security needs of each client. Subsalt is HIPAA and SOC2-Type 2-compliant. The system is designed to minimize any risk of data exposure or breach. Subsalt's Postgres compatible SQL interface integrates directly with existing data and ML software, making adoption a breeze.

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.

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.

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.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Fine-tuning is when you take a dataset, and create a model to use AI. This is not always possible without programming so we created a solution in RIku that handles everything in a very easy format. Fine-tuning unlocks an entirely new level of power for artificial intelligence and we are excited to help you explore this. Public Share Links are landing pages you can create for any of the prompts. These can be designed with your brand in mind, including colors and adding your logo. These links can be shared with anyone, and if they have access to the password to unlock it they will be able make generations. No-code assistant builder for your audience. We found that projects using multiple large languages models have a lot of problems. They all return their outputs in a slightly different way.

Here is where the future of AI development begins. Modular is a composable, integrated suite of tools which simplifies your AI infrastructure, allowing your team to develop, deploy and innovate faster. Modular's inference engines unify AI industry frameworks with hardware. This allows you to deploy into any cloud or on-prem environments with minimal code changes, unlocking unmatched portability, performance and usability. Move your workloads seamlessly to the best hardware without rewriting your models or recompiling them. Avoid lock-in, and take advantage of cloud performance and price improvements without migration costs.