Alternatives to Cleanlab

dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use. With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.

Explore a diverse array of resources to kickstart your AI initiative. The AI hub offers an extensive selection of essential tools, such as industry-specific AI starter kits, datasets, coding notebooks, pre-trained models, and ready-to-deploy solutions and pipelines. Gain access to top-notch resources from external sources or those developed internally by your organization. Efficiently prepare and manage your data for model training by collecting, organizing, tagging, or selecting features, with a user-friendly drag-and-drop interface. Collaborate seamlessly with team members to tag extensive datasets and implement a robust quality control process to maintain high dataset standards. Easily build models with just a few clicks using intuitive model wizards, requiring no prior data science expertise. The system intelligently identifies the most suitable models for your specific challenges while optimizing their training parameters. For those with advanced skills, there's the option to fine-tune models and adjust hyper-parameters. Furthermore, enjoy the convenience of one-click deployment into production environments for inference. With this comprehensive framework, your AI project can flourish with minimal hassle.

Embracing data-centric AI has become remarkably straightforward thanks to advancements in automated data quality profiling and synthetic data creation. Our solutions enable data scientists to harness the complete power of their data. YData Fabric allows users to effortlessly navigate and oversee their data resources, providing synthetic data for rapid access and pipelines that support iterative and scalable processes. With enhanced data quality, organizations can deliver more dependable models on a larger scale. Streamline your exploratory data analysis by automating data profiling for quick insights. Connecting to your datasets is a breeze via a user-friendly and customizable interface. Generate synthetic data that accurately reflects the statistical characteristics and behaviors of actual datasets. Safeguard your sensitive information, enhance your datasets, and boost model efficiency by substituting real data with synthetic alternatives or enriching existing datasets. Moreover, refine and optimize workflows through effective pipelines by consuming, cleaning, transforming, and enhancing data quality to elevate the performance of machine learning models. This comprehensive approach not only improves operational efficiency but also fosters innovative solutions in data management.

Capture production data. Evaluate outputs together and fine-tune the performance of your LLM. A detailed log overview will help you understand what is happening in production. Work with domain experts, product managers and engineers to create reliable model outputs. Track AI metrics, such as speed, token usage, and quality scores. Copilot automates model evaluations and improvements for your use cases. Create, manage, or optimize prompts for precise and relevant interactions between AI models and users. Compare fine-tuned models and foundation models to improve prompt performance. Build a fine-tuning dataset with your team. Create custom fine-tuning data to optimize model performance.

Tune Studio is a highly accessible and adaptable platform that facilitates the effortless fine-tuning of AI models. It enables users to modify pre-trained machine learning models to meet their individual requirements, all without the need for deep technical knowledge. Featuring a user-friendly design, Tune Studio makes it easy to upload datasets, adjust settings, and deploy refined models quickly and effectively. Regardless of whether your focus is on natural language processing, computer vision, or various other AI applications, Tune Studio provides powerful tools to enhance performance, shorten training durations, and speed up AI development. This makes it an excellent choice for both novices and experienced practitioners in the AI field, ensuring that everyone can harness the power of AI effectively. The platform's versatility positions it as a critical asset in the ever-evolving landscape of artificial intelligence.

OpenPipe offers an efficient platform for developers to fine-tune their models. It allows you to keep your datasets, models, and evaluations organized in a single location. You can train new models effortlessly with just a click. The system automatically logs all LLM requests and responses for easy reference. You can create datasets from the data you've captured, and even train multiple base models using the same dataset simultaneously. Our managed endpoints are designed to handle millions of requests seamlessly. Additionally, you can write evaluations and compare the outputs of different models side by side for better insights. A few simple lines of code can get you started; just swap out your Python or Javascript OpenAI SDK with an OpenPipe API key. Enhance the searchability of your data by using custom tags. Notably, smaller specialized models are significantly cheaper to operate compared to large multipurpose LLMs. Transitioning from prompts to models can be achieved in minutes instead of weeks. Our fine-tuned Mistral and Llama 2 models routinely exceed the performance of GPT-4-1106-Turbo, while also being more cost-effective. With a commitment to open-source, we provide access to many of the base models we utilize. When you fine-tune Mistral and Llama 2, you maintain ownership of your weights and can download them whenever needed. Embrace the future of model training and deployment with OpenPipe's comprehensive tools and features.

Now you have the opportunity to fine-tune GPT without any technical expertise required. By customizing AI models to suit your individual requirements, you can enhance their capabilities effortlessly. With Prompteasy.ai, fine-tuning AI models takes just seconds, streamlining the process of creating personalized AI solutions. The best part is that you don't need to possess any knowledge of AI fine-tuning; our sophisticated models handle everything for you. As we launch Prompteasy, we are excited to offer it completely free of charge initially, with plans to introduce pricing options later this year. Our mission is to democratize AI, making it intelligent and accessible to everyone. We firmly believe that the real potential of AI is unlocked through the way we train and manage foundational models, rather than merely utilizing them as they come. You can set aside the hassle of generating extensive datasets; simply upload your relevant materials and engage with our AI using natural language. We will take care of constructing the dataset needed for fine-tuning, allowing you to simply converse with the AI, download the tailored dataset, and enhance GPT at your convenience. This innovative approach empowers users to harness the full capabilities of AI like never before.

Easily tweak and profit from your AI models with just a single click. Designed for AI startups, machine learning engineers, and researchers, Bakery is an innovative platform that simplifies the process of fine-tuning and monetizing AI models. Users can either create new datasets or upload existing ones, modify model parameters, and share their models on a dedicated marketplace. The platform accommodates a broad range of model types and offers access to community-curated datasets to aid in project creation. Bakery’s fine-tuning process is optimized for efficiency, allowing users to construct, evaluate, and deploy models seamlessly. Additionally, the platform integrates with tools such as Hugging Face and supports decentralized storage options, promoting adaptability and growth for various AI initiatives. Bakery also fosters a collaborative environment where contributors can work together on AI models while keeping their model parameters and data confidential. This approach guarantees accurate attribution and equitable revenue sharing among all participants, enhancing the overall collaborative experience in AI development. The platform's user-friendly interface further ensures that even those new to AI can navigate the complexities of model fine-tuning and monetization with ease.

Axolotl is an innovative open-source tool crafted to enhance the fine-tuning process of a variety of AI models, accommodating numerous configurations and architectures. This platform empowers users to train models using diverse methods such as full fine-tuning, LoRA, QLoRA, ReLoRA, and GPTQ. Additionally, users have the flexibility to customize their configurations through straightforward YAML files or by employing command-line interface overrides, while also being able to load datasets in various formats, whether custom or pre-tokenized. Axolotl seamlessly integrates with cutting-edge technologies, including xFormers, Flash Attention, Liger kernel, RoPE scaling, and multipacking, and it is capable of operating on single or multiple GPUs using Fully Sharded Data Parallel (FSDP) or DeepSpeed. Whether run locally or in the cloud via Docker, it offers robust support for logging results and saving checkpoints to multiple platforms, ensuring users can easily track their progress. Ultimately, Axolotl aims to make the fine-tuning of AI models not only efficient but also enjoyable, all while maintaining a high level of functionality and scalability. With its user-friendly design, it invites both novices and experienced practitioners to explore the depths of AI model training.

Introducing a versatile and precise synthetic data generation solution. In just minutes, you can create the specific data required for your perception system. Tailor scenarios to fit your needs with limitless variations available. Datasets can be generated effortlessly in the cloud. Anyverse delivers a robust synthetic data software platform that supports the design, training, validation, or refinement of your perception system. With unmatched cloud computing capabilities, it allows you to generate all necessary data significantly faster and at a lower cost than traditional real-world data processes. The Anyverse platform is modular, facilitating streamlined scene definition and dataset creation. The intuitive Anyverse™ Studio is a standalone graphical interface that oversees all functionalities of Anyverse, encompassing scenario creation, variability configuration, asset dynamics, dataset management, and data inspection. All data is securely stored in the cloud, while the Anyverse cloud engine handles the comprehensive tasks of scene generation, simulation, and rendering. This integrated approach not only enhances productivity but also ensures a seamless experience from conception to execution.

StableVicuna represents the inaugural large-scale open-source chatbot developed through reinforced learning from human feedback (RLHF). It is an advanced version of the Vicuna v0 13b model, which has undergone further instruction fine-tuning and RLHF training. To attain the impressive capabilities of StableVicuna, we use Vicuna as the foundational model and adhere to the established three-stage RLHF framework proposed by Steinnon et al. and Ouyang et al. Specifically, we perform additional training on the base Vicuna model with supervised fine-tuning (SFT), utilizing a blend of three distinct datasets. The first is the OpenAssistant Conversations Dataset (OASST1), which consists of 161,443 human-generated messages across 66,497 conversation trees in 35 languages. The second dataset is GPT4All Prompt Generations, encompassing 437,605 prompts paired with responses created by GPT-3.5 Turbo. Lastly, the Alpaca dataset features 52,000 instructions and demonstrations that were produced using OpenAI's text-davinci-003 model. This collective approach to training enhances the chatbot's ability to engage effectively in diverse conversational contexts.

Utilizing Automaton AI's ADVIT platform, you can effortlessly create, manage, and enhance high-quality training data alongside DNN models, all from a single interface. The system automatically optimizes data for each stage of the computer vision pipeline, allowing for a streamlined approach to data labeling processes and in-house data pipelines. You can efficiently handle both structured and unstructured datasets—be it video, images, or text—while employing automatic functions that prepare your data for every phase of the deep learning workflow. Once the data is accurately labeled and undergoes quality assurance, you can proceed with training your own model effectively. Deep neural network training requires careful hyperparameter tuning, including adjustments to batch size and learning rates, which are essential for maximizing model performance. Additionally, you can optimize and apply transfer learning to enhance the accuracy of your trained models. After the training phase, the model can be deployed into production seamlessly. ADVIT also supports model versioning, ensuring that model development and accuracy metrics are tracked in real-time. By leveraging a pre-trained DNN model for automatic labeling, you can further improve the overall accuracy of your models, paving the way for more robust applications in the future. This comprehensive approach to data and model management significantly enhances the efficiency of machine learning projects.

Mistral AI Studio serves as a comprehensive platform for organizations and development teams to create, tailor, deploy, and oversee sophisticated AI agents, models, and workflows, guiding them from initial concepts to full-scale production. This platform includes a variety of reusable components such as agents, tools, connectors, guardrails, datasets, workflows, and evaluation mechanisms, all enhanced by observability and telemetry features that allow users to monitor agent performance, identify root causes, and ensure transparency in AI operations. With capabilities like Agent Runtime for facilitating the repetition and sharing of multi-step AI behaviors, AI Registry for organizing and managing model assets, and Data & Tool Connections that ensure smooth integration with existing enterprise systems, Mistral AI Studio accommodates a wide range of tasks, from refining open-source models to integrating them seamlessly into infrastructure and deploying robust AI solutions at an enterprise level. Furthermore, the platform's modular design promotes flexibility, enabling teams to adapt and scale their AI initiatives as needed.

Elham.ai is a no-code machine-learning platform that enables users to create and implement AI models effortlessly without any programming knowledge. The platform features a user-friendly interface that allows for the uploading of datasets, selection of problem types such as classification and regression, while Elham takes care of essential processes like data preprocessing, feature engineering, model training, evaluation, and deployment. With integration capabilities through Zapier, it connects to ChatGPT/OpenAI, facilitating the transformation, summarization, or analysis of integration data using advanced AI models. Additionally, it provides streamlined sign-up and login processes, allowing teams to begin utilizing its features immediately. By simplifying the machine-learning workflow, the platform seeks to turn unprocessed data into meaningful insights while managing the intricacies of model tuning and infrastructure setup, thereby enhancing productivity for its users. Overall, Elham.ai represents a significant advancement in making AI accessible to a broader audience.

MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.

Discover the reasons why leading companies are turning to the Crux external data automation platform to enhance their external data integration, transformation, and monitoring without the need for additional personnel. Our cloud-native technology streamlines the processes of ingesting, preparing, observing, and consistently delivering any external dataset. Consequently, this enables you to receive high-quality data precisely where and when you need it, formatted correctly. Utilize features such as automated schema detection, inferred delivery schedules, and lifecycle management to swiftly create pipelines from diverse external data sources. Moreover, boost data discoverability across your organization with a private catalog that links and matches various data products. Additionally, you can enrich, validate, and transform any dataset, allowing for seamless integration with other data sources, which ultimately speeds up your analytics processes. With these capabilities, your organization can fully leverage its data assets to drive informed decision-making and strategic growth.

DeepEval offers an intuitive open-source framework designed for the assessment and testing of large language model systems, similar to what Pytest does but tailored specifically for evaluating LLM outputs. It leverages cutting-edge research to measure various performance metrics, including G-Eval, hallucinations, answer relevancy, and RAGAS, utilizing LLMs and a range of other NLP models that operate directly on your local machine. This tool is versatile enough to support applications developed through methods like RAG, fine-tuning, LangChain, or LlamaIndex. By using DeepEval, you can systematically explore the best hyperparameters to enhance your RAG workflow, mitigate prompt drift, or confidently shift from OpenAI services to self-hosting your Llama2 model. Additionally, the framework features capabilities for synthetic dataset creation using advanced evolutionary techniques and integrates smoothly with well-known frameworks, making it an essential asset for efficient benchmarking and optimization of LLM systems. Its comprehensive nature ensures that developers can maximize the potential of their LLM applications across various contexts.

Create sophisticated embedded machine learning applications without needing a doctorate. Gather data from sensors, audio sources, or cameras using devices, files, or cloud services to develop personalized datasets. Utilize automatic labeling tools that range from object detection to audio segmentation to streamline your workflow. Establish and execute reusable scripts that efficiently process extensive data sets in parallel through our cloud platform. Seamlessly integrate custom data sources, continuous integration and delivery tools, and deployment pipelines using open APIs to enhance your project’s capabilities. Speed up the development of custom ML pipelines with readily available DSP and ML algorithms that simplify the process. Make informed hardware choices by assessing device performance alongside flash and RAM specifications at every stage of development. Tailor DSP feature extraction algorithms and craft unique machine learning models using Keras APIs. Optimize your production model by analyzing visual insights related to datasets, model efficacy, and memory usage. Strive to achieve an ideal equilibrium between DSP configurations and model architecture, all while keeping memory and latency restrictions in mind. Furthermore, continually iterate on your models to ensure they evolve alongside your changing requirements and technological advancements.

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.

Explore and analyze a wide array of both open-source and proprietary AI models simultaneously. Replace expensive APIs with affordable custom AI solutions tailored for your needs. Adapt foundational models using your private data to ensure they meet your specific requirements. Smaller fine-tuned models can rival the performance of GPT-4 while being up to 90% more cost-effective. With Airtrain’s LLM-assisted scoring system, model assessment becomes straightforward by utilizing your task descriptions. You can deploy your personalized models through the Airtrain API, whether in the cloud or within your own secure environment. Assess and contrast both open-source and proprietary models throughout your complete dataset, focusing on custom attributes. Airtrain’s advanced AI evaluators enable you to score models based on various metrics for a completely tailored evaluation process. Discover which model produces outputs that comply with the JSON schema needed for your agents and applications. Your dataset will be evaluated against models using independent metrics that include length, compression, and coverage, ensuring a comprehensive analysis of performance. This way, you can make informed decisions based on your unique needs and operational context.

Stability AI, along with its CarperAI lab, is excited to unveil Stable Beluga 1 and its advanced successor, Stable Beluga 2, previously known as FreeWilly, both of which are robust new Large Language Models (LLMs) available for public use. These models exhibit remarkable reasoning capabilities across a wide range of benchmarks, showcasing their versatility and strength. Stable Beluga 1 is built on the original LLaMA 65B foundation model and has undergone meticulous fine-tuning with a novel synthetically-generated dataset utilizing Supervised Fine-Tune (SFT) in the conventional Alpaca format. In a similar vein, Stable Beluga 2 utilizes the LLaMA 2 70B foundation model, pushing the boundaries of performance in the industry. Their development marks a significant step forward in the evolution of open access AI technologies.

Eliminate data outages by proactively identifying and resolving data quality problems before they enter production. Achieve full test coverage of your data pipelines in just one day, going from 0 to 100%. With automatic regression testing across billions of rows, understand the impact of each code modification. Streamline change management processes, enhance data literacy, ensure compliance, and minimize the time taken to respond to incidents. Stay ahead of potential data issues by utilizing automated anomaly detection, ensuring you're always informed. Datafold’s flexible machine learning model adjusts to seasonal variations and trends in your data, allowing for the creation of dynamic thresholds. Save significant time spent analyzing data by utilizing the Data Catalog, which simplifies the process of locating relevant datasets and fields while providing easy exploration of distributions through an intuitive user interface. Enjoy features like interactive full-text search, data profiling, and a centralized repository for metadata, all designed to enhance your data management experience. By leveraging these tools, you can transform your data processes and improve overall efficiency.

Fortanix Confidential AI presents a comprehensive platform that allows data teams to handle sensitive datasets and deploy AI/ML models exclusively within secure computing environments, integrating managed infrastructure, software, and workflow orchestration to uphold privacy compliance across organizations. This service features on-demand infrastructure driven by the high-performance Intel Ice Lake third-generation scalable Xeon processors, enabling the execution of AI frameworks within Intel SGX and other enclave technologies while ensuring no external visibility. Moreover, it offers hardware-backed execution proofs and comprehensive audit logs to meet rigorous regulatory standards, safeguarding every aspect of the MLOps pipeline, from data ingestion through Amazon S3 connectors or local uploads to model training, inference, and fine-tuning, while also ensuring compatibility across a wide range of models. By leveraging this platform, organizations can significantly enhance their ability to manage sensitive information responsibly while advancing their AI initiatives.

While generative AI is a relatively recent development, our efforts over the last five years have paved the way for this moment. Deep Lake merges the strengths of data lakes and vector databases to craft and enhance enterprise-level solutions powered by large language models, allowing for continual refinement. However, vector search alone does not address retrieval challenges; a serverless query system is necessary for handling multi-modal data that includes embeddings and metadata. You can perform filtering, searching, and much more from either the cloud or your local machine. This platform enables you to visualize and comprehend your data alongside its embeddings, while also allowing you to monitor and compare different versions over time to enhance both your dataset and model. Successful enterprises are not solely reliant on OpenAI APIs, as it is essential to fine-tune your large language models using your own data. Streamlining data efficiently from remote storage to GPUs during model training is crucial. Additionally, Deep Lake datasets can be visualized directly in your web browser or within a Jupyter Notebook interface. You can quickly access various versions of your data, create new datasets through on-the-fly queries, and seamlessly stream them into frameworks like PyTorch or TensorFlow, thus enriching your data processing capabilities. This ensures that users have the flexibility and tools needed to optimize their AI-driven projects effectively.

LLaMA-Factory is an innovative open-source platform aimed at simplifying and improving the fine-tuning process for more than 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It accommodates a variety of fine-tuning methods such as Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, empowering users to personalize models with ease. The platform has shown remarkable performance enhancements; for example, its LoRA tuning achieves training speeds that are up to 3.7 times faster along with superior Rouge scores in advertising text generation tasks when compared to conventional techniques. Built with flexibility in mind, LLaMA-Factory's architecture supports an extensive array of model types and configurations. Users can seamlessly integrate their datasets and make use of the platform’s tools for optimized fine-tuning outcomes. Comprehensive documentation and a variety of examples are available to guide users through the fine-tuning process with confidence. Additionally, this platform encourages collaboration and sharing of techniques among the community, fostering an environment of continuous improvement and innovation.

Entry Point AI serves as a cutting-edge platform for optimizing both proprietary and open-source language models. It allows users to manage prompts, fine-tune models, and evaluate their performance all from a single interface. Once you hit the ceiling of what prompt engineering can achieve, transitioning to model fine-tuning becomes essential, and our platform simplifies this process. Rather than instructing a model on how to act, fine-tuning teaches it desired behaviors. This process works in tandem with prompt engineering and retrieval-augmented generation (RAG), enabling users to fully harness the capabilities of AI models. Through fine-tuning, you can enhance the quality of your prompts significantly. Consider it an advanced version of few-shot learning where key examples are integrated directly into the model. For more straightforward tasks, you have the option to train a lighter model that can match or exceed the performance of a more complex one, leading to reduced latency and cost. Additionally, you can configure your model to avoid certain responses for safety reasons, which helps safeguard your brand and ensures proper formatting. By incorporating examples into your dataset, you can also address edge cases and guide the behavior of the model, ensuring it meets your specific requirements effectively. This comprehensive approach ensures that you not only optimize performance but also maintain control over the model's responses.

Ludwig serves as a low-code platform specifically designed for the development of tailored AI models, including large language models (LLMs) and various deep neural networks. With Ludwig, creating custom models becomes a straightforward task; you only need a simple declarative YAML configuration file to train an advanced LLM using your own data. It offers comprehensive support for learning across multiple tasks and modalities. The framework includes thorough configuration validation to identify invalid parameter combinations and avert potential runtime errors. Engineered for scalability and performance, it features automatic batch size determination, distributed training capabilities (including DDP and DeepSpeed), parameter-efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and the ability to handle larger-than-memory datasets. Users enjoy expert-level control, allowing them to manage every aspect of their models, including activation functions. Additionally, Ludwig facilitates hyperparameter optimization, offers insights into explainability, and provides detailed metric visualizations. Its modular and extensible architecture enables users to experiment with various model designs, tasks, features, and modalities with minimal adjustments in the configuration, making it feel like a set of building blocks for deep learning innovations. Ultimately, Ludwig empowers developers to push the boundaries of AI model creation while maintaining ease of use.

Microsoft Foundry Models centralizes more than 11,000 leading AI models, offering enterprises a single place to explore, compare, fine-tune, and deploy AI for any use case. It includes top-performing models from OpenAI, Anthropic, Cohere, Meta, Mistral AI, DeepSeek, Black Forest Labs, and Microsoft’s own Azure OpenAI offerings. Teams can search by task—such as reasoning, generation, multimodal, or domain-specific workloads—and instantly test models in a built-in playground. Foundry Models simplifies customization with ready-to-use fine-tuning pipelines that require no infrastructure setup. Developers can upload internal datasets to benchmark and evaluate model accuracy, ensuring the right fit for production environments. With seamless deployment into managed instances, organizations get automatic scaling, traffic management, and secure hosting. The platform is backed by Azure’s enterprise-grade security and over 100 compliance certifications, supporting regulated industries and global operations. By integrating discovery, testing, tuning, and deployment, Foundry Models dramatically shortens AI development cycles and speeds time to value.

NVIDIA Cosmos serves as a cutting-edge platform tailored for developers, featuring advanced generative World Foundation Models (WFMs), sophisticated video tokenizers, safety protocols, and a streamlined data processing and curation system aimed at enhancing the development of physical AI. This platform empowers developers who are focused on areas such as autonomous vehicles, robotics, and video analytics AI agents to create highly realistic, physics-informed synthetic video data, leveraging an extensive dataset that encompasses 20 million hours of both actual and simulated footage, facilitating the rapid simulation of future scenarios, the training of world models, and the customization of specific behaviors. The platform comprises three primary types of WFMs: Cosmos Predict, which can produce up to 30 seconds of continuous video from various input modalities; Cosmos Transfer, which modifies simulations to work across different environments and lighting conditions for improved domain augmentation; and Cosmos Reason, a vision-language model that implements structured reasoning to analyze spatial-temporal information for effective planning and decision-making. With these capabilities, NVIDIA Cosmos significantly accelerates the innovation cycle in physical AI applications, fostering breakthroughs across various industries.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

Lleverage is used by product and engineering teams to create AI features that are production-ready. Fast. Even if you don't have any prior AI experience. Create comprehensive workflows by using the powerful visual designer, designed to simplify building AI features & pipelines from scratch. Our powerful visual builder simplifies the creation of AI pipelines and features. Use detailed logs to optimize features continuously. Using proprietary datasets, you can fine-tune foundational models to your specific needs and customize them.

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.

DeepSeek-VL is an innovative open-source model that integrates vision and language capabilities, catering to practical applications in real-world contexts. Our strategy revolves around three fundamental aspects: we prioritize gathering diverse and scalable data that thoroughly encompasses various real-life situations, such as web screenshots, PDFs, OCR outputs, charts, and knowledge-based information, to ensure a holistic understanding of practical environments. Additionally, we develop a taxonomy based on actual user scenarios and curate a corresponding instruction tuning dataset that enhances the model's performance. This fine-tuning process significantly elevates user satisfaction and effectiveness in real-world applications. To address efficiency while meeting the requirements of typical scenarios, DeepSeek-VL features a hybrid vision encoder that adeptly handles high-resolution images (1024 x 1024) without incurring excessive computational costs. Moreover, this design choice not only optimizes performance but also ensures accessibility for a broader range of users and applications.

Bitext specializes in creating multilingual hybrid synthetic training datasets tailored for intent recognition and the fine-tuning of language models. These datasets combine extensive synthetic text generation with careful expert curation and detailed linguistic annotation, which encompasses various aspects like lexical, syntactic, semantic, register, and stylistic diversity, all aimed at improving the understanding, precision, and adaptability of conversational models. For instance, their open-source customer support dataset includes approximately 27,000 question-and-answer pairs, totaling around 3.57 million tokens, 27 distinct intents across 10 categories, 30 types of entities, and 12 tags for language generation, all meticulously anonymized to meet privacy, bias reduction, and anti-hallucination criteria. Additionally, Bitext provides industry-specific datasets, such as those for travel and banking, and caters to over 20 sectors in various languages while achieving an impressive accuracy rate exceeding 95%. Their innovative hybrid methodology guarantees that the training data is not only scalable and multilingual but also compliant with privacy standards, effectively reduces bias, and is well-prepared for the enhancement and deployment of language models. This comprehensive approach positions Bitext as a leader in delivering high-quality training resources for advanced conversational AI systems.

Our advanced multimodal assistant is meticulously crafted with a focus on privacy, security, and operational efficiency. Yasa is trained to interpret various forms of content, including text, images, videos, and tabular data, with plans to expand to additional modalities in the future. It can assist you in brainstorming for creative projects, answering fundamental questions, or extracting valuable insights from your internal datasets. With just a few straightforward commands, you can generate, train, compress, or deploy it on your own servers. Our proprietary algorithms enable you to customize the model according to your specific data and requirements. We utilize innovative techniques that encompass retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to optimize our model based on your unique datasets, ensuring that it meets your operational needs effectively. In doing so, we aim to enhance user experience and deliver tailored solutions that drive productivity and innovation.

Oumi is an entirely open-source platform that enhances the complete lifecycle of foundation models, encompassing everything from data preparation and training to evaluation and deployment. It facilitates the training and fine-tuning of models with parameter counts ranging from 10 million to an impressive 405 billion, utilizing cutting-edge methodologies such as SFT, LoRA, QLoRA, and DPO. Supporting both text-based and multimodal models, Oumi is compatible with various architectures like Llama, DeepSeek, Qwen, and Phi. The platform also includes tools for data synthesis and curation, allowing users to efficiently create and manage their training datasets. For deployment, Oumi seamlessly integrates with well-known inference engines such as vLLM and SGLang, which optimizes model serving. Additionally, it features thorough evaluation tools across standard benchmarks to accurately measure model performance. Oumi's design prioritizes flexibility, enabling it to operate in diverse environments ranging from personal laptops to powerful cloud solutions like AWS, Azure, GCP, and Lambda, making it a versatile choice for developers. This adaptability ensures that users can leverage the platform regardless of their operational context, enhancing its appeal across different use cases.

Langtrace is an open-source observability solution designed to gather and evaluate traces and metrics, aiming to enhance your LLM applications. It prioritizes security with its cloud platform being SOC 2 Type II certified, ensuring your data remains highly protected. The tool is compatible with a variety of popular LLMs, frameworks, and vector databases. Additionally, Langtrace offers the option for self-hosting and adheres to the OpenTelemetry standard, allowing traces to be utilized by any observability tool of your preference and thus avoiding vendor lock-in. Gain comprehensive visibility and insights into your complete ML pipeline, whether working with a RAG or a fine-tuned model, as it effectively captures traces and logs across frameworks, vector databases, and LLM requests. Create annotated golden datasets through traced LLM interactions, which can then be leveraged for ongoing testing and improvement of your AI applications. Langtrace comes equipped with heuristic, statistical, and model-based evaluations to facilitate this enhancement process, thereby ensuring that your systems evolve alongside the latest advancements in technology. With its robust features, Langtrace empowers developers to maintain high performance and reliability in their machine learning projects.

DataGen delivers cutting-edge AI synthetic data and generative AI solutions designed to accelerate machine learning initiatives with privacy-compliant training data. Their core platform, SynthEngyne, enables the creation of custom datasets in multiple formats—text, images, tabular, and time-series—with fast, scalable real-time processing. The platform emphasizes data quality through rigorous validation and deduplication, ensuring reliable training inputs. Beyond synthetic data, DataGen offers end-to-end AI development services including full-stack model deployment, custom fine-tuning aligned with business goals, and advanced intelligent automation systems to streamline complex workflows. Flexible subscription plans range from a free tier for small projects to pro and enterprise tiers that include API access, priority support, and unlimited data spaces. DataGen’s synthetic data benefits sectors such as healthcare, automotive, finance, and retail by enabling safer, compliant, and efficient AI model training. Their platform supports domain-specific custom dataset creation while maintaining strict confidentiality. DataGen combines innovation, reliability, and scalability to help businesses maximize the impact of AI.

An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.

AI is today blocked by a lack of labeled data. Not models. The first data-centric AI platform powered by a programmatic approach will unblock AI. With its unique programmatic approach, Snorkel AI is leading a shift from model-centric AI development to data-centric AI. By replacing manual labeling with programmatic labeling, you can save time and money. You can quickly adapt to changing data and business goals by changing code rather than manually re-labeling entire datasets. Rapid, guided iteration of the training data is required to develop and deploy AI models of high quality. Versioning and auditing data like code leads to faster and more ethical deployments. By collaborating on a common interface, which provides the data necessary to train models, subject matter experts can be integrated. Reduce risk and ensure compliance by labeling programmatically, and not sending data to external annotators.

OpenEuroLLM represents a collaborative effort between prominent AI firms and research organizations across Europe, aimed at creating a suite of open-source foundational models to promote transparency in artificial intelligence within the continent. This initiative prioritizes openness by making data, documentation, training and testing code, and evaluation metrics readily available, thereby encouraging community participation. It is designed to comply with European Union regulations, with the goal of delivering efficient large language models that meet the specific standards of Europe. A significant aspect of the project is its commitment to linguistic and cultural diversity, ensuring that multilingual capabilities cover all official EU languages and potentially more. The initiative aspires to broaden access to foundational models that can be fine-tuned for a range of applications, enhance evaluation outcomes across different languages, and boost the availability of training datasets and benchmarks for researchers and developers alike. By sharing tools, methodologies, and intermediate results, transparency is upheld during the entire training process, fostering trust and collaboration within the AI community. Ultimately, OpenEuroLLM aims to pave the way for more inclusive and adaptable AI solutions that reflect the rich diversity of European languages and cultures.

ThinkData Works provides a robust catalog platform for discovering, managing, and sharing data from both internal and external sources. Enrichment solutions combine partner data with your existing datasets to produce uniquely valuable assets that can be shared across your entire organization. The ThinkData Works platform and enrichment solutions make data teams more efficient, improve project outcomes, replace multiple existing tech solutions, and provide you with a competitive advantage.

Amazon Nova Forge gives enterprises unprecedented control to build highly specialized frontier models using Nova’s early checkpoints and curated training foundations. By blending proprietary data with Amazon’s trusted datasets, organizations can shape models with deep domain understanding and long-term adaptability. The platform covers every phase of development, enabling teams to start with continued pre-training, refine capabilities with supervised fine-tuning, and optimize performance with reinforcement learning in their own environments. Nova Forge also includes built-in responsible AI guardrails that help ensure safer deployments across industries like pharmaceuticals, finance, and manufacturing. Its seamless integration with SageMaker AI makes setup, training, and hosting effortless, even for companies managing large-scale model development. Customer testimonials highlight dramatic improvements in accuracy, latency, and workflow consolidation, often outperforming larger general-purpose models. With early access to new Nova architectures, teams can stay ahead of the frontier without maintaining expensive infrastructure. Nova Forge ultimately gives organizations a practical, fast, and scalable way to create powerful AI tailored to their unique needs.

Twine AI provides customized services for the collection and annotation of speech, image, and video data, catering to the creation of both standard and bespoke datasets aimed at enhancing AI/ML model training and fine-tuning. The range of offerings includes audio services like voice recordings and transcriptions available in over 163 languages and dialects, alongside image and video capabilities focused on biometrics, object and scene detection, and drone or satellite imagery. By utilizing a carefully selected global community of 400,000 to 500,000 contributors, Twine emphasizes ethical data gathering, ensuring consent and minimizing bias while adhering to ISO 27001-level security standards and GDPR regulations. Each project is comprehensively managed, encompassing technical scoping, proof of concept development, and complete delivery, with the support of dedicated project managers, version control systems, quality assurance workflows, and secure payment options that extend to more than 190 countries. Additionally, their service incorporates human-in-the-loop annotation, reinforcement learning from human feedback (RLHF) strategies, dataset versioning, audit trails, and comprehensive dataset management, thereby facilitating scalable training data that is rich in context for sophisticated computer vision applications. This holistic approach not only accelerates the data preparation process but also ensures that the resulting datasets are robust and highly relevant for various AI initiatives.

Latitude is a comprehensive platform for prompt engineering, helping product teams design, test, and optimize AI prompts for large language models (LLMs). It provides a suite of tools for importing, refining, and evaluating prompts using real-time data and synthetic datasets. The platform integrates with production environments to allow seamless deployment of new prompts, with advanced features like automatic prompt refinement and dataset management. Latitude’s ability to handle evaluations and provide observability makes it a key tool for organizations seeking to improve AI performance and operational efficiency.