AI model fine-tuning

Scale specializes in data annotation, curation, and collection specifically tailored for AI applications, including autonomous vehicles and defense. Their offerings span from generative AI to reinforcement learning (RLHF), creating custom large language models (LLMs) to adapt AI initiatives to specific enterprise data. Collaborating with leading AI models from OpenAI, Google PaLM, and others, Scale facilitates the integration of enterprise data with these AI technologies. They deliver a full-stack generative AI platform, powering applications for federal, defense, and commercial sectors, emphasizing quality, safety, and strategic differentiation in AI deployment and management.

Scale

Data annotation, AI model training, generative platform, enterprise integration.

AI data labeling

Brev offers a cloud-based development environment optimized for machine learning, providing easy access to powerful GPUs. Customers can fine-tune their own language models or utilize pre-configured notebooks, including ControlNet for Stable Diffusion. Brev enables users to generate creative visuals such as logos and QR codes, as well as interact with PDFs through OCR. It simplifies the process by connecting to cloud providers to source the best-priced GPUs, configuring them for users' needs, and offering SSH wrapped connections for seamless code editor integration. From individuals to engineers of esteemed companies, Brev is trusted for their robust infrastructure and user-friendly setup.

Brev

Brev streamlines AI model training, tuning on cloud GPUs.

AI model infrastructure

Labelbox offers a data-centric platform for building AI applications. It includes data labeling, curation, model evaluation, and workflows tailored for AI data set preparation. Users can pre-label data with existing models, compare model predictions with ground truth, and utilize reinforcement learning with human feedback. The platform also features analytics dashboards and tools for evaluating large language models and vision tools. With Labelbox, companies in industries like agriculture, retail, and healthcare can improve their AI models with high-quality training data, optimize model performance, and automate tasks, significantly reducing the time spent on data management and curation.

Labelbox

Platform for data labeling, AI model training, and evaluation.

Automorphic specializes in making language models more secure and self-improving. They offer a platform called Conduit which allows users to infuse language models with knowledge using as few as 10 samples, overcoming the inefficiencies of prompt stuffing and context-window limitations. Conduit permits fine-tuning and combining behavioral and knowledge adapters. It incorporates human-in-the-loop feedback for rapid model iteration and deployment. The system also improves models continuously by augmenting datasets. Conduit supports rapid adapter loading, is OpenAI API compatible, and offers on-premises deployment to ensure data security. Automorphic focuses on tuning, feedback, and performance, simplifying model deployment.

Automorphic

Enhances language models with efficient knowledge infusion and adaptability.

Together AI is a cloud-based platform specializing in generative AI technologies, providing the fastest cloud services for AI model inference, fine-tuning, and training. They allow customers to build and train generative AI models using private data, offering dedicated GPU clusters for training with custom models. Leveraging leading-edge AI research and breakthroughs like Cocktail SGD, FlashAttention 2, and sub-quadratic model architectures, Together AI enables the efficient creation of state-of-the-art models. They also support open-source initiatives with the RedPajama project and offer a range of AI language and image models fine-tuned for specialized tasks.

Together AI

Cloud platform offering API, training, and hosting for generative AI.

OpenPipe is a startup offering a platform for training and deploying machine learning models with a focus on lowering costs and shortened deployment loops. Users can automatically capture production data, train models in a few clicks, and deploy them on managed endpoints ready for scaling. They highlight significant cost savings over GPT-4, reduced latency, and simpler model management. With various use cases from CEOs and CTOs, OpenPipe has saved customers $7M this year, boasting features like autoscaling, analytics, and support for hundreds of thousands of training rows and fine-tuning of open-source models like Mistral and Llama 2.

OpenPipe

Train, deploy fine-tuned AI models quickly and cost-effectively.

AI model fine-tuning

Similar spaces