Best RLHF Tools with a Free Trial

Reinforcement Learning with Human Feedback (RLHF) in Vertex AI enables businesses to develop models that learn from both automated rewards and human feedback. This method enhances the learning process by allowing human evaluators to guide the model toward better decision-making. RLHF is especially useful for tasks where traditional supervised learning may fall short, as it combines the strengths of human intuition with machine efficiency. New customers receive $300 in free credits to explore RLHF techniques and apply them to their own machine learning projects. By leveraging this approach, businesses can develop models that adapt more effectively to complex environments and user feedback.

Supercharge your AI with human expertise. SUPA is here to help you streamline your data at any stage: collection, curation, annotation, model validation and human feedback. Better data, better AI. SUPA is trusted by AI teams to solve their human data needs. Our lightning-fast machine-led labeling platform integrates with our diverse workforce to provide high-quality data at scale, making it the most cost-efficient solution for your AI. We do next-gen labeling for ‍next-gen AI. Our use cases range from LLM generation, data curation, Segment Anything (SAM) output validation to sketch generation and semantic segmentation.

Our cloud-based annotation platform helps you to create projects, annotate, monitor progress and download annotation results. Your tasks can be assigned either to our managed annotation team or to our global crowd.

Labellerr is a data annotation platform designed to expedite the preparation of high-quality labeled datasets for AI and machine learning models. It supports various data types, including images, videos, text, PDFs, and audio, catering to diverse annotation needs. The platform offers automated annotation features, such as model-assisted labeling and active learning, to accelerate the labeling process. Additionally, Labellerr provides advanced analytics and smart quality assurance tools to ensure the accuracy and reliability of annotations. For projects requiring specialized knowledge, Labellerr offers expert-in-the-loop services, including access to professionals in fields like healthcare and automotive.

The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input.

Achieve peak model performance with the best data. Create & manage training data for any visual modality, debug models and boost performance, and make foundation models your own. Expert review, QA and QC workflows help you deliver higher quality datasets to your artificial intelligence teams, helping improve model performance. Connect your data and models with Encord's Python SDK and API access to create automated pipelines for continuously training ML models. Improve model accuracy by identifying errors and biases in your data, labels and models.

Scale Data Engine helps ML teams build better datasets. Bring together your data, ground truth, and model predictions to effortlessly fix model failures and data quality issues. Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Data Engine. Significantly improve model performance by uncovering and fixing model failures. Find and label high-value data by curating unlabeled data with active learning and edge case mining. Curate the best datasets by collaborating with ML engineers, labelers, and data ops on the same platform. Easily visualize and explore your data to quickly find edge cases that need labeling. Check how well your models are performing and always ship the best one. Easily view your data, metadata, and aggregate statistics with rich overlays, using our powerful UI. Scale Data Engine supports visualization of images, videos, and lidar scenes, overlaid with all associated labels, predictions, and metadata.

Manage unstructured data and pipelines to develop AI solutions at amazing speed. Enterprise-grade data platform for vision AI. Dataloop is a one-stop shop for building and deploying powerful computer vision pipelines data labeling, automating data ops, customizing production pipelines and weaving the human-in-the-loop for data validation. Our vision is to make machine learning-based systems accessible, affordable and scalable for all. Explore and analyze vast quantities of unstructured data from diverse sources. Rely on automated preprocessing and embeddings to identify similarities and find the data you need. Curate, version, clean, and route your data to wherever it’s needed to create exceptional AI applications.