Vertex AI
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case.
Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.
Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex.
Learn more
Ango Hub
Ango Hub is a quality-focused, enterprise-ready data annotation platform for AI teams, available on cloud and on-premise. It supports computer vision, medical imaging, NLP, audio, video, and 3D point cloud annotation, powering use cases from autonomous driving and robotics to healthcare AI.
Built for AI fine-tuning, RLHF, LLM evaluation, and human-in-the-loop workflows, Ango Hub boosts throughput with automation, model-assisted pre-labeling, and customizable QA while maintaining accuracy. Features include centralized instructions, review pipelines, issue tracking, and consensus across up to 30 annotators. With nearly twenty labeling tools—such as rotated bounding boxes, label relations, nested conditional questions, and table-based labeling—it supports both simple and complex projects. It also enables annotation pipelines for chain-of-thought reasoning and next-gen LLM training and enterprise-grade security with HIPAA compliance, SOC 2 certification, and role-based access controls.
Learn more
Kognic
Kognic offers an advanced annotation platform specifically designed for sensor-fusion data, aiming to reduce annotation efforts and costs while maintaining high-quality standards. It supports various data labeling needs, from simple static objects to complex scenarios, accommodating 2D/3D objects, 2D instance segmentation, and free space annotations. A key feature is the co-pilot, which leverages imported predictions as prompts for automation, significantly reducing annotation time by up to 68% without compromising quality. This approach enables more efficient human feedback where it's needed most. Kognic also emphasizes refining critical data to enhance AI performance, offering smart sorting based on model confidence and loss metrics, advanced filtering of predicted and annotated objects, and effortless creation of data chunks for targeted review. It is enterprise-ready, and developed for global-scale missions.
Learn more
