Google Compute Engine
Compute Engine is Google's infrastructure as a service (IaaS) platform for organizations to create and run cloud-based virtual machines.
Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications. Integrate Compute with other Google Cloud services such as AI/ML and data analytics. Make reservations to help ensure your applications have the capacity they need as they scale. Save money just for running Compute with sustained-use discounts, and achieve greater savings when you use committed-use discounts.
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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.
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Pipeshift
Pipeshift is a modular orchestration platform designed to facilitate the building, deployment, and scaling of open source AI components, including embeddings, vector databases, large language models, vision models, and audio models, across any cloud environment or on-premises infrastructure. The platform offers end-to-end orchestration, ensuring seamless integration and management of AI workloads, and is 100% cloud-agnostic, providing flexibility in deployment. With enterprise-grade security, Pipeshift addresses the needs of DevOps and MLOps teams aiming to establish production pipelines in-house, moving beyond experimental API providers that may lack privacy considerations. Key features include an enterprise MLOps console for managing various AI workloads such as fine-tuning, distillation, and deployment; multi-cloud orchestration with built-in auto-scalers, load balancers, and schedulers for AI models; and Kubernetes cluster management.
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