Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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RunPod
RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.
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Vertex AI Notebooks
Vertex AI Notebooks offers a comprehensive, end-to-end solution for machine learning development within Google Cloud. It combines the power of Colab Enterprise and Vertex AI Workbench to give data scientists and developers the tools to accelerate model training and deployment. This fully managed platform provides seamless integration with BigQuery, Dataproc, and other Google Cloud services, enabling efficient data exploration, visualization, and advanced ML model development. With built-in features like automated infrastructure management, users can focus on model building without worrying about backend maintenance. Vertex AI Notebooks also supports collaborative workflows, making it ideal for teams to work on complex AI projects together.
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MosaicML
Easily train and deploy large-scale AI models with just a single command by pointing to your S3 bucket—then let us take care of everything else, including orchestration, efficiency, node failures, and infrastructure management. The process is straightforward and scalable, allowing you to utilize MosaicML to train and serve large AI models using your own data within your secure environment. Stay ahead of the curve with our up-to-date recipes, techniques, and foundation models, all developed and thoroughly tested by our dedicated research team. With only a few simple steps, you can deploy your models within your private cloud, ensuring that your data and models remain behind your own firewalls. You can initiate your project in one cloud provider and seamlessly transition to another without any disruptions. Gain ownership of the model trained on your data while being able to introspect and clarify the decisions made by the model. Customize content and data filtering to align with your business requirements, and enjoy effortless integration with your existing data pipelines, experiment trackers, and other essential tools. Our solution is designed to be fully interoperable, cloud-agnostic, and validated for enterprise use, ensuring reliability and flexibility for your organization. Additionally, the ease of use and the power of our platform allow teams to focus more on innovation rather than infrastructure management.
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