Cloud notebooks
AI Notebooks provides a quick way to launch Jupyter or VS Code notebooks in the cloud for development and experimentation.
OVHcloud AI & Machine Learning is a Public Cloud portfolio for building, training, deploying, and integrating AI and machine learning models. It supports data scientists, developers, and organisations working with predictive analytics and generative AI applications.
OVHcloud AI & Machine Learning is a Public Cloud portfolio for the AI lifecycle, from experimentation and model training to production deployment and application integration. Its range includes cloud notebooks, GPU-focused training services, model deployment, and API access to generative AI models.
The portfolio is aimed at data scientists, developers, and organisations building predictive analytics, machine learning, deep learning, or generative AI applications. OVHcloud presents the services as a way to use managed cloud capabilities while retaining access to data, open-source approaches, and standard recovery protocols.
The supplied material does not specify individual AI product limits, supported frameworks, GPU models, API quotas, or detailed pricing. Those points should be checked on the product-specific pages before selecting a service.
AI Notebooks provides a quick way to launch Jupyter or VS Code notebooks in the cloud for development and experimentation.
AI Training is designed for training AI, machine learning, and deep learning models, with a focus on using GPU resources efficiently.
AI Deploy supports deploying machine learning models and applications into production, including the creation of API access points for predictions.
AI Endpoints provides easy-to-integrate generative AI models and secure APIs for application development. The page also identifies a newer developer-focused AI Endpoints service in early access.
OVHcloud highlights open-source solutions in its data services and specifically references technologies such as Apache Spark.
The portfolio is positioned around reversibility, with data recoverable through standard protocols. Related Public Cloud capabilities include unlimited, unmetered bandwidth and the private vRack network for connecting services.
Data scientists can use Jupyter or VS Code environments in the cloud to explore datasets, write code, and iterate on AI or machine learning experiments.
Teams can use AI Training for model-building workloads that require cloud compute and GPU resources, while considering GPU usage as part of the training workflow.
Development teams can deploy a trained machine learning model with AI Deploy and create API access points so other software can request predictions.
Application developers can use AI Endpoints to connect software to generative AI models through APIs rather than building the model-serving layer themselves.
Organisations working with predictive analysis or larger data workflows can combine the AI portfolio with related Public Cloud analytics services and open-source-oriented infrastructure.
The portfolio includes AI Notebooks, AI Training, AI Deploy, and AI Endpoints. Together, these services address notebook-based development, model training, production deployment, and generative AI API integration.
Yes. The AI Deploy description says it can deploy machine learning models and applications into production and create API access points for predictions.
The AI Notebooks description specifically mentions Jupyter and VS Code notebooks in the cloud.
Yes. AI Endpoints is described as providing easy-to-integrate generative AI models through secure APIs. A newer developer-oriented AI Endpoints service is identified as being in early access.
The supplied evidence confirms that OVHcloud has a Public Cloud prices catalogue and offers US$200 in free Public Cloud credit for starting a project. It does not provide AI-specific prices or usage limits, so current product pricing should be checked in the relevant pricing or control-panel pages.
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