Artificial Intelligence and Machine Learning with Docker Enterprise
Docker Enterprise supports Nvidia GPUs to enable rapid, high-precision computation for all your data science and Big Data needs
VIDEO
Docker Enterprise 3.1 GPU Worker Nodes
Learn how Docker Enterprise runs on Nvidia GPUs on bare metal or public clouds for data science and Big Data.
LEARN MOREOverview
Containerization with Docker Enterprise enables data scientists to be more productive and directly improves the speed of innovation in their organizations. Expedite and improve data models, and as a result, make breakthroughs possible, faster and reliably.
Portability of Data Science Environments
Streamline experiments, analysis, and practices with Docker Enterprise by capturing environments, including configuration and dependencies, in a portable package that facilitates reproducibility at scale.
- The underlying Docker image records all the instructions to quickly (re)build a container when necessary.
- The container captures an experiment’s state (e.g., data, code, results, package versions, parameters, etc.) at any point in time as a whole and can be deployed on your on-premises infrastructure or any of the cloud platforms such as AWS, Microsoft Azure and Google Cloud Platform.
- If needed, data scientists can set up and run multiple configurations at the same time using Docker containers in isolation to keep each environment independent from the others, even on the same system.
Simplified Collaboration
Use Docker Trusted Registry to securely share experiments with colleagues, and build on prior work to take innovation to the next level.
- Research teams can securely store, manage and collaborate on container images for experiments and analyses, with the ability to manage users and permissions.
- Test models against production grade standards before sharing with colleagues.
CASE STUDY:
Discover how data scientists at BCG Gamma use Docker Enterprise to scale and deliver AI and ML apps faster.
READ FULL CASE STUDY