Domino Data Science Workbench

Whether you’re a beginner or a seasoned data scientist, Domino provides a data science workbench to help you get more done. With a governed environment, scalable compute, and a range of tools for deploying, publishing, and reproducing results, Domino helps data scientists work more efficiently, with better results for their teams.

Domino’s data science workbench enables you to run hundreds of machine learning experiments in parallel. The workbench provides elastically scalable compute, and fosters collaboration and reproducibility. It also makes it easy to build and deploy your models. It enables you to share and publish your results with ease.

Domino’s data science workbench includes tools that allow you to build and run models, publish your results, and create reproducible workflows. The platform also provides tools to help you collaborate with other data scientists. This includes a jupyter lab that integrates with your data science tool of choice. You can build and run your models with the help of Flask, Shiny, and Dash. It also includes tools to publish and distribute your results, including Docker images, CI/CD pipelines, and REST API endpoints.

Domino’s data science workbench also includes tools to automate and scale your production model management processes. These tools help you to easily deploy, scale, and automate your models, as well as monitor and manage resource usage. It also helps to make your workflows reproducible, which results in a higher return on investment for your company. It also allows you to share your work with the rest of your team, and it makes it easy to build custom toolsets for your data analysis needs.

Domino’s data science workbench is built to address gaps in modern analytical workflows. It offers scalable compute and a governed environment, so you can work more efficiently and collaborate more effectively. You can run hundreds of machine learning experiments in parallel, and compare results with ease. You can publish your results through web or email, and you can use tools like Shiny, Dash, and Flask to create your own apps.

Domino centralizes your models so you can quickly and easily spread jobs across machines and schedule automatic recurring jobs. It also provides a centralized snapshot of your project when it’s finished. This snapshot helps to detect conflicts and enable you to share and publish your work. It also makes it easy to manage horizontal scalability and high availability. It also offers easy-to-access NVIDIA GPUs, distributed frameworks, and other compute clusters. You can also self-serve and dynamically adjust Kubernetes-based compute clusters.

The Domino data science workbench is a governed environment that fosters collaboration and reusability. You can build and run models, publish your results, build and deploy your custom toolsets, and use tools like Shiny, Dash, Flask, and Docker images. You can work on your platform of choice, and you can build a data science workflow that is easy to scale and replicate across your team.

Domino’s Enterprise MLOps Platform is designed to automate elastic compute for data science workloads. It eliminates the learning curve and time spent guessing compute needs, and it allows IT to monitor and manage resource usage. It also serves as a front end to the cloud, providing a centralized infrastructure for your production models.