Domino Data Science Workbench – Deeper Insights For Teams, More Productive Workforce, and Higher Returns on Investment
Using Domino’s data science workbench, data scientists can run hundreds of machine learning experiments in parallel. This results in deeper insights for teams, a more productive workforce, and higher returns on investment. The platform is built for modern data analysis workflows, and provides elastically scaled compute, environment management, publishing, and tools to deploy models.
Domino’s data science workbench is built for collaboration, reusability, and reproducibility. This includes flexible, governed compute, tools to run hundreds of machine learning experiments, and tools to publish results. The workbench includes tools to deploy models, provide access control, and manage conflicts. These features accelerate production model management processes. And because Domino is integrated into your environment, you can access distributed frameworks and build lightweight self-service web forms.
Domino’s Enterprise MLOps Platform serves as a front end to the cloud. It automates elastic compute designed for data science workloads, and eliminates DevOps learning curves. It allows you to spread jobs across machines, schedule automatic recurring jobs, and monitor resource usage. You can also self-serve and adjust Kubernetes-based compute clusters as needed.
Domino’s data science workbench helps data scientists collaborate by delivering an environment governed by rules. This fosters reusability and reproducibility, and makes work more easily shared. It also provides data scientists with powerful tools, and provides easy comparison of results. This results in faster progress for individuals, teams, and companies.
Domino is a first-class platform for standardizing open-source data science development. The software is designed to address key gaps in data analysis workflows. It helps data analysis teams build a custom tool set, and gives non-technical users the ability to interact with models. It also provides tools to publish models, including REST API endpoints. You can also easily export models as Docker images or CI/CD pipelines.
Domino’s Enterprise MLOps platform makes it easy to scale, run automatic recurring jobs, and monitor resource usage. It also provides a consistent environment for your team. This means you can share models with others, and easily access distributed frameworks. The platform provides a high-availability environment, and allows you to experiment 10x faster. You can run hundreds of machine learning experiments in parallel, and publish results for easy comparison. You can also run models on your own NVIDIA GPUs. It supports many languages, and provides access to a broad range of skills. It is also portable, and can be deployed to multiple infrastructures.
Domino’s data science workbench also makes it easy to collaborate with other teams. You can create apps using Flask, Dash, or Shiny. This allows your team members to easily share their models with each other, and work on their preferred platform. In addition, it gives non-technical users the ability interact with models, including building lightweight self-service web forms. It also provides tools to deploy models, including REST API endpoints.
Domino’s Enterprise MLOps software also provides a centralized, cloud-based data science workbench. This enables data scientists to use tools in an elastically scaled compute environment that provides flexible tools to run hundreds of machine learning experiments in parallel.