Many organizations believe that hiring a data scientist solves all their data science problems. However, customers also need a robust set of tools that:
- Enable data science teams to build and deliver models faster
- Use a single platform that automatically tracks the work of data science teams
- Enable collaboration and discussion
- Provide transparency on the work that is being delivered
Customers want to accelerate the time to value when they invest in a data science platform. The data science platform must provide an environment with enterprise-grade features that centralize the tools that are used across the model building and deployment life cycle.
Because data scientists have different requirements, it is important to choose a data science platform that enables data scientists to use their tools of choice. Other data scientists are looking for preconfigured stacks to help jumpstart model development.
- Domino Data Science Platform comes preconfigured with optimized computing stacks for research. They include languages, tools, and packages such as R, Python, Jupyter, RStudio, and TensorFlow. You can take advantage of new data science tools by building a custom environment with any packages with which you are familiar. Also, you can run code in a native development environment such as RStudio, SAS Studio, or JupyterLab.
- Domino Data Science Platform provides a single location for teammates to build, validate, deliver, and monitor models. The Reproducibility Engine automatically tracks all experiments so that data scientists never lose work and can reproduce results. Domino keeps code, environment details, data, discussions, parameters, and results for each experimental step.
- Data science is an experimental-based process. Domino Data Science Platform enables you to understand, build on, and contribute to the collective expertise of your organization. You can use existing code snippets, pipelines, or other artifacts to reduce redundant tasks such as manual data cleansing.
- A self-service model delivery framework reduces deployment friction. Domino Data Science Platform supports multiple modes of model delivery so that models fit seamlessly into existing workflows and systems to maximize impact. Stakeholders can receive scheduled reports, use self-service web forms, and engage with fully developed applications that are built in Shiny or Flask. IT engineers of downstream systems can call batch and real-time APIs that are automatically versioned, secured, and highly available.