Domino is a platform built from the ground up to accelerate modern analytical workflows. In many cases, data analysis teams awkwardly graft tools on to their workflows or build their own custom toolset — or, lacking the resources for custom development, they simply tolerate inefficiency.
Domino Data Lab is a full-stack data science tool that accelerates the end to end process of analyzing, deploying and managing analytics pipelines and models. It integrates with popular open source and premium platforms, easily enables a single team to collaborate cohesively and to rapidly prototype and scale models to production.
Code + Data = Results – It’s important to track and link all components of your data science pipeline so that you can easily trace back from an output to the code and data that generated it. This requires centralized storage and code execution, as well as a robust set of features that enable sharing and collaboration across teams.
It also needs to be able to store all the outputs produced by your code and keep an ongoing record of your results and diagnostics. These can be accessed and shared from a unified web interface.
A central server should be able to enforce access controls, merge changes and detect conflicts in different collaborators’ work. This makes it easier for teams to collaborate and reduces the number of inefficient email attachments.
Having an easy to use version control system is also essential for a successful and efficient data science workflow as it facilitates continuous code and data testing. It also provides a unified source of truth for your code and data, reducing confusion.
This enables the Team to develop, design and deploy solutions more efficiently than ever before. It also gives the Team the ability to collaborate in real time and share models, code and results as a single repository for further review, debugging and documentation.
One of the best things about Domino is that it helps the Team keep track of code changes and identifies when code or model apis have been updated. This allows them to quickly and efficiently re-run model tests to ensure that everything is working as intended.
Its ability to connect to Bitbucket to track and manage code changes makes it a very powerful and seamless integration for a Data Science team. It also enables them to spin up interactive workspaces of different sizes for exploring data and running jobs.
As the Team was developing a predictive modeling model, they needed to have a way to share their model with multiple users as well as the ability to easily execute their models and test them in real time. The Domino Data Lab platform allowed us to seamlessly and efficiently build our model using the MLlib, then run it on a shared workspace. This streamlined our data science workflow and helped us deliver the product faster than ever before.
Another key feature of Domino that allowed our Team to collaborate and build the product faster was its ability to serve our model apis from a single web portal. This streamlined our entire data science workflow and made it much easier for the Team to get models to production.