domino data lab today announced Domino Code Assist (DCA) technology that makes it easier for data scientists and business analysts to collaborate and build machine learning workflows.
Domino’s eponymous platform is a machine learning operations (MLOps) technology that helps organize and track data science operations across the enterprise. DCA is low code tools Provides a visual interface that helps non-technical users in your organization generate sophistication python R code for common data science operations.
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According to the company, DCA will help democratize data science and analytics for chief data officers (CDOs) and business users in a number of ways. First and foremost, DCA helps solve the “cold start” problem, making resolution faster for non-technical business analysts. Second, the company claims DCA exposes low-code business analysts to well-written code. Domino also lowers the barrier to entry, helping to better coordinate all data science efforts within an organization.
Domino Code Assist bridges the data science gap to open source tools
“Code Assist enables data scientists and code-minded analysts to generate code that performs complex data manipulations and specify choices in a GUI that generates complex visualizations,” notes Domino Data. Lab co-founder and CEO Nick Elprin said. Today’s ITPro“Previously, you had to explicitly write this code yourself. This was a barrier that discouraged non-technical analysts from getting involved with these great open source tools.”
Elprin explained that Code Assist adds a GUI layer to several popular open source data science libraries. Libraries include Pandas and Vaex for data transformation, Plotly for data visualization, and Solara for building and deploying Jupyter apps.
Before DCA was released, he said, you couldn’t use these libraries meaningfully without code.
How to integrate Domino Code Assist with the Domino platform
According to Elprin, DCA is now built into Domino, so anyone using Jupyter notebooks or RStudio for data science operations in Domino can benefit.
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“This makes Domino worthwhile for many analysts and data scientists who are not used to writing complex code from scratch,” he says. “Not only can these people use Domino to develop and deploy ML models, but they can also perform ad-hoc analysis, descriptive statistics tasks, or scientific computing research such as simulations.”
Elprin pointed out that DCA has multiple integrations with existing Domino functionality. For example, DCA provides a GUI for browsing data registered in the Domino platform, making standard data sets and data sources easy to find and use. DCA also makes it easy to publish interactive analytical apps and dashboards using Domino’s existing app hosting capabilities, he added.
“DCA has significantly improved the skills of analysts for customers who do not currently consider themselves code-first,” said Elprin. “We all know that the most impactful data science done today is done in Python or R. DCA makes these superpowers much more accessible to enterprise data analysts. increase.”
Sean Michael Kerner is an IT consultant, tech enthusiast, and tinkerer. He consults industry and media organizations on technology issues.