Ponicode For Data Science: Establishing Code Quality

When it comes to code quality, the machine learning industry is considered to still be at a very early stage. Today we provide Ponicode for Data Science to enable data scientists to stay ahead of the curve and to be pioneers of code quality in machine learning.

We are proud to introduce you to a new solution designed for data scientists, aimed at raising awareness on code quality best practices and helping them to produce healthy code: Ponicode for Data Science. 

Code quality has everything to do with Data Science. 

At Ponicode, we believe that there is a global shortage of resources and guidelines when it comes to code quality in the field of data science. Most data scientists usually have a background in mathematics and research and not necessarily in software engineering, but they increasingly have to take over machine learning engineering tasks. Moreover, as the machine learning (ML) industry is gaining maturity the pain points of quality code (maintainability, bug fixing resources and costs) are replicated in data projects. Appropriate tools and code quality guidelines therefore need to be promoted among data science teams as soon as possible. 

This is why Ponicode has decided to tackle the global issue of code quality in data science and the lack of knowledge and best practices around it.

The first users of Ponicode for Data Science are our own data experts! 

At Ponicode we have data scientists who are in charge of implementing their models. Our commitment for code quality means providing them with a tool enabling them to unit test their code while taking into account their knowledge basis. 

For the past few months, as we were building this offer, we met companies who tend to rely more and more on profiles very similar to Ponicode's data scientists to perform both research and implementation. In this new paradigm, helping them to develop healthy, robust and maintainable code has become a priority. The machine learning industry is evolving and the shift left approach is starting to make its way to the data scientists, especially innovation seekers who are on the front row of product development. Today we are extending our commitment to data science through our Python support. Ponicode can now help data scientists with early testing and, in particular, unit testing, in order to develop high performing and production-ready machine learning pipelines and allow data teams to reach their full potential. 

A data science-ready solution

This new solution has two goals: helping data scientists to get familiar with code quality concepts and guidelines, supporting them to be able to easily adopt these guidelines, and all while reducing the time spent on testing.

Ponicode for Data Science is essentially Ponicode's capacity to support Python and to offer data teams a visual, low-code interface in which they can create exhaustive unit test files, in just a few clicks. This low code approach enables data scientists to clearly see the list of suggested tests in one table : we call it the table-driven test approach. They can identify bugs and regressions immediately and increase their capacity to categorise flaws due to inconsistent pre-processing or broken model evaluation code from the model training related flaws. Ponicode helps data scientists make better decisions to evaluate, fix and improve their pipelines.

Ponicode brings more confidence to data teams

This new aspect of Ponicode provides data scientists with a tool that enables them to quickly perform unit tests without any prior training or knowledge and to do it fast. Data science teams and data projects’ stakeholders can gain a lot of confidence in what they deliver thanks to thorough unit testing. Moreover, CTOs can monitor and gain the assurance that the code developed both by software engineers and data scientists are reaching all code quality targets. High performing softwares, happy users and happy teams is what we believe in and the Ponicode for Data science release adds to the range of solutions we offer today in order to reach that target.

What’s next?

Go and try our tool for Python. Our public beta needs your feedback to drive our roadmap. 

Get started and reach out on our Slack to share your first impression with our team.

Do you want to investigate how to deploy our solution to your whole tech team? Find out more about Ponicode Enterprise 👨‍💻