JetBrains bring in IDE to aid data scientists in developing AI models in Python

JetBrains DataSpell offers a better experience than the usual Jupyter notebooks that most data scientists use

JetBrains bring in IDE to aid data scientists in developing AI models in Python

JetBrains has announced that it is making its family of integrated development environments (IDEs) for programming languages available for data scientists using Python code to develop AI models. 

JetBrains DataSpell, which is now in an early access programme, promises a better experience than the usual Jupyter notebooks that most data scientists use to write and manage code, according to project manager Andrey Cheptsov. The goal is to raise data scientists’ overall productivity at a time when many more AI initiatives are being launched by enterprise IT departments to either cut costs or increase income as part of a digital business transformation initiative, according to Cheptsov.

According to Cheptsov, JetBrains DataSpell achieves this goal by making it easier to traverse data without coming in the way of writing code. He also mentioned that it allows data scientists to switch between Command and Editor mode for simpler cell and content customization. According to Cheptsov, JetBrains’ new IDE complements rather than replaces Jupyter notebooks. JetBrains DataSpell works with local Jupyter notebooks as well as remote Jupyter, JupyterHub, and JupyterLab servers, according to him.

Intelligent coding aid for Python, an out-of-the-box table of contents, folding tracebacks, and interactive tables are among the enhancements to the Jupyter notebook experience. Both Markdown and JavaScript formats are supported in cell outputs. JetBrains DataSpell includes Python scripts as well as other tools for altering and displaying static and interactive data. It makes it easy to adhere to best practises, according to Cheptsov. JetBrains DataSpell has minimal support for the R programming language in addition to Python, with support for other data science languages planned in the future.

Despite the enthusiasm for AI among many firms, some are becoming increasingly concerned about raising the productivity of data science teams. In a given year, many data science teams are only able to deploy a small number of AI models in production systems. The explanation for this goes beyond the data scientists’ tools, yet the less time spent navigating complex databases, the more time available to work on numerous projects. This is critical since some large corporations are currently attempting to deploy and manage hundreds of AI models that must be updated on a regular basis.

As companies of all sizes compete for data scientist expertise, the experience provided by tools may weigh in with other factors such as salary. Many data scientists nowadays don’t enjoy coding as much as the average application developer. Regardless of the tools used to create code, the need for more complex approaches to writing code is becoming obvious as data scientists work with not only one other, but also developers who are being asked to embed AI models into their apps. Most of those developers are used to working within the confines of an IDE, thus JetBrains DataSpell provides a familiar environment, according to Cheptsov.

Naturally, inertia is the most difficult obstacle to overcome when adopting any tool that involves behavioural change, an issue that is exacerbated by the fact that each data science team prefers to select its own tools and design its own methods. The problem that enterprises will soon face is creating a set of best practises for entire teams of data scientists in order to increase productivity without requiring each team member to use the same tool in the same way.