The basis for the port, the only one between Ostia and Antium, was evidently the mouth of the Numicus river. Lavinium was on a hill at the southernmost edge of the Silva Laurentina, a dense laurel forest, and the northernmost edge of the Pontine Marshes, a vast malarial tract of wetlands. The coastline then, as now, was a long strip of beach. Lavinium was a port city of Latium, 6 km (3.7 mi) to the south of Rome, midway between the Tiber river at Ostia and Antium.
The comune of Pomezia and the museum are directly behind the photographer. The archaeological excavations are in a field off to the left of the photograph. On the left is the Castello Borghese, possibly the site of the Roman arx or citadel. The structures in the photograph vary in date. The issue organizations will soon find themselves struggling with is defining a set of best practices for entire teams of data scientists to improve productivity without requiring every member of those teams to use the same tool in precisely the same way.Gate into the interior of the settlement of the frazione of Pratica di Mare, a medieval walled village at the site of the center of ancient Lavinium. Of course, inertia is the biggest challenge when introducing any tool that requires behavioral change, a problem that is compounded because each data science team tends to select its own tools and define its own processes. Most of those developers routinely work within the constructs of an IDE, so JetBrains DataSpell creates an environment they will readily understand, Cheptsov said.
Jetbrains dataspell code#
Regardless of the tools employed to write code, the need for more sophisticated approaches to writing code is becoming apparent as data scientists find themselves collaborating with not only each other but also developers who are being asked to embed AI models into their applications. Many data scientists today don’t enjoy writing code as much as the average application developer might. Supply and demandĪs organizations of all sizes fight to attract and retain data scientist talent, the experience provided by tools could factor in alongside considerations such as salary.
That’s critical because some large enterprises are already trying to roll out and maintain hundreds of AI models that need to be continuously updated. The reason for that goes well beyond the tools employed by data scientists, but the less time spent navigating complex datasets the more time there should be to work on multiple projects. Many data science teams are only able to successfully deploy a small number of AI models in production environments in a year. In addition to Python, JetBrains DataSpell includes basic support for the R programing language, with support for other data science languages planned.ĭespite many organizations’ enthusiasm for AI, some are increasingly concerned about improving data science teams’ productivity. “It makes it easier to follow best practices,” Cheptsov said. JetBrains DataSpell supports Python scripts alongside additional tools for manipulating and visualizing both static and interactive data. Cell outputs support both Markdown and JavaScript formats. JetBrains DataSpell is compatible with Jupyter notebooks running on local machines, as well as remote Jupyter, JupyterHub, and JupyterLab servers, he added.Įnhancements to the Jupyter notebook experience include intelligent coding assistance for Python, an out-of-the-box table of contents, folding tracebacks, and interactive tables. JetBrains’ new IDE doesn’t replace Jupyter notebooks as much as it augments them, Cheptsov said. Join today’s leading executives at the Low-Code/No-Code Summit virtually on November 9.