The world of data science awaits more adaptation to open-source Python Programming

Data science is a quickly growing technology as organizations of all sizes embrace artificial intelligence (AI) and machine learning (ML). Along with that growth has come no shortage of concerns. The biggest barrier to successful enterprise adoption of data science is insufficient investment in data engineering and tooling to enable the production of good models," Peter Wang, Anaconda CEO and cofounder told VentureBeat. "We've always known that data science and machine learning can suffer from poor models and inputs, but it was interesting to see our respondents rank this even higher than the talent/headcount gap."

Open-source tools offer a lower barrier to entry than licensed software. Companies can experiment more efficiently and with fewer constraints. They are also more likely to find talent for programming languages and data science tools that are freely available to everyone. A case in point is Python, the dominant programming language for data science, which happens to be open source. It has the most versatile and extensive capabilities for manipulating data and building machine learning models. Python has even superseded commercial tools like MatLab in terms of capabilities for data science applications.

Most data science and machine learning frameworks such as TensorFlow, SciKit-Learn, or PyTorch build directly on Python and are also open-source. Often, their creators are large companies already dominant in their respective markets. Evidently, the benefits of making a library like TensorFlow open-source outweigh the costs for its creator Google.

While Google gave potential competitors a powerful deep learning tool, it probably benefits more from the massively expanded talent pool, the sprawling deep learning innovation, and the widespread adoption of the framework by other companies that open-sourcing TensorFlow entailed. Other machine learning libraries, such as XGBoost, originated as research projects in universities. For these institutions, the benefits of open source software are overwhelming for the reasons discussed above.

Most machine learning models require large amounts of data to train. Modern machine learning models, especially deep neural networks used in computer vision and natural language processing, require vast amounts of computational resources to train. This would present an almost insurmountable challenge for smaller organizations and individuals, who simply do not have this amount of data internally, nor the budget to run expensive model training experiments. If it weren't for open-source data, machine learning would be almost exclusively the domain of large corporations. This may be in the interest of the shareholders of said corporations, but certainly not of society at large, which benefits from the innovations produced by startups and individuals.