You can’t scale GenAI without open source technology

Much of our everyday toolset as data scientists is already open source. Python. R. Apache Spark. Not to mention incredibly powerful and niche packages like RDKit Cheminformatics and Harvard’s Geospatial Analytics extensions. Most truly interesting innovations made in the data science field are made broadly available through open source licenses. 

Open source tools are the only way to keep up with and take advantage of all future developments in GenAI.

Unlike closed source products, open source alternatives allow developers to solve their own problems by building on top of existing infrastructure – creating custom packages, algorithms, models, and more. 

Instead of relying on a few in-house developers interpreting the needs of their audience, open source projects use the power of that audience to build around all the edge case needs precisely by allowing people to solve their own problems. Besides, almost all proprietary technologies stand on the shoulders of the open source technologies – especially in the data science space.

With the emergence of GenAI, the pace of innovation is speeding up. Being able to quickly embrace and prototype use cases in this field will be crucial for securing competitive advantage. The opportunities to augment standard data science techniques with GenAI are huge and we’re just scratching the surface. Being able to try out different models quickly allows you to rapidly prototype and find areas where it can add the most business value with the least risk.

Proprietary software providers will always lag behind open source competitors – even with GenAI functionalities. We can see this in the way every proprietary data science player – new and old – embraces open source libraries across their offerings to stay relevant.

In the end, open source data science allows you to stay up with current tech trends — and with GenAI, even more so than before. Proprietary tools lag behind and lock up functionality unless your wallet is fat enough to unlock it. And even when you’ve paid for access, these products continue to be a black box where you cannot inspect any of a product’s source code or gain clarity over how your data is treated.

When companies develop proprietary data science products, they often have concerns about future-proofing. Companies and teams around the world invest time and money in building internal data science IP – whether those are data science workflows themselves, custom LLMs, or something else. You want to be sure that data science IP remains relevant and usable even if the current vendor goes out of business or just can’t keep up with needed innovations anymore. Proprietary tools can’t give you that guarantee.

That’s why it’s important to make sure your internal data science IP can also be used outside of a proprietary environment that – right now – may look like such a great fit. The alternative is to ensure your data science IP can continue to be executed within an open source environment. Because the truth is, there is lock-in, no matter what software you use. 

If you write lines of code, you are locking yourself into the programming language. If you create data flow diagrams, you are locking yourself into that particular way of modeling. And if you use a stack of tools, you are spreading that lock-in across different technologies. 

So what’s the way out of this maze of lock-ins?

When making a high stakes decision about building out your data science function, you want to make a decision you won’t regret in the next five years. With GenAI being adopted at pace, who knows where we will be in five years time. You want to choose an approach that will allow you to build data science workflows that do all your regular work, but also allow you to train, customize, and also safeguard GenAI models. 

So no matter what you do, bet on an established technology that has already shown it can stand the test of time. Most of the time, those are open source products where new innovations in the field remain accessible and older programs or processes can also still run, giving you the best of both worlds. 

And if there really is something missing for your team – like some connector to that weird legacy data warehouse that’s only running inside your firm – open source tools allow developers to easily add the connectivity either independently or with sponsorship. Or if your team is producing great new tools that you want to give everybody access to, there’s a way to add new libraries or modules to an open source environment, benefitting a whole community of other users. 

The only way to keep up with the pace of innovation is to take advantage of open source. You can either choose an open source product like KNIME from the get go and benefit from immediate access to the latest and greatest in GenAI functionality. Or you can choose closed source that will always lag behind and charge you an arm and a leg for the same functionality. The choice is clear – to stay ahead, choose open source.