At Dassault Systèmes, we believe in the power of Virtual Twins to unlock the ability of ML to solve the most technical challenges. For decades, models have been at the core of our technologies. Moreover, Virtual Twins embed science-based behavioral models, a key differentiator to Digital Twins, allowing them to simulate the real world and generate realistic synthetic scientific data in the process.
In addition, there is a human element: domain experts can guide data scientists in understanding scientific data while building appropriate feature engineering and data scientist techniques. But what if domain experts could do the work themselves?
Developing programming skills is a critical bottleneck for domain experts trying to become data scientists. Domain experts are not all Python aficionados. This holds true across most, if not all domains. A possible way to work around that bottleneck is to bring the power of data science with low-code no-code solutions. Bonus point if it directly integrates to production environments, deploying to the “designers” of the corresponding domain (e.g. Designers, Lab Technicians, etc.) BIOVIA Pipeline Pilot, which has been validated for the past 20 years and documented to deliver value to the industry, provides a proven solution to the problem.
The Role of Low-Code No-Code Solutions in Data Science
To dig deeper into this, we asked the latest exciting product in AI for an opinion, ChatGPT, but thought it was important to bring additional context leveraging our experience from our flagship product.
Me: Can we turn domain experts into data scientists with low-code no-code solutions?
There is a growing trend in data science towards using low-code and no-code solutions to allow domain experts with little to no programming experience to analyze and visualize data. These solutions enable these experts to leverage their deep knowledge of their fields and apply it to data analysis and machine learning tasks, for requiring extensive coding skills.ChatGPT
At BIOVIA, we have enabled domain experts, one domain at a time for the past 20 years, so that they can author and share with their peer’s data-driven workflow leveraging ML model training and inference in production. We released our first ML learner components back in 2002! We started with Chemists with a lot of success and the adoption of Pipeline Pilot in the Biopharma industry. Then came the Biologists. Now, we are partnering with our other brands serving diverse domains to expand the list of parts we served at Dassault Systèmes with Clinical Trials (with Medidata), Mining (with GEOVIA) and beyond, so the value of low-code no-code can be accessed reliably in other industries.
One significant benefit of low-code and no-code solutions is that they can democratize data science by making it more accessible to a wider range of people. This is particularly useful for organizations that want to harness the power of data science but may not have a large team of highly skilled programmers. By empowering domain experts to work with data, organizations can tap into a wealth of knowledge and expertise that might otherwise go untapped.ChatGPT
This has been at the core of the success of Pipeline Pilot. We have provided the ability to develop best practices in the low-code no-code authoring IDE (still offering the ability to leverage libraries in R, Java, Python, etc. thanks to programming components) and publish them to non-technical users through a simple web interface (WebPort) accessible directly in the work environment (such as the lab).
Many low-code and no-code solutions are available today, ranging from drag-and-drop data visualization tools to fully-featured machine learning platforms. Some examples include Google’s Cloud AutoML, Microsoft’s Power BI, and Data Robot. These tools often have intuitive user interfaces and provide a variety of pre-built models and algorithms, making it easy for domain experts to get started with data science projects.ChatGPT
And of course, BIOVIA Pipeline Pilot! While we are working actively on bringing the power of Pipeline Pilot to the 3DEXPERIENCE platform on the cloud, its underlying infrastructure already makes it easy to deploy to cloud platforms today and manage, then democratize, end-to-end domain-centric data workflows.
Another advantage of low-code and no-code solutions is that they can speed up the development process by allowing non-programmers to quickly prototype and iterate on their ideas. This can be particularly useful in fast-paced environments where time to market is a key consideration.ChatGPT
Ask our champions! You can reach out to them on our 3DSwym community, where the Pipeline Pilot users exchange information, content and ideas access here.
Of course, low-code and no-code solutions are not a replacement for traditional programming skills, and it is still crucial for data scientists to have a strong foundation in computer science and mathematics. However, they can be a valuable addition to the toolkit of any data scientist. They can help to bridge the gap between domain experts and data scientists, enabling organizations to extract greater value from their data.Chat GPT
Absolutely – which is why in the past 2 years we have put significant effort into integrating key technologies used every day by data scientists. In addition to this, the Jupyter Notebook component allows you to create ML training workflows directly and integrate them seamlessly into your data pipelines.
If ChatGPT says it, it must be true!
At BIOVIA, we have the data to back it up. Over the past 20 years we have built technology and expertise in turning domain experts (such as computational chemists) into data scientists, enabling their ecosystem with data-driven ML-powered end-to-end solutions accessible from a web browser. Feel free to reach out if you want to unleash the power of ML with your domain experts in complex scientific and engineering data.