DSAIEngineering is designed to be an engineering-first training platform and community for data and AI practitioners.
The emphasis is on developing practical knowledge across software engineering, data science, AI theory, ML/AI engineering, and business applications. In other words, the focus is on learning how to build realistic data and AI systems that create business value and generate revenue.
I have started by developing realistic examples that demonstrate how tabular foundation models can be used in practical workflows, either as companions to or replacements for classical tabular machine learning models.
My long-term aim is to develop realistic blueprints that can eventually be deployed to production.
For now, I am working mostly in Jupyter notebooks because platforms such as Kaggle provide free GPU access and abstract away many engineering challenges. This is useful at the current stage because I am focusing on strengthening my understanding of data science, AI theory, and business use cases.
Gradually, I want to incorporate more sophisticated software engineering and ML/AI engineering practices so that the code becomes more robust, reliable, and production-ready.
In the meantime, if you are a data and AI practitioner who resonates with this vision, I invite you to join the journey by subscribing to the newsletter and connecting with me on LinkedIn.
If you have already been following along and have feedback on how I can make the content more useful to you, or if you would like to collaborate, please let me know.
Mohit Saharan