Across the globe, there’s a lot of demand for data mesh, data platforms and modernising data ecosystems.
The most important thing for aspirants is to get the fundamentals right before diving into data science and AI, feels Amit
AI is definitely going to enrich our experience, how we interact or how we do our day-to-day work, or be how do we interact with other human or objects.
My vision is that AI/ML models create solutions that humans can utilise in various tasks, but not replace humans in any manner
The accepted best practices for formulating and validating assumptions that we would hear from experts were not written down anywhere. So, my collaborator Emre Kiciman and I thought: Why not create a library for Causal Inference that enabled these best practices for everyone?
As one of the leading brands in mobility, we see our roles as an enabler in moving the industry forward and future-ready through such partnerships in the innovation ecosystem.
Once you make a decision on how machines should resolve moral tradeoffs, the question is how to actually implement it?
Financial crime is the most challenging and fascinating domain I’ve been through, and it provides a huge potential for exploration for data scientists
One just needs enough information to make a model work and not drown in every detail, thinking that at some point it will be useful.
An effective way to build amazing portfolios is to learn data science by building real-world projects, says Ankit
Publishing a paper is just one way to contribute to the community.
India’s infrastructure to support EV manufacturing still needs massive improvements.
