Rashmi Nagpal
Rashmi is a Software Engineer at Cactus with a passion for building products in AI/ML. In her almost 4 years career in tech, she’s brought products to life at pre-seed startups, scaled teams and software at hypergrowth unicorns, and shipped redesigns and features used by millions at established giants. When she's not coding, capturing cosmos using her telescope, or playing board games with friends, you can find Rashmi playing with fluffy - her maltese breed pet dog!
Session
There has been a renaissance around Artificial Intelligence systems in recent years. However, despite the hype, only a small percentage, i.e. 13% of Machine Learning models see the light of day!
Well, effectively building and deploying machine learning models is more of an art than science! ML models are indeed inherently complex, have fuzzy boundaries, and rely heavily on data distribution. But what if they are trained on biased data? Then they’ll generate highly biased decisions! As the famous saying goes by, “Garbage in, garbage out,” so if the model is trained on skewed and unfair data distribution, they are bound to produce fuzzy output!
So, join me in this talk as I will share my learnings in developing effective practices to build and deploy ethical, fair and unbiased machine learning models into production.