You're navigating data transparency and security in ML. How do you find the right balance?
In the evolving field of Machine Learning (ML), balancing data transparency and security is a complex challenge. You need to ensure that your data is accessible enough to be useful for ML purposes while protecting it against unauthorized access and breaches. This delicate balance requires a nuanced approach, considering both the potential of ML to unlock insights and the risks associated with data exposure. To navigate these waters successfully, understanding the key aspects of data handling in ML is crucial, from ethical considerations to technical safeguards.
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Vaibhava Lakshmi RavideshikAmbassador @ DeepLearning.AI and @ Women in Data Science Worldwide
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Saurabh KumarEngineering @Adora | Prev. Rapyuta(ML), Yahoo(ML), Nokia | IIT Delhi
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RADHA KRISHNAN S?? Data Science Leader | Certified Data Scientist | Machine Learning | Deep Learning | AI | Azure Open AI | MS…