Machine Learning (ML) in a production environment: Tips and Tricks

By Janani Ravi

Machine learning (ML) and deep learning are more popular than ever and are going to be a permanent part of the future of technology. However, an increasing number of organisations are running up against new challenges in ML logic when an application is deployed to production. For example, one challenge is the risk of overfitting, which occurs when a model learns the training data too well, and is not able to generalise the data. But, overfitting is only one important risk that will be covered in this event.  

Join Simmi Dhamija from Tech Mahindra as she shares insights into her organisation’s digital transformation. Then, Janani Ravi will address ML challenges head on and dive into: 

  • Machine learning concepts

  • Challenges to be aware of when working with deep learning models

  • Overfitting and how to mitigate risk

About the author

Janani Ravi has a master's degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York and Singapore at companies such as Microsoft, Google and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

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