Krishi Sharma
Krishi Sharma is a software developer at KUNGFU.AI where she builds software applications that power machine learning models and deliver data for a broad range of services. As a former data scientist and machine learning engineer, she is passionate about building tools that ease the infrastructure dependencies and reduce potential technical debt around handling data. She helped build and maintains an internal Python tool, Potluck, which allows machine learning engineers the ability to bootstrap a containerized, production ready application with data pipelining templates so that her team can focus on the data and metrics without squandering too much time finagling with deployment and software.
Sessions
In the world of machine learning, more data and diverse data sets usually leads to better training, particularly with human centered products such as self-driving cars, IOT devices and medical applications. However, privacy and ethical concerns can make it difficult to effectively leverage many different datasets, particularly in medical and legal services. How can a data scientist or machine learning engineer leverage multiple data sources to train a model without centralizing the data in one place? How can one benefit from multiple datasets without the hassle of breaching data privacy and security?
When it comes to data driven projects, verifying and trusting experiment results is a particularly grueling challenge. This talk will explore both how we can use Python to instill confidence in performance metrics for data science experiments and the best way to keep experiments versioned to increase transparency and accessibility across the team. The tactics demonstrated will help data scientists and machine learning engineers save precious development time and increase transparency by incorporating metric tracking early on.