Chip Huyen shares frameworks and case studies for implementing ML systems
Chip Huyen’s new interactive course shares frameworks, case studies and live coding/infrastructure examples to help your team avoid these pitfalls and successfully leverage ML.
Machine Learning (ML) has rapidly become an essential tool for businesses looking to leverage data and drive growth. However, effectively implementing and managing ML projects can be challenging, with many pitfalls to avoid. Chip Huyen’s new interactive course shares frameworks, case studies and live coding/infrastructure examples to help your team avoid these pitfalls and successfully leverage ML.
Chip has built ML systems at Nvidia, Netflix, and Snorkel AI, among others, and wrote the Amazon bestseller ‘Designing Machine Learning Systems’. She also teaches a course at Stanford on the same topic, honing her curriculum to suit cutting edge business needs.
You’ll be joined by a select group of engineers and scientists from leading organizations and enterprises. Together you will answer the following questions:
- What is the best way to plan and manage the different components of an ML system + its stakeholders?
- How do you design your supporting data systems,feature framework and ML Ops infrastructure to be reliable and flexible enough to iterate quickly and allow for experimentation?
- How do you identify the most impactful initiatives to improve your model metrics? Improve the model, improve the data quality or quantity, or improve the feature engineering?