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Love this course!

Just one thing, would the links in read me be cleaned up?

have a nice day!

After going to the site, the site is not getting open and after some seconds it shows's site taking too long to respond, and sometimes 182.79.218.34 refused to connect.

If we decide to do another version of the course, here are some new topics that could be exciting to add. This is off the top of my head, feel free to suggest other topics.

Bias / fairness

• Detecting and reducing bias in ML systems
• Ethics for ML practitioners

Deployment

• More complicated web serving scenarios (ensembles, graphs of models, low-latency, larger models)
• More prescriptive recommendations on deployment (how to do AB tests, shadow mode, instant rollbacks, etc)
• Model optimization (quantization, distillation, compression, etc)
• Edge / mobile deployment
• On-prem or data-sensitive deployment

Troubleshooting

• More specific pytorch recommendations

Testing

• More specific testing recommendations -- "test coverage" for ML, what to do when tests fail etc
• More on data slices, how to pick them, and how to manage them
• Testing suggestions for language data

Monitoring

• More on what to monitor
• How to set up a monitoring system

Data

• Managing data at a larger scale
• Managing user data for ML

Infrastructure / tooling

• Feature stores -- why, when, and how
• Logging infrastructure for ML
• Spark -- why, when, and how
• Tools for building reproducible data pipelines (Airflow, Kubeflow, etc)

Model lifecycle management

• How to know when to retrain models
• How to set up reproducible retraining pipelines
• How to select data for your next training run (active learning & friends)

Thanks a lot for creating this awesome course.

Are contributions to the pages in the course welcomed(for example: adding more text content to a page)? I ask because I see that a lot of valuable insights from the video are not captured in the corresponding summary text.