Learn how to set up Machine Learning projects like a pro. This includes an understanding of the ML lifecycle, an acute mind of the feasibility and impact, an awareness of the project archetypes, and an obsession with metrics and baselines.
MLOps Infra & toolingthe ML code portion in a real-world ML system is a lot smaller than the infrastructure needed for its support
Troubleshooting Deep Neural NetworksA common sentiment among practitioners is that they spend 80–90% of time debugging and tuning the models and only 10–20% of time deriving math equations and implementing things.
Data ManagementTesting & Explainability- Understand at a deeper level how well your model is performing.
- Become more confident in your model’s ability to perform well in production.
- Understand the model’s performance envelope (where you should expect it to perform well and where not).
most data scientists and ML engineers do not know how to build production ML systems.
ML Teams and StartupsDeep-Learning-in-Production
ahkarami • Updated Oct 7, 2023
