50 points by machine_learner 7 months ago flag hide 12 comments
clouddad 7 months ago next
Some basic principles to start with: * Decouple model training and inference. * Use feature stores to manage features. * Monitor production systems and logs for issues.
tensorjockey 7 months ago next
Good start! I'd also like to add: * Automate model retraining and validation pipelines. * Have a system in place to detect data drift. * Implement continuous integration and delivery (CI/CD) in ML pipelines.
mlops 7 months ago next
Great points! Also to consider * Perform hyperparameter tuning for better model performance. * Use spot instances to save on infrastructure costs. * Implement DevOps best practices for ML projects e.g. infrastructure as code.
reinforcelearn 7 months ago next
Here is my recommendation: * Implement model explainability techniques for clearer insights about model behavior and decisions. * Make use of cloud-native ML platforms for easier deployment and scaling. * Regularly monitor and optimize resource utilization for better infrastructure costs.
hyperparam 7 months ago next
Here are some tips I would like to add: * Implement model interpretability for better model explainability. * Make use of GPU-based infrastructure for faster processing speeds. * Regularly review and optimize experiment tracking and workflow management.
deeplearner 7 months ago prev next
Agreed! And some more to the list: * Use multi-cloud or hybrid-cloud architectures to improve scalability. * Implement real-time analytics to monitor system performance and model accuracy. * Implement automation for failure detection, handling and scaling.
automl 7 months ago next
Some more useful tips: * Implement MLOps for automation, version control and collaboration. * Use differential privacy techniques for data privacy preservation. * Regularly perform model validation and retraining.
optimizer 7 months ago next
These are some tips I find useful: * Implement model compression techniques for better model inference. * Use model interpretability techniques for better understanding of model behavior. * Make use of transfer learning to train models on large-scale datasets efficiently.
alexnet 7 months ago prev next
A few more tips: * Use model versioning to keep track of model improvements. * Implement A/B testing to evaluate impact of changes. * Make use of containerization technology like Docker for consistent runtime environments.
mlfan 7 months ago next
I find these tips helpful: * Use parallel computing frameworks to speed up model training. * Make use of data lineage for debugging and auditing. * Use microservices-based architecture to improve system modularity and maintainability.
bigdatafan 7 months ago next
These are my suggestions: * Use data augmentation techniques to generate synthetic data. * Perform feature engineering for better model performance. * Make use of distributed computing for large-scale data processing tasks.
mlengineer 7 months ago next
Here are some tips I suggest: * Use explainable AI techniques to make model decisions more interpretable. * Implement data version control to keep track of data used for training and validation. * Make use of GPU-based platforms for faster model training and inference.