1 point by ml_engineer 1 year ago flag hide 19 comments
user1 1 year ago next
I've found using containerization (Docker) and automated pipeline tools (Jenkins) to be really effective. This way, you can consistently build and deploy models with proper versioning.
user2 1 year ago next
I agree, user1! Using tools like Docker and Jenkins definitely improve the CI/CD process for ML models. I would also add version control systems like Git to track changes made to the code.
user4 1 year ago prev next
Great suggestions, everyone! Another best practice I've found is to include A/B testing to compare new models with existing ones to minimize impact on business KPIs.
user6 1 year ago next
A/B testing and model explainability are excellent ideas! I'd suggest taking that a step further and incorporating automated retraining pipelines that can evaluate and select the best model.
user3 1 year ago prev next
In my experience, monitoring and logging systems such as Prometheus and Grafana are quite helpful in watching ML model performance and catching any potential issues in production in real-time.
user5 1 year ago next
Monitoring tools are essential indeed! I'd also include model explainability tools like LIME or SHAP to help monitor the performance of individual predictions, and to debug any issues.
user7 1 year ago prev next
Don't forget testing and validating the data that your model receives in production! It shouldn't be a surprise when data distributions change significantly.
user8 1 year ago next
Absolutely! Using a data validation library like Great Expectations is a must for validating data both before training and in production.
user9 1 year ago prev next
Security is important too! Implement authentication and access controls to protect models and data. I use Keycloak as an IDP for manage user access.
user11 1 year ago next
Securing models and data should always be a high priority, user9! I also use JWT tokens for securing requests and responses.
user10 1 year ago prev next
I think it's essential to build a maintainable architecture. Adopt microservices so that each microservice exposes an API, and can scale independently according to the required resources.
user14 1 year ago next
Modularizing ML models can help build a more scalable solution. I recommend building models as separate components to enable smooth integration and scaling.
user15 1 year ago next
When scaling, remember to also pay attention to allocation of computing resources. Use Kubernetes or Amazon ECS to efficiently allocate and scale resources in real-time.
user17 1 year ago next
As user15 suggested, Kubernetes can help with scaling, but make sure you include proper resource requests and limits to avoid unneeded strain on your infrastructure.
user19 1 year ago next
Using Redis as a caching layer and K8s is a powerful combo, user17! Just ensure you use proper eviction policies to avoid cache contention.
user12 1 year ago prev next
Continuous monitoring and alerting are significant when it comes to detecting performance issues or model degradation. Tools like Nagios or Sentry could be useful.
user13 1 year ago next
You're right, user12! Alerting is critical to spot any regression ahead of time. We use both Sentry and PagerDuty to ensure quick issue response times.
user16 1 year ago prev next
Consider reproducibility and validation throughout the ML development lifecycle. This means not just testing the model's performance but also evaluating data preprocessing methods.
user18 1 year ago next
To avoid bottlenecks and improve performance, ensure caching is implemented throughout your infrastructure. Tools like Redis and Memcached can accelerate model queries.