MLOps


How to deploy machine learning projects to Kubernetes?

bodywork

Functions and examples

Benefits

  • Bodywork brings DevOps to your machine learning projects and will form the basis of your Machine Learning Operations (MLOps) platform.
  • It will ensure that your projects are always trained with the latest data, the most recent models are always deployed and your machine learning systems remain highly-available.

Target users

  • Bodywork is aimed at teams who want to deploy machine learning projects in containers.
  • It will deliver your project’s Python modules directly from your Git repository into Docker containers and manage their deployment to a Kubernetes cluster.

bodywork
bodywork

How to use bodywork?

  • A GiHub account
    • support for GitLab, BitBucket and Azure DevOps will come later in 2021
  • Access to Kubernetes cluster
  • Divide your project into discrete stages and create an executable Python module for each one.
  • Bundle these files together with a bodywork.yaml configuration file, into a Git repository and you’re ready to go.
    stages

How to troubleshoot a serverless application?

Grafana

  • Amazon Managed Service for Grafana (AMG), is a fully managed service that makes visualizing and analyzing operational data at scale easier.
  • Many customers choose AMG because of an existing investment in Grafana, its deep integration with vendors they might already be using, consolidation of metrics across environments, and powerful visualizations for both in-cloud and on-premises workloads.
  • Amazon Managed Service for Grafana is a powerful tool for analyzing your serverless application’s metrics and logs. Grafana
    Grafana

Ref