Summary
In this chapter, we described what MLOps is and what it does in the ML lifecycle. We discussed the benefits MLOps brings to the table. We showed you how you can easily spin up a sophisticated MLOps system powered by SageMaker projects from the SageMaker Studio IDE. We deployed a model build/deploy/monitor template from SageMaker projects and experienced what everything as code really means.
We made a complete run of the CI/CD process to learn how things work in this MLOps system. We learned in great detail how an ML pipeline is implemented with SageMaker Pipelines and other SageMaker managed features. We also learned how the SageMaker model registry works to version control ML models.
Furthermore, we showed how to monitor the CI/CD process and approve deployments in CodePipeline, which gives you great control over the quality of the models and deployment. With the MLOps system, you can enjoy the benefits we discussed: faster time to market, productivity, repeatability...
