Скачать или смотреть CI/CD & Continuous Training in ML // Part 4 // MLOps Coffee Sessions #17

MLOps level 2: CI/CD pipeline automation
For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.

Figure 4. CI/CD and automated ML pipeline.

This MLOps setup includes the following components:

Source control
Test and build services
Deployment services
Model registry
Feature store
ML metadata store
ML pipeline orchestrator

Characteristics of stages discussion.

Figure 5. Stages of the CI/CD automated ML pipeline.

The pipeline consists of the following stages:

Development and experimentation: You iteratively try out new ML algorithms and new modelling where the experiment steps are orchestrated. The output of this stage is the source code of the ML pipeline steps that are then pushed to a source repository.

Pipeline continuous integration: You build source code and run various tests. The outputs of this stage are pipeline components (packages, executables, and artefacts) to be deployed in a later stage.

Pipeline continuous delivery: You deploy the artefacts produced by the CI stage to the target environment. The output of this stage is a deployed pipeline with the new implementation of the model.

Automated triggering: The pipeline is automatically executed in production based on a schedule or in response to a trigger. The output of this stage is a trained model that is pushed to the model registry.

Model continuous delivery: You serve the trained model as a prediction service for the predictions. The output of this stage is a deployed model prediction service.

Monitoring: You collect statistics on the model performance based on live data. The output of this stage is a trigger to execute the pipeline or to execute a new experiment cycle.

The data analysis step is still a manual process for data scientists before the pipeline starts a new iteration of the experiment. The model analysis step is also a manual process.

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Timestamps:
0:00 - Recap of Part 3
1:38 - Intro Skit
2:34 - MLOps Level 2
5:25 - MLOps level 0: Manual process review
5:48 - Discussion on the schematic representation of an automated ML pipeline for CT diagram
7:49 - Added component - Packages
9:37 - Source control
10:26 - Text and Build Services
14:07 - Deployment Service
18:10 - Feature Store
26:15 - ML Metadata Store
26:27 - So when people talk about explainability, would that be part of the Metadata Store?
27:48 - So the idea here, like a big picture is zooming out, is that I push a model in the production and like, forget about it?
28:00 - Because everything is automated so if the model starts not to do what it says it does, does it automatically get retrained?
34:00 - Stages of the ML CI/CD automation pipeline diagram
39:00 - Reproducibility aspect of the pipeline
41:10 - Prediction Service
43:37 - Highlight of Maturity Level of CI/CD
43:56 - Recap of CI Maturity
44:46 - Unit testing for future Engineering Logic
49:24 - Recap of CD Maturity
53:10 - Deployments
54:32 - Recap of today's session
59:19 - Closing/Summary