Continuous Delivery of Machine Learning Systems

Unal Patel

March 24, 2023

Machine learning systems require a different workflow to reach production as they grow. This involves a lot of attention from engineers responsible for integrating, testing, and deploying these systems.This can become a significant pain point for organizations that adopt machine learning systems. Using Continuous Delivery, an end-to-end pipeline can close the feedback loop at every stage and keep models performing well.

Automated CI/CD Pipeline

Automated CI/CD pipelines streamline software delivery by continuously building, testing, and deploying new versions of your software. This streamlined process eliminates errors from manual processes, provides standardized feedback loops, and allows you to quickly and efficiently deliver your software at scale.

To simplify the CI/CD pipeline setup, AWS Cloud offers managed CI/CD workflow tools like AWS CodePipeline and AWS Step Functions. These tools allow your team to manage their CI/CD pipelines and automate builds easily. They also make it easier to reuse CI/CD pipelines.

An automated CI/CD Pipeline helps your team quickly try new ML algorithms and models. You can then implement these ideas in the pipeline and deploy them to a testing or production environment. This ensures that your ML pipeline is constantly evolving and improving.

Automated Deployment

Automated deployment is a crucial component of automating your CI/CD pipeline. It helps to reduce lead time and eliminate manual deployment tasks that can be a pain for your team.

A good deployment automation tool should make code between development and production environments reproducible. It should also be able to isolate and decouple the execution environment for custom code runtime, allowing developers to share their work across different pipelines.

ML systems often have complex and non-deterministic outputs, which can be challenging to deploy into production. To avoid this, a reliable and repeatable software release process is essential.

To achieve this, ML pipelines are automated to deliver online predictions for new models trained on new data. This can be done in various ways, including:


Automated Testing

Automation testing is a technique that uses test scripts to verify the functionality of software systems. It allows testers to perform repetitive tests and execute them many times without manual intervention.

This process can save developers and their teams considerable time in identifying bugs early, leading to faster product release times and lower costs.

However, introducing automation testing in an organization should not be taken lightly. Companies must understand that it takes time and resources to implement it.

The first step in the process is to select the correct test tool for the application under test (AUT). A detailed assessment of the AUT should be done before choosing any agency.

Automated Monitoring

As machine learning models become more and more critical to business operations, they must perform as expected in production. If not, models could be left in production without supervision, or businesses may own models that no longer deliver business value.

As a result, ML practitioners need a robust monitoring system to ensure the systems they build keep performing as expected in production. The system should monitor data and model performance, triggering alerts for both dramatic and slow-leak regression in computational performance.

Automated monitoring enables IT teams to identify issues faster and more efficiently, reducing time-to-resolution and increasing agility. Unlike manual, non-automated monitoring, automatic monitoring offers IT groups complete visibility into all their systems and can help them detect and respond to any potential problems sooner.

OMi supports cascading default values and conditional parameter values, which allow using the same parameters with several policy templates to create hardware- or platform-independent monitoring solutions. In addition, it enables application experts to define auto assignments for management templates and aspects.