Definition
A CI/CD pipeline helps ML teams achieve rapid and reliable updates of models in production, enabling the building of robust, bug-free AI/ML applications more quickly and efficiently.
Overview
As MLOps continues to evolve, it is becoming increasingly common for DevOps methodologies to used for ML/AI applications. One of the core concepts of DevOps that is helping to define MLOps is continuous integration and continuous delivery (CI/CD).
As an essential DevOps practice, CI/CD embraces tools and methods for the continuous, reliable delivery of software applications by streamlining the building, testing, and deployment of applications to production.
- Continuous integration (CI): The practice of automating the integration of code changes from multiple contributors to a software application or ML project.
- Continuous delivery (CD): The practice of delivering every build into a production-like environment for integration and testing prior to deployment.
- Continuous deployment: An additional step which automates the configuration and deployment of an application into production.
For rapid and reliable updates of models in production, ML teams need a powerful and automated CI/CD system. This enables data teams to rapidly explore new ideas around feature engineering and model architectures and implement them more quickly. CI/CD also enables teams to build, test, and deploy new pipeline components to their target environment.
A strong CI/CD pipeline enables ML teams to build robust, bug-free models more quickly and efficiently. This reliable and sustainable solution can be crucial to efficiently scaling ML models.
Why CI/CD for machine learning is important
Machine learning model development can be a time consuming process, with many manual steps that take time to complete and leave significant room for human error. This is a major challenge as it is critically important to move quickly and accurately when developing models to avoid problems such as training-serving skews and model bias.
Conversely, an efficient CI/CD pipeline enables ML teams to streamline testing and deployment through automation, saving time while delivering a higher quality product.
Definition: MLOps is a core function of machine learning engineering that is focused on streamlining the process of deploying ML models into production. It is a collaborative movement that involves everyone from across the machine learning supply chain to achieve this goal, as explained in our Ultimate Guide to MLOps Tools in 2024.
Key elements of a CI/CD pipeline
A CI/CD pipeline is an automated system that streamlines model development and deployment. They can be used to build code, run tests, and deploy new versions of a model when changes are made. The testing portion of the CI/CD process is concentrated more in CI, whereas the deployment portion is covered in the CD part of the process, as the pipeline is automatically deploying or delivering the model into production.
Automated CI/CD pipelines eliminate errors, provide standardized feedback loops, and allow for quicker model updates. Taken together, it is a major improvement to previous manual model development lifecycles.
A typical CI/CD pipeline will be made up of four key stages:
- Source
- Build
- Test
- Deployment
Let’s take a closer look at each of these stages:
1. Source stage
The typical CI/CD pipeline in MLOps starts with a source, such as a model code repository. When changes are made to a model’s code, this triggers the CI/CD system to kickstart the pipeline process. It’s also possible to set up pipelines that are automatically triggered by scheduled workflows, on command, or by other pipelines.
2. Build stage
To build a model that can potentially be deployed and used by end-users, code must be combined with its dependencies. This stage can vary depending on the language of the program. For example, Python programs don’t need to be compiled whereas Java or Go applications must. In the case of MLOps, model training can also considered as part of the build stage.
3. Test stage
The test stage is arguably the most important because it is the stage that validates the code’s correctness and the performance with the model. Testing can last anywhere from minutes to hours depending on the model size and complexity. For larger projects, tests tend to be carried out in multiple stages.
4. Deployment stage
Deployment is the final stage. A model can only be ready for deployment when there is a running instance of the code that has successfully gone through the testing stage. Most projects will have more than one deployment environment, such as one for staging and one for production. The former is used to ensure that the model is deployed correctly whereas the latter is the final end-user environment where the ‘live’ model operates.
Monitoring is another important stage that comes after deployment. During monitoring, data is collected on model performance based on live data, and the output of this stage is a trigger to execute the pipeline or to execute a new testing cycle.
Challenges in CI/CD for machine learning
Although a machine learning system is in essence a software system, CI/CD for machine learning presents a series of key challenges when compared with “traditional” software.
First of all, since ML is experimental in nature, the model development process involves running ML experiments to determine modeling techniques and configuration parameters that work best for a defined problem.
The challenge here is tracking the reproducibility of these experiments to make sure that they’re able to re-use the code and replicate the model’s performance on the same dataset.
The testing stage also presents more potential areas of complexity when it’s an ML system that is subject to the tests rather than a regular software system. This is because ML development involves data and models in addition to the source code, meaning that teams must test and validate both data and models to ensure that the ML system performs effectively.
Finally, deploying a machine learning system is not just about deploying a model that has been trained offline; it also requires the deployment of a multi-step pipeline that retrains and deploys the ML model prediction service into production. This separate pipeline requires teams to automate steps to train and validate new models prior to deployment, adding another layer of complexity when trying to achieve continuous delivery.
Benefits of a robust CI/CD pipeline
The main benefits of a robust CI/CD pipeline include:
Fast building, testing and deployment
CI/CD allows for each minor update to be deployed directly after testing, rather than waiting for multiple changes to stack up before they are implemented. Because large inherently carry more risk in terms of failure, CI/CD reduces that risk factor of each deployment.
Scaling to meet demand in real-time
Traditional pipelines come with limited capacity, however, serverless CI/CD pipelines scale their capacity up or down in response to project demand. From an economic perspective, this also means that teams pay only for what they’re using, meaning they can less capacity for small projects but have the flexibility for high capacity when needed.
More efficient building of new pipelines
CI/CD pipelines that have microservice architectures enable pieces of pipelines to be recycled and put together into new pipelines quickly, rather than having to re-write the same piece of code for each new pipeline.
Producing clean, identical outputs
In ML model development, a good deal of frustration can be caused by intermittent failures. A reliable CI/CD pipeline helps to eliminate this problem by consistently producing clean, identical outputs for each input.
Automating the delivery process
CI/CD allows ML teams to run and visualize the entire end-to-end model development process, dramatically reducing instances of human error, especially for repetitive tasks.
Ready to get started with CI/CD for machine learning?
Implementing machine learning in a production environment is about far more than just deploying a model for prediction. Setting up a CI/CD machine learning pipeline enables ML teams to automatically build, test, and deploy new ML pipeline implementations and iterate quickly based on changes in data and business environments.
Gradually implementing CI/CD best practices into ML model training and pipelines results in significant automation of ML development and optimization and can be accomplished quickly and easily using JFrog ML.
JFrog ML is a full-service machine learning platform that enables teams to take ML models and transform them into well-engineered applications. Our cloud-based platform removes the friction between ML developers and operations enabling fast iterations, limitless scaling, and customizable infrastructure.
Come improve the quality and efficiency of your AI/ML development by taking an online tour, scheduling a one on one demo or starting a free trial at your convenience.
