Contact us anytime to know more — Abhishek P., Founder & CFO CISIN
Understanding CI/CD
Continuous Integration (CI) and Deployment, or Continuous Deployment (CD), are practices in software development that promote frequent code integration and automated Deployment of models.
Continuous Deployment automates Deployment to ensure rapid and dependable Deployment of models developed through Continuous Integration/CD.
Continuous Deployment can also be applied when developing machine learning (ML) models - in this instance, integrating code changes to an inclusive repository before verifying them through automated tests.
At the same time, Continuous Integration automates Deployment for rapid model updates and rollouts. CI/CD Configuration Management can help develop and deploy machine learning models quickly and reliably.
Machine Learning Benefits From CI/CD
- Faster iterations: of CI/CD automate the integration and testing of machine learning models, providing developers with continuous updates as they integrate changes while receiving immediate feedback about the performance of their models. This rapid feedback allows rapid hypothesis testing and quickly converge towards an optimized model.
- Improved collaboration: An ML project often includes numerous team members - data scientists and engineers. Continuous Integration/Continuous Deployment facilitates teamwork through its central repository for Code and models; developers can easily merge changes from different branches seamlessly for consistency between releases; this promotes collaboration while decreasing conflict during development processes.
- Enhanced reliability: The CI/CD pipeline ensures robustness and reliability during Machine Learning deployment by automating testing processes that ensure code changes don't introduce regressions or break functionality. Each time a commit is made, automated tests involving unit and integration tests should run to verify their stability and reliability for every model used.
- Version Control and Reproducibility: CI/CD practices greatly emphasize version control to facilitate reproducible machine learning experiments. Every change to model or Code is tracked in version control history for auditing purposes and easy rollback to previous versions and roll forward of experiment results if required, ultimately helping share experiments while guaranteeing reproducibility.
- Continuous monitoring and feedback: Continuous Integration/Continuous Deployment enables constant monitoring of machine learning models deployed into production. Developers can monitor model performance, identify anomalies, and send alerts when potential issues emerge via integration monitoring tools in the deployment pipeline - providing an actual time feedback loop for proactive maintenance and prompt remediation of deployed ML models.
Implementing CI/CD In Machine Learning
The following components are vital to implementing CI/CD effectively for ML:
- Version control: Establish a version management system like Git to monitor and organize your codebase. Create a Git repository, commit all initial code changes, and establish branches for any features or experiments you plan to test in separate environments.
- Automated Tests: Create an extensive set of mechanical tests to check the reliability and correctness of your machine learning code, from unit, performance, and integration tests - using testing frameworks such as pytest or unittest for writing and running them.
- Continuous integration (CI): Select a Continuous Integration server such as Jenkins, Travis CI, or GitLab CI/CD to automate ML code integration and testing. Configure it to monitor repository commits or pull requests and trigger build processes when they happen; add custom scripts or configuration files that install dependencies and run tests for optimal results.
- Dependency management: Use a package manager such as pip or conda to manage dependencies in your Python environment, creating and updating requirements.txt/environment.yml with all packages required along with versions for a more straightforward installation process and consistent dependencies across domains. This ensures consistency across environments while simplifying installation procedures.
- Model packaging: Create a standard format for packaging and storing your ML models; pickle files, HDF5 files, or ONNX are among the more widely used methods. Next, create an automated function or script to load and save models using that particular format while tracking versioning techniques that allow for version management of models.
- Continuous Deployment (CD): Automate your deployment process so models can seamlessly transition from development environments into production ones. Use tools or scripts such as Docker or Kubernetes for this, then set up pipelines for Deployment that pull all new models and dependencies from repositories before being deployed into target environments.
- Collaboration and Code Review: Code review practices facilitate team collaboration among members by maintaining quality code without errors, ensuring code changes are reviewed before implementation, and using platforms like GitHub or GitLab, which offer features designed specifically to manage code reviews and facilitate collaboration.
- Monitoring: Integrate monitoring tools and practices into your CI/CD pipeline to assess the performance of deployed machine learning models. Utilize tools like Prometheus or ELKstack to collect metrics and logs; set alerts if abnormal behavior or performance degradation arises.
- Continuous improvement: Review your Continuous Integration/Deployment processes frequently to identify issues and optimize performance. Metrics like build times, coverage of tests, and success rates in Deployment can help identify areas for improvements and then iterate on them for increased efficiency and reliability.
Introduction Of CI/CD To Mlops
Implementing Continuous Integration/Continuous Delivery in DevOps is straightforward: Code, build, test, and release to production.
DevOps is also part of MLOp's machine learning development processes to make reliable large-scale machine learning solutions at scale.
Implementation/Control/Deployment in the Machine Learning Lifecycle as part of MLOps presents unique difficulties.
Operationalizing machine learning systems becomes even more complex due to added data sources, model parameters, and configuration versions that further complicate operations management. This article will introduce the fundamental CI/CD process and discuss what implementing one for machine learning entails.
What Is A CI/CD Pipeline?
A Continuous Integration/Continuous Deployment workflow (CI/CD pipeline) is an automated workflow that assists software delivery from source code to production, typically consisting of four stages.
- Source: Pushing Code to a source code repository initiates the CI/CD process.
- Build Code: is packaged together with all its dependencies and made executable.
- Test: Automated testing should ensure the Code works as intended and detect bugs quickly before it enters production.
- Deployment: Software that has successfully passed all automated tests can be deployed directly into production environments.
A Continuous Integration/Continuous Deployment system offers many advantages to developers. A sound CI/CD system enables rapid code implementation and automating of build, test, and Deployment into the production of new software versions.
Regular iterative deployments lower risk per Deployment while helping detect issues quicker in production environments.
An effective CI/CD pipeline offers many advantages for automating software delivery processes and eliminating human mistakes that might otherwise arise during manual testing and Deployment of applications.
Want More Information About Our Services? Talk to Our Consultants!
Implementing A CI/CD Practice For Ml Pipelines
As previously discussed, creating a Continuous Integration and Continuous Deployment pipeline for machine learning involves automating the building, testing, and Deployment of systems used for training ML models for prediction.
Pipeline Continuous Integration
Continuous Integration (CI) Process in which changes made to the source code repository (usually Git) are built, tested, and packaged for delivery as soon as changes occur in the source code repository (usually Git-based).
The Pipeline Continuous Integration process includes three stages - Development, Build, and Testing.
During the development phase, you can iteratively test new machine learning models and algorithms, orchestrated and tracked through experiment steps.
When ready, select your ideal model before pushing its Code into the source code repository for Deployment into an ML pipeline.
Once changes in a source code repository have been identified, an automated CI/CD Agile Development Delivery process begins automatically.
At build time, an ML pipe and its components and any dependencies are constructed into packages, container images, or executable files for distribution to downstream environments.
Requires Testing
Testing comes after building the machine learning pipeline with dependencies is complete and is divided into three steps within pipeline CI:
- Unit tests
- Tests on data and models
- Tests of Integration
Unit tests involve logic and methods implemented within a model for feature engineering that have been created via unit tests, with their goal being to detect bugs in feature creation code and any discrepancies between specifications and reality.
Pipeline Continuous Integration also requires data and model testing and unit tests for optimal operation of pipeline CI environments.
Data tests include checking schema and statistical properties and feature importance tests, which measure data dependencies; model testing typically entails verifying that loss in machine learning models decreases with each iteration and validating model performance to ensure no overfitting exists within them.
Pipeline CI also involves performing integration tests to verify that the machine learning pipeline has been constructed and completed.
Pipeline Continuous Delivery
At each phase in the Pipeline Continuous Delivery (CD) Process, CI/CD Pipelines continually deploy new implementations of their respective multilayer pipeline (ML Pipeline), providing continuous training (CT).
As part of CD, deployed machine learning models in production will automatically trigger their retraining based on triggers from a live environment, for instance, due to performance degradation or changes to data distributions used to predict (commonly known as concept drift).
Once trained, trained models will automatically become predictive services within the CD process. The CT pipeline will automatically initiate to update and retrain its model with more current data.
Building CI Workflow For Ml Projects
Prerequisites
As stated, we identified two challenges to our success that must be overcome. Various open-source tools provide quick fixes.
I selected Data Version Control (DVC) because of its user-friendly command-line interface; no coding is needed! In addition, DVC was specifically created to work seamlessly alongside Git, offering additional features such as an ML pipeline.
Quality control becomes more challenging when it no longer involves passing or failing decisions. Two machine learning models may give similar predictions; therefore, the question becomes which model is better and by how much.
Performance metrics like accuracy loss ratio (ALR), R-score, or any combination thereof would provide helpful indicators.
Responsibility Of The Development Team
By switching evaluation metrics for tests, you solve and create problems simultaneously. Metrics help refine criteria while being specific to every situation - something only the development team is qualified to decide upon; standardization cannot occur here.
To overcome this obstacle, responsibility should now lie with the development team. Evaluation metrics will be used to judge quality; thus, the ML pipeline must produce model and performance reports in an easily comparable format, such as JSON, within the folder model/performance.json.
Also Read: Why CI/CD required for software development
Four Ways Machine Learning Teams Use CI/CD For Production
DevOps has adopted Continuous Integration and Continuous Deployment or Continuous Deployment as one of their core concepts to support machine learning operations.
Within DevOps, Continuous Integration/Continuous Deployment or Continuous Deployment encompasses tools and methodologies designed to deliver software applications reliably - this involves streamlining build, testing, and Deployment into the production of applications efficiently. Below is the definitions of these terms:
- Continuous Integration (CI) is a practice that automates the building and testing of Code each time it's committed to version control and pushed into a code repository.
- Continuous Delivery (CD) is a practice that involves deploying each build into a production environment and automating the integration and testing before the Deployment.
- Continuous Deployment (CD), which automates the configuration and Deployment to the production environment, complements continuous integration by adding additional steps.
Recently developed Continuous Integration/Continuous Deployment tools were mainly created for traditional software applications.
You likely already understand that building and deploying traditional apps differs significantly from machine learning applications.
Azure Devops - Continuous Integration And Delivery (CI/CD), Machine Learning (Ml), With Azure Devops
This section will demonstrate how GitOps provides teams with a robust framework to run continuous integration/continuous delivery (CI/CD) pipelines and machine learning pipelines.
Industry
Retail and consumer products
Use Case
This team uses machine learning to assist a client in automating ticket resolution for users or maintenance issues quickly and automatically.
Machine learning provides rapid solutions.
Overview
The team Build CI/CD Pipeline Azure to facilitate its continuous integration/continuous delivery (CI/CD) workflow.
Production and development environments for their machine learning (ML) workflows were configured, as were all relevant CI/CD processes before their model's Deployment into production and post-deployment.
Continuous Integration And Delivery (CI/CD) For Ml Using Gitops, Jenkins, And Argo Workflows
This section will demonstrate how a team can utilize GitOps to design an infrastructure to support continuous integration/continuous deployment (CI/CD) pipelines for continuous learning pipelines.
Industry
Computer software
Core tools for CI/CD
- Argo
- Jenkins
Overview
Our team used Jenkins as part of its Continuous Integration/Continuous Deployment solution, running code quality checks, smoke tests, production-like runs in their test environment and reviewing model code using one pipeline with automated unit tests and code reviews for every pull request submitted by reviewers.
Pull requests were put through automated smoke tests that included model training and prediction capabilities; additionally, an entire pipeline ran with accurate data to test whether each stage performed as intended.
Once trained, a model's quality report is generated and reviewed manually by an industry specialist before Deployment of any validated models that have passed all previous checks and validation processes.
Continuous Integration And Delivery (CI/CD) For Ml Using Aws Codepipeline With Step Functions
The team used AWS Step Functions and AWS CodePipeline to orchestrate its CI/CD workflow.
Industry
Transportation and Logistics
Use Case
In this case, a professional services and consulting company team worked on a public project. They built machine learning apps that addressed problems such as:
- How to predict the time it will take for a package to arrive.
- Indicating a location based on unstructured address data and resolving it to a coordinate system (latitude/longitude).
Core tools for CI/CD
- AWS CodeBuild is a fully managed continuous integration tool that compiles Code, runs tests, and creates software packages ready for Deployment.
- AWS CodePipeline is a fully managed continuous delivery service that helps automate release pipelines.
- AWS Step Functions is a serverless function orchestrator, which makes it simple to sequence AWS Lambda Functions and multiple AWS Services.
Overview
AWS Cloud offers managed CI/CD tools such as AWS Step Functions and AWS CodePipeline to help machine learning teams achieve continuous integration and delivery.
The team used AWS CodeCommit to trigger a build in CodePipeline through an AWS CodeBuild task. AWS Step Functions orchestrated the workflows of every action taken from CodePipeline.
Understanding the Architecture
AWS Step Functions workflow orchestration made life simpler for the developers managing multiple pipelines and models in CodePipelines.
Deployment updates were more accessible as each pipeline job focused on one process rather than several at once; build tests were quicker to deliver and troubleshoot.
An example project using AWS CodePipeline and Step Functions to organize machine learning pipelines requiring custom containers is provided here.
CodePipeline calls Step Functions with their container image URI and tag passed from CodePipeline for orchestration purposes.
This blog post will offer more insight into the architecture. Our team utilized these tools for orchestration and management; however, AWS recently introduced Amazon SageMaker Pipelines, which provides an easy CI/CD solution explicitly designed to support Machine Learning applications.
Continuous Integration And Delivery (CI/CD) For Ml With Vertex Ai On Google Cloud
This section explores an example team that successfully adapted workflow orchestration and software management tools tailored explicitly for machine-learning projects to use pipelines more tailored towards machine-learning initiatives than typical software engineering efforts.
Industry
Business Intelligence and Financial Technology Services
Use Case
Digits Financial, Inc., a fintech company, offers a dashboard allowing startups and small businesses to monitor their expenses using machine learning.
Their use cases include:
- Create the most influential finance engines for modern companies, which can ingest financial data and convert it into a live business model.
- Predicting future events by extracting information from unstructured data.
- Clustering information helps to highlight the most critical data for customers.
Core tools for CI/CD
- TensorFlow Extended
- Vertex AI Pipelines
Overview
Digits' team was able to effectively oversee, coordinate, and execute continuous integration, delivery, and Deployment of machine learning pipelines using Vertex AI Pipelines managed product and TensorFlow Extended running on Google Cloud Infrastructure.
The team utilized an ML-native tool instead of traditional continuous integration/continuous delivery processes to ensure their models were going through standard workflows such as feature engineering, scoring models, model analysis, and validation, as well as monitoring them all within one pipeline.
Machine Learning Pipelines
Tensorflow Extended allowed the team to treat every component of their machine-learning stack as an individual step that could then be orchestrated using third-party tools like Apache Beam Airflow or Kubeflow Pipelines, depending on whether their pipeline was deployed for testing or production environments.
Furthermore, custom components could easily be created and added to their channel without traditional CI/CD barriers getting in their way.
It is modified from this source.
They shifted away from Kubeflow ML pipelines towards Google Cloud Vertex AI Pipeline, which allowed them to integrate model development (ML) and operations (Ops) into high-performing steps with reproducibility.
Machine Learning Pipelines: Benefits
In a few ways, the team benefited from using ML workflows to orchestrate their ML workloads. Hannes Hapke describes in this video how the startup gained the following benefits.
- The team's DevOps requirements were reduced by using ML pipelines
- When they hosted the pipelines in their infrastructure, managed ML pipes reduced the cost of running 24/7 clusters.
- The team could focus on their other projects because model updates were easily integrated and automated.
- Model updates were consistent between all ML projects because teams could reuse components or the entire pipeline and run the same tests.
- All machine learning metadata and information are in one place.
- Now, models can be automatically tracked and audited.
Want More Information About Our Services? Talk to Our Consultants!
Conclusion
An automated CI/CD Continuous Integration Continuous Deployment ML system enables you to automatically build, test, deploy, and iterate on new machine learning implementations in response to changes in business or data environments.
MLOps makes this easier; use its platform for automating model development pipelines to automate your ML system and achieve accurate continuous delivery.
Implementation and continuous deployment (CI/CD) offers numerous advantages when deploying and developing machine learning (ML) models.
By adopting these practices, organizations can accelerate ML development processes while encouraging team member collaboration, improving model reliability and reproducibility, and offering continuous monitoring and feedback loops for constant monitoring and feedback loops. As machine learning advances, CI/CD methods implementation will become even more vital in driving innovation, efficiency, and value creation for systems using machine learning technology and applications.
