Unlocking the Power of MLOps

Managing the Lifecycle of Machine Learning Models (Part 2)

In the previous article we started to talk about MLOps in general and highlighted the main steps required to start working as an MLOps Engineer.

In this article, we will focus on an important aspect of your Machine Learning Project pipeline which is automating the build, train, test and deployment of your Models through CI/CD Pipelines.

CI/CD Pipeline

If you’re coming from a non-software Engineering background and trying to make your way into Data Science and Machine Learning, then keep in mind that there is a number of shared concepts between both fields and one of which is CI/CD. If you have no background at all and don’t know where to start, then checkout this article to know more.

If you have read both the terms MLOps and CI/CD interchangeably and it makes you confused as if there actually is a difference between both, then read this article to the end and I’ll make sure to clear any confusion.

CI/CD stands for Continuous Integration/Continuous Delivery (or Deployment), which is a set of practices used in software development (including ML) to automate and streamline the processes of building, testing, and deploying software changes.

Continuous Integration (CI)

Continuous Integration is a practice where developers regularly merge their code changes into a central repository. This is done frequently, often multiple times per day. Each time code changes are merged, the CI system automatically builds the code, runs unit tests and other automated tests, and reports the results. This allows for early detection of integration errors, and helps to ensure that the code is always in a working state.

Continuous Delivery (CD)

Continuous Delivery is the next step after Continuous Integration. It focuses on automating the process of deploying the code to production. With Continuous Delivery, the code is always in a releasable state, meaning that it can be deployed to production at any time. This is achieved by automating the build, test, and deployment processes. When a new code change is committed, it triggers the automated build and test processes, and if everything passes, the code is automatically deployed to a staging environment where further testing can be carried out.

Continuous Deployment:

Continuous Deployment takes Continuous Delivery one step further. With Continuous Deployment, the code is not just automatically deployed to a staging environment, but is also automatically deployed to production if all automated tests pass. This requires a high degree of confidence in the automated tests, and is typically only used in organizations with very mature CI/CD processes.

A CI/CD pipeline is a set of practices and tools that enable software development teams to deliver code changes more quickly, efficiently, and reliably. The pipeline consists of a series of stages, each of which performs a specific task in the software development process.

CI/CD Pipeline Stages

1. Code management: This stage involves managing the code changes, which are typically stored in a version control system such as Git. Developers create, modify, and review code changes, which are then committed to the version control system.
2. Continuous Integration (CI): In this stage, the code changes are automatically built, tested, and verified for correctness. This ensures that any errors or issues are caught early in the development process, before they can cause problems further down the line
3. Continuous Delivery (CD): In this stage, the code changes are automatically deployed to a testing or staging environment, where further testing and validation can be carried out. This ensures that the code is always in a releasable state, and that any issues are caught before they reach production.
4. Continuous Deployment: In this final stage, the code changes are automatically deployed to production if all tests and validations pass successfully. This allows teams to deliver changes to customers more quickly and efficiently, without the need for manual intervention.

CI/CD is an essential practice in machine learning that offers several benefits. By utilizing this practice, data scientists can iterate faster on their models, enhance the accuracy and reliability of their models, and minimize development and deployment time and costs.

Furthermore, CI/CD can help foster collaboration and improve communication between different team members. Ultimately, incorporating CI/CD in machine learning projects can lead to better quality models that are deployed more quickly and efficiently.

NOTE

Keep in mind that CI/CD are actually a set of practices and not a tool or framework by itself.

How to implement CI/CD Pipelines ?

Some ways to implement the CI/CD Pipelines depending on your infrastructure.

• GitHub provides a feature called GitHub Actions that allows developers to automate their workflows, including the building, testing, and deployment of their code changes. GitHub Actions can be used to create custom CI/CD pipelines that are tailored to the specific needs of a project.
• AWS CodePipeline: AWS CodePipeline is a managed CI/CD service provided by Amazon Web Services. It integrates with a wide range of AWS services and provides a visual interface for defining your pipeline.
• Google Cloud Build: Google Cloud Build is a managed CI/CD service provided by Google Cloud Platform. It provides a YAML-based configuration format and integrates with a wide range of Google Cloud services.

GitHub Workflows for CI/CD

GitHub workflows are a set of configurable automated processes that allow you to build, test, and deploy your code on GitHub. Workflows are defined using a YAML file that specifies a series of jobs and steps to be executed. Workflows can be triggered by various events, such as code pushes, pull requests, or other GitHub events.

Each workflow consists of one or more jobs, which can run on different operating systems or virtual environments. Jobs consist of one or more steps, which are individual commands that are executed in sequence. Steps can include running tests, building packages, deploying code, or any other task that can be automated.

GitHub workflows provide a powerful and flexible way to automate your development pipeline, making it easier to build, test, and deploy your code. Workflows can be customized to suit the specific needs of your project, and can help ensure that your code is always tested and deployed in a consistent and reliable way.

In short, GitHub workflows provide a way to implement CI/CD practices on GitHub, by defining automated processes that help you build, test, and deploy your code changes more efficiently and reliably.

Difference between MLOps and CI/CD

MLOps (Machine Learning Operations) is a set of practices that are focused on automating the Machine Learning pipeline, from data preparation to model deployment and monitoring. MLOps involves a range of activities, including data versioning, model training, model deployment, monitoring, and feedback loops.

CI/CD (Continuous Integration/Continuous Delivery) is a set of practices that are focused on automating the process of building, testing, and deploying code changes. CI/CD is typically used in software development, but can also be applied to Machine Learning projects to automate the testing, building, and deployment of models.

In practice, CI/CD and MLOps are used together to automate the entire Machine Learning pipeline, from data preparation to deployment. CI/CD is used to automate the testing, building, and deployment of models, while MLOps provides the overall framework and best practices for managing and scaling Machine Learning projects.

By combining CI/CD and MLOps, organizations can accelerate the development and deployment of Machine Learning models, while ensuring that the models are reliable, scalable, and meet the organization’s business objectives.

How to implement MLOps in your project ?

To automate a machine learning project pipeline, you can use a combination of tools and techniques. Here are some steps you can follow:

1. Define your pipeline: Start by defining the steps involved in your machine learning project pipeline. This might include data preparation, model training, evaluation, and deployment.
2. Choose a version control system: Use a version control system such as Git to keep track of your code changes and collaborate with other team members.
3. Set up a continuous integration and deployment (CI/CD) system: Use a CI/CD system to automate the testing, building, and deployment of your machine learning models. This can help you catch errors early and ensure that your models are deployed consistently and reliably.
4. Use containerization: Containerization can help you ensure that your models run consistently across different environments. Use a tool like Docker to package your code, dependencies, and configuration into a container.
5. Use an orchestration tool: An orchestration tool like Kubernetes can help you manage and scale your containers, making it easier to deploy your machine learning models in production.
6. Monitor your pipeline: Use monitoring tools to track the performance of your pipeline and identify any issues or bottlenecks.

That’s it for the CI/CD Pipeline, How to implement it (GitHub is an example) and the difference between CI/CD and MLOps.

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