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Applying CI/CD to AI/ML Product Development: Use Cases and Examples with GitLab, Travis, Jenkins, and Ansible

Introduction

Continuous Integration (CI) and Continuous Deployment (CD) are crucial practices in modern software development, enabling teams to build, test, and deploy software at a rapid pace.
The use of Feature Engineering and Feature Switches to enable or remove functionality from the Build in a Automated Way.

In the field of artificial intelligence (AI) and machine learning (ML), CI/CD plays a vital role in managing complex workflows, ensuring code quality, and accelerating the delivery of AI/ML products.
This lecture will discuss the application of CI/CD to AI/ML product development and explore use cases and examples of some of the tools you will be using:

GitLab
Travis
Jenkins
Ansible
With examples of yow they will be used in a typical AI/ML product build and deployment workflow.

I. Benefits of CI/CD in AI/ML Product Development

Accelerated development cycles
Improved collaboration and communication among team members LO 2 from Course Outline
Reduced risk of errors and bugs. In terms of engineering and building the quality software product: Refer to Lecture Slides on Software Testing:
Streamlined validation and testing processes
Enhanced scalability and flexibility: The common way to achieve this is with Feature Engineering and Feature Switches.

II. GitLab: All-in-one CI/CD Platform for AI/ML Projects

Overview of GitLab
Version control system
Built-in CI/CD tools : Therefore GitLab can be used as a CI CD Pipeline.
Integration with external CI/CD services: Integrating GITLab with Travis.com, CircleCI.com
GitLab CI/CD pipeline
Stages: build, test, and deploy
How to Configure the CI CD triggers and actions via .gitlab-ci.yml file
Integration with popular AI/ML frameworks like TensorFlow
Monitoring and tracking AI/ML models with GitLab
Building the AI ML Product is largely concepts with training and eliminating Model Drift in the ML OPS MODEL.
Model versioning: Models - The ML Ops Model - are created by the Process of Baysian Model Training. Read more here:
Performance metrics visualization
Collaboration features (e.g., issue tracking, merge requests) [Project Management Consideration.]

III. Travis CI and Jenkins: Powerful CI/CD Tools for AI/ML Workflows

Travis CI A SAAS software as a service CI CD pipeline with integration to other related tools like GitLab.
For your your Assignment:
Overview and key features
Integration with GitHub and GitLab
Configuration via .travis.yml file: YAML is an xml configuration file grammer
Building and testing AI/ML models
Deploying AI/ML models to production
Jenkins
Overview and key features
JENKINS is an automation server: You can configure it to automate any type of IT Operations.
Extensible plugin system
Configuration via Jenkinsfile or web-based interface
Building and testing AI/ML models
<Assignment Topic>Deploying AI/ML models to production
What exactly is the ML OPS Model?
What does it mean to push the ML Ops model into production?
Assignment: Comparison between Travis CI and Jenkins
Assignment Questions:
Advantages and disadvantages
Choosing the right CI/CD tool for your AI/ML project

IV. Ansible: Automating Deployment and Configuration Management in AI/ML Projects

Overview of Ansible: Used to create Build Tooling Automation ​

Ansible is an open-source automation tool that simplifies complex tasks such as configuration management, application deployment, and task orchestration. Orchestration is Process Choregraphy It uses a human-readable language called YAML (XML type scripting language)

Yet Another Markup Language) to define automation tasks and uses an agentless architecture, relying on SSH or APIs for communication with remote systems [1].

In the context of CI/CD for AI/ML, Ansible can be used to automate the deployment and configuration of infrastructure required for training and deploying machine learning models.
Seamless integration with popular CI/CD tools like Jenkins [2] and Kubernetes [3] makes it a powerful choice for managing AI/ML pipelines.

Here's how Ansible can be applied in a typical AI/ML CI/CD pipeline:

Infrastructure Provisioning: Ansible can be used to provision and configure infrastructure resources like virtual machines, storage, and network components needed for AI/ML workloads. This ensures consistent environments across development, testing, and production stages.
Dependency Management: AI/ML projects often require specific software packages, libraries, and frameworks. Ansible can automate the installation and configuration of these dependencies, ensuring that the required versions are consistently used across environments.
Model Training and Validation: Ansible can help automate the process of training and validating machine learning models by orchestrating the execution of training scripts, managing data pipelines, and monitoring model performance metrics.
Model Deployment: Once a model is trained and validated, Ansible can be used to deploy it into production environments in a consistent and repeatable manner. This includes containerization and deployment to Kubernetes clusters [3] or other target environments.
Monitoring and Maintenance: Ansible can be integrated with AIOps platforms like Instana [1] to provide automated application observability and enterprise IT automation, improving SLAs and reducing unplanned service downtime. Read my book on AI DEV OPS: ​
In conclusion, Ansible is a powerful tool for managing AI/ML CI/CD pipelines, offering automation and consistency across the entire process, from infrastructure provisioning to model deployment and maintenance. By integrating with popular CI/CD tools and platforms, Ansible ensures a seamless and efficient workflow for AI/ML practitioners.
References:
Agentless architecture
YAML-based scripting language
Extensive library of pre-built modules
Ansible playbooks for AI/ML projects

Installing dependencies
Configuring environments
Deploying AI/ML models to production
Integration with CI/CD pipelines

Triggering Ansible playbooks from GitLab, Travis CI, or Jenkins
Ensuring consistent and reliable AI/ML deployments

V. Sample of a Complete Workflow for AI/ML Project using CI/CD

{These topics will span your Assignment and your Project}
Setting up a GitLab repository and configuring the CI/CD pipeline
Developing AI/ML models using TensorFlow
Building and testing models using Travis CI SAAS or Jenkins : Jenkins is a J2EE jar Web Application you install and run on your own laptop.
Collaborating and iterating on AI/ML models in GitLab
Deploying AI/ML models to production using Ansible
Monitoring and tracking AI/ML model performance in GitLab

Conclusion

CI/CD is a game-changer for AI/ML product development, facilitating rapid iteration, improved collaboration, and reliable deployment of AI/ML models. By harnessing the power of GitLab, Travis CI, Jenkins, and Ansible, teams can build robust AI/ML workflows that accelerate innovation and enable the delivery of high-quality, cutting-edge AI/ML products.

A Case Study of doing this at work:

Hi, my name is Tanveer, and I'm a recent graduate from Cestar College. I've just started my first job, working on an AI/ML product that focuses on optimizing supply chain management. Our goal is to create a more affordable, ethical, and sustainable flow of goods that benefits everyone. I'm excited to share my experience using cutting-edge tools and methods to make a difference in the world.
Every day, I start my work by checking the progress of our AI/ML models on GitLab. The GitLab platform provides excellent version control and collaboration features, allowing our team to work efficiently and stay up-to-date with the latest changes. It has been an invaluable tool for managing our codebase and keeping track of the various experiments we run.
Once I'm caught up, I begin working on improving our AI/ML models. I use TensorFlow to create and train models that can predict and optimize various aspects of supply chain management, such as demand forecasting, inventory management, and transportation planning. TensorFlow's flexibility and scalability have made it easier for me to experiment with different algorithms and techniques, which has been instrumental in finding the best solutions for our product.
As I iterate on the models, I rely heavily on Continuous Integration (CI) and Continuous Deployment (CD) principles to ensure that my work consistently meets the high standards of quality we set for ourselves. I use tools like Travis and Jenkins to automate the process of building, testing, and deploying our AI/ML models. These tools help us catch any potential issues early on and prevent them from affecting the final product.
One of the key aspects of our AI-driven supply chain management system is its ability to adapt to changing conditions in real-time. To achieve this, we use Ansible for automating the deployment and configuration of our infrastructure. By automating these processes, we can quickly and reliably scale our system to handle fluctuations in demand and ensure that our AI models are always up-to-date with the latest data.
As our AI/ML product continues to evolve, I'm proud of the positive impact it's having on supply chain management. By optimizing the flow of goods, we're not only reducing costs but also making the entire process more ethical and sustainable. The AI models I've helped create are now actively reducing waste, improving working conditions, and promoting fair trade practices throughout the supply chain.
Every time I see the tangible results of my work, I'm reminded of why I chose this career path. My journey from Cestar College to my current role has been challenging, but it has also been incredibly rewarding. I hope my story serves as a source of inspiration and motivation for others who are considering a career in AI/ML. The potential for positive change is immense, and with the right tools and dedication, we can all play a part in shaping a better future.
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