Machine Learning Operations (MLOps) Course

1. Deep Explanation of MLOps MLOps, or Machine Learning Operations, applies DevOps principles to the end-to-end lifecycle of machine learning models. It bridges the gap between data science and production, ensuring that models are reproducible, scalable, and maintainable. Key stages include:

• Model Development: Data scientists explore and train models, using version control for code and data.

• Continuous Integration (CI: Automated testing, linting, and validation of model code, data pipelines, and configurations.

• Containerization: Packaging models and dependencies into Docker images for consistent deployments.

• Continuous Deployment (CD): Automated release of model images to Kubernetes clusters via GitLab CI/CD and GitOps tools like Argo CD.

• Monitoring and Observability: Tracking model performance, data drift, and infrastructure metrics in production.

• Governance and Compliance: Managing model lineage, access controls, and auditability.

By integrating these stages, MLOps accelerates model delivery, improves reliability, and enables collaboration between data science and engineering teams.

2. Example of an MLOps Workflow

An end-to-end MLOps workflow might look like this:

1. Code Commit: Data scientists push model code and pipeline definitions to GitLab.

2. CI Pipeline: GitLab CI runs unit tests, data validation checks, and builds a Docker image containing the model.

3. Artifact Registry: The built image is pushed to a container registry (e.g., GitLab Container Registry).

4. GitOps Deployment: Argo CD monitors a Git branch containing Kubernetes manifests for the model service.

5. Kubernetes Deployment: Argo CD syncs changes, deploying the new model version to the cluster.

6. Inference Service: A Kubernetes Service exposes the model via REST or gRPC for inference requests.

7. Monitoring: Prometheus and Grafana collect metrics on request latency, error rates, and data drift.

8. Feedback Loop: Logged inference data and performance metrics feed back into retraining pipelines.

3. Step-by-Step Creation of an MLOps Tutorial Application

A. Sample ML Model to Deploy We will use a simple Scikit-learn Iris classifier. The training script (train.py) resides in model/, producing a serialized model iris_model.joblib.

B. Deployment with Argo CD, GitLab CI, and Kubernetes The pipeline and deployment consist of:

• GitLab CI/CD: Defined in .gitlab-ci.yml, it tests, builds, and pushes the Docker image to the GitLab Container Registry.

• Argo CD: Uses an application.yaml in argo-cd/ to watch the k8s/ manifests branch and deploy updates to the Kubernetes cluster.

• Kubernetes: Deployment and Service YAML files in k8s/ directory define the model-serving service.

C. Example Repository Structure mlops-tutorial/ ├── .gitlab-ci.yml ├── argo-cd/ │ └── application.yaml ├── model/ │ ├── train.py │ └── iris_model.joblib ├── k8s/ │ ├── deployment.yaml │ └── service.yaml ├── src/ │ └── serve.py └── README.md