MLOps in Practice: Building an End-to-End Image Classifier
In machine learning, training a model is often the easiest part. The real challenge lies in building a reproducible, scalable, and automated pipeline that takes a model from a Jupyter Notebook to production. In this post, I'll walk you through my latest project: a production-ready End-to-End Image Classifier that implements industry-standard MLOps practices.
The Tech Stack & Infrastructure
To ensure reliability and scalability, this project integrates several key technologies. Below are the infrastructure highlights used to power the pipeline:
- CI/CD: We utilize GitHub Actions to automate testing and reporting workflows on every pull request.
- Deployment & Build: The entire environment is containerized using Docker, ensuring consistency across development and CI environments.
- Infrastructure: Data versioning and remote storage are configured to integrate with AWS (S3) buckets via DVC.
Beyond the Model: Key MLOps Components
1. Configuration Management with Hydra
Hardcoding hyperparameters is a recipe for disaster. I used Hydra to manage configurations dynamically. This allows for easy experimentation without changing code:
# Run with different parameters instantly
python src/training/train.py model=resnet data=cifar10 hyperparameters.learning_rate=0.0012. Data Version Control (DVC)
Code is versioned with Git, but what about data? Using DVC, I track large datasets (like MNIST and CIFAR-10) and link them to specific commits. This ensures that every model training run is 100% reproducible with the exact data used at that time.
3. Continuous Machine Learning (CML)
One of the coolest features of this pipeline is the integration of CML. When a Pull Request is opened, GitHub Actions triggers a workflow that:
- Trains the model on a subset of data.
- Generates a classification report and confusion matrix.
Comprehensive Testing
To maintain code quality, I implemented a robust testing suite using pytest. The pipeline includes:
- Unit Tests: Checking data loaders, model output shapes, and training loops.
- Linting: Enforcing style with
flake8,black, andisort. - Type Checking: Static analysis with
mypy.
Experiment Tracking
Tracking metrics across dozens of runs can get messy. This project supports both MLflow and Weights & Biases (W&B) to log loss, accuracy, and hyperparameters, making it easy to compare different architecture decisions.
Conclusion
Building this project reinforced that MLOps is not just about tools—it's about culture and discipline. By leveraging Docker, GitHub Actions, and tools like DVC, we transform fragile scripts into robust engineering systems.