The provided strategy outlines the implementation of a tweet sentiment analysis project using MLOps practices. Initially, it emphasizes establishing a robust development environment by setting up a virtual environment, using cookiecutter for a project structure, and initializing version control with Git. This ensures consistency and manages dependencies effectively, utilizing tools like GitHub for collaborative work.
Subsequently, data management and versioning are prioritized. DVC is used to track dataset versions, store data on AWS S3, and automate data handling processes, ensuring data integrity and seamless integration into the project structure. This step fosters a reliable and reproducible data handling process.
The final strategy focuses on the development, monitoring, and deployment of the machine learning pipeline. This involves defining modelling pipelines, managing experiments and models with MLflow, and automating processes through CI/CD pipelines. Deployment is carried out using AWS Lambda for a REST API, with ongoing monitoring to ensure model performance and adaptability over time.
The strategies
⛳️ Strategy 1: Establish your development environment
- Set up a virtual environment using Python's venv module
- Install cookiecutter and create a project structure template
- Initialize a Git repository for version control
- Create and configure a GitHub repository for the project
- Install essential Python packages for data processing and machine learning
- Define a requirements.txt file to manage project dependencies
- Set up pre-commit hooks to enforce code styles
- Create a README.md file outlining project objectives and setup instructions
- Configure environment variables for secure management of credentials
- Document initial project structure and setup process
⛳️ Strategy 2: Implement data management and versioning
- Identify a dataset containing tweets for sentiment analysis
- Set up a DVC repository to track dataset versions
- Push the dataset to a remote storage like AWS S3
- Document data transformation steps using DVC pipelines
- Integrate data versioning into the project structure
- Automate data download and preparation using DVC commands
- Ensure data integrity by checking dataset hashes
- Keep data configuration files like .dvc files under version control
- Create a data dictionary to describe dataset features
- Use DVC to monitor dataset changes and update model training accordingly
⛳️ Strategy 3: Develop, monitor, and deploy the machine learning pipeline
- Define a clear modelling pipeline using scikit-learn or similar frameworks
- Checkpoint training experiments using MLflow to track model parameters and performance
- Save model artefacts and logs to a cloud storage service like AWS S3
- Implement automated unit tests to verify data and model integrity
- Set up CI/CD pipelines using GitHub Actions or similar services
- Schedule builds for training and deployment using a CI/CD tool
- Visualise the pipeline and model metrics using MLflow or Dagshub dashboards
- Deploy the model as a REST API using AWS Lambda or a similar service
- Continuously monitor the deployed model for performance and drift
- Maintain a living documentation of the project's workflow and changes
Bringing accountability to your strategy
It's one thing to have a plan, it's another to stick to it. We hope that the examples above will help you get started with your own strategy, but we also know that it's easy to get lost in the day-to-day effort.
That's why we built Tability: to help you track your progress, keep your team aligned, and make sure you're always moving in the right direction.
Give it a try and see how it can help you bring accountability to your strategy.
