Ensuring Data Integrity and Continuity for Machine Learning Projects
Introduction
In a typical Machine Learning project, the final implemented solution should provide automated training and implementation of the selected models. This is where CI/CD comes into play: This continuous integration / continuous deploying solution provides an end-to-end pipeline that completes the cycle of a full project and ensures the model's performance. Initially, Continuous Integration and Deployment is a DevOps technique to implement an automated pipeline for production’s sake by:
- streamlining (rationalization)
- testing
- deploying/ production
The DevOps field corresponds to a collection of processes that tries to reduce the development life cycle of a system by enabling the continuous delivery of high-quality software.
MLOps, on the other hand, is the process of automating and industrializing machine learning applications and workflows. CI/CD represents here an automation workflow of the ML pipeline through the following operations:
- building the model
- testing
- deploying
This also prevents the data scientist to take care and worry about this process, by ensuring no human negligence and constant improvement of the model efficiency by permanent monitoring of the ML model. Any change in the model construction is thus eased and its development automated with reliable delivery.
As the CI/CD workflow will automate the different steps of an ML project, let’s do a quick reminder about the typical lifecycle of an ML project.
- Data preparation: In most cases, the data is initially presented in raw form. For this reason, it is necessary to perform a few steps of preprocessing these data sets to make them usable for the modeling step. This step is generally performed by the Data Scientist or sometimes by the Data Analyst and may require the use of tools such as Apache Spark, MySQL or Python, and libraries such as Pandas or Numpy.
- Model Training: This step led by the Data Scientist is the main focus of the project life cycle: the purpose of the model implementation is to respond to a specific problem by designing and setting the appropriate algorithm. This iteration usually requires the import of tools such as TensorFlow, PyTorch frameworks, or the library Scikit-Learn.
- Model Deploying: Once the model is ready, the Machine Learning Engineer or the Data Engineer is intended to make it available to the customer for easy and convenient use.
- New raw data: Although the project may be expected to be coming to an end, very often the Data Engineer receives new raw data available after these steps. They must therefore be integrated into the cycle described above to refine and improve the model performance developed previously.
Understanding CI/CD
- What is CI/CD?: Continuous Integration (CI) and Continuous Deployment (CD) are best practices in software development that aim to enhance collaboration and streamline the release process.
- Continuous Integration (CI): CI involves frequently merging code changes from various contributors into a shared repository. Each integration triggers an automated build and testing process to identify and address integration issues promptly. This ensures that the codebase is always functional and ready for deployment.
- Continuous Deployment (CD): CD takes CI a step further by automating the release of validated code into production. With CD, developers can consistently and safely deploy changes to users, minimizing manual interventions and reducing the risk of errors.
Reference
- This is based on: https://github.com/arunp77/Machine-Learning/tree/main/Projects-ML
- Credit Goes to: https://www.Datascientist.com/
- Let’s connect on Linkedin: https://www.linkedin.com/in/arunp77/
