The best practices of MLOps can be classified into seven fundamental paradigms, when applied, and used thoroughly, enable us to construct systems of the highest quality.
- Collaboration: Teamwork is the cornerstone of all successful ML and software systems. Ensuring the MLOps organizational structure is built to facilitate collaboration between model development, data management, software development teams, and business stakeholders is crucial. Effective communication, continuous feedback, and sharing of knowledge, responsibilities, and goals improve ML models’ speed and quality while promoting innovation.
- Documentation: Arguably one of the two most essential practices in MLOps, documentation enables and maintains transparency, quality, and reproducibility for every step in the MLOps pipeline. Adequate documentation ensures that teams working together can understand each other, that errors are identified easier and faster, and that efficiency does not suffer when code ownership changes. Applying documentation across all the steps in the MLOps pipeline – data sources, labelling methods, metadata information, exploratory analyses, data validation, feature engineering and extraction, ML experiments, model selection criteria, model hyperparameters, deployment process, model served, refinement criteria, validation scores, and inferences served.
- Versioning: To ensure that changes made to and during each of the stages of MLOps are tracked and the history is maintained, and to allow the team to rollback to previous versions when issues arise or to reproduce previous results, versioning should be applied to datasets and data pipelines, training features, ML models and model training pipelines, and deployment pipelines.
- Continuance: This ensures that the ML system is served consistently, updated and improved regularly to meet the changing business requirements and can accommodate changes in hardware and technology architectures, including continuous integration, continuous delivery, continuous training, monitoring, and logging.
- Automation: To save time and computational resources and to enable efficient and effective ML operations, it is essential to automate as much of the MLOps as possible, such as the data pipeline components of ingestion, validation, transformation, and loading, the ML model pipeline components of continuous training, validation and logging, and the CI/CD pipeline including model deployment, monitoring, refinement, and inference serving.
- Testing: The accuracy of inferences served by machine learning systems and their consistent availability are crucial parameters for success. In addition to unit testing and integration testing to identify and fix issues before they impact production, MLOps includes model testing and validation, tests for model bias and security, and data integrity tests.
- Monitoring: Tracking the performance of ML models over time and identifying issues such as model drift, computational and numerical performance, and stability allow us to identify issues before they become significant problems and to course-correct when needed. Monitoring of the model and inferences being served for compliance, security, and bias ensure the reliability and accuracy of our system.
MLOps has become essential for successfully developing and deploying machine learning systems. In the changing and evolving landscape of machine learning development and the ongoing explosion in its applications, MLOps will increasingly play a crucial role in any organization’s success. Understanding and embracing this methodology is, therefore, an essential investment in the future for any industry or business. As machine learning continues transforming industries and businesses, adopting MLOps practices will become increasingly important to stay competitive and succeed.
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