The Road to MLOps: The 7 Principles of Machine Learning
November 11, 2021
Today, machine learning (ML) can be a game-changer for businesses. However, without some form of systematization, most ML projects don’t get past the initial experimental or prototype phase. MLOps brings business interest back into the forefront of ML operations where ML experts and data scientists work through the lens of an organizational interest with clear direction and measurable benchmarks.
The ML development lifecycle consists of three key pipelines: data preparation, modeling, and operationalization. MLOps principles aim to standardize and automate each of these areas.
Today, the level of automation of the data preparation, ML modeling, and deployment pipelines determines a company’s overall ML maturity. This level of maturity is correlated with the velocity at which the organization can train and deploy incremental ML models. The primary objective of an MLOps team is either to automate the deployment of ML models into core software systems or enable the models as a service component.
The ideal way to accomplish this is to automate the complete ML workflow, ensuring there is minimal or no need for manual intervention. MLOps includes the implementation of triggers for automated model training and deployment based on key data and performance indicators, as well as changes to model training and application code. An added benefit of the automation of testing is the ability to solve problems quickly without impacting production workloads. Lastly, to fully automate ML, teams need to leverage Continuous Integration (CI)/Continuous Delivery (CD) in order to automatically deliver new versions of code.
Continuous Everything (Continuous X)
The primary ML assets can be broken down into the ML model, ML model parameters, ML model hyperparameters, training scripts, and training and testing data. Continuous improvements require that all components and their dependencies are properly identified through versioning. ML artifacts are integrated into either a microservice or an infrastructure component, and, as such any new version of the ML, assets needs to be properly tracked and orchestrated. A deployment service such as MLOps can provide the appropriate logging, monitoring, and notifications to ensure that all ML models, codebases, and data artifacts are stable. This results in MLOps ensuring continuous integration, continuous delivery, continuous retraining, as well as continuous monitoring.
The goal of versioning within the framework of MLOps is to properly track the lineage of ML training scripts, ML models, and their underlying datasets over time. It ensures that as part of the framework, dedicated time and processes will focus on iterative improvements of scripts, models, and datasets. This is accomplished by tracking ML models and datasets through version control systems. Datasets are expected to change over time and in turn, the ML models leveraging them require updating as well. Therefore, to develop reliable software systems, every ML model specification should go through a code review phase and be versioned in a version control system, which makes the training of these models auditable and reproducible. As the underlying data evolves over time, the model version that is best suited to handle the data’s distribution can be automatically promoted without impacting production workflows.
Machine Learning development is a highly iterative and research-centric process. In contrast to traditional software development processes, in ML development, multiple prototypes can be executed in parallel before selecting the best model to be promoted into production. The most common way to track multiple experiments is to use different branches, managed in a version control system. Each branch is dedicated to a separate experiment and the output of each branch is a trained model. The trained ML models are compared with each other to determine which one is the best fit to solve the business problem. Libraries to track the weights and biases of a model can also be leveraged to automatically track the hyperparameters and metrics of the experiments.
The end-to-end development pipeline includes three essential components: a data preparation pipeline, a machine learning model pipeline, and an application pipeline. In accordance with this separation, three different scopes are distinguished for testing in ML systems, including tests for features and data, tests for model development, and tests for ML infrastructure. This ensures that testing ML training models include routines that verify that the algorithms making decisions are aligned with the business objectives and correlate with business impact metrics. Additionally, it also makes sure that the trained model includes up-to-date data and satisfies business requirements.
Once the ML model has been deployed, it needs to be monitored to assure that the ML model performs as expected and the underlying data follows the same patterns and distributions that the model was trained on. Monitoring can also include examining dependency changes within the pipeline, data version changes, changes in the source system, and upgrades in these dependencies. MLOps allows users to monitor ML performance through different metrics and assessment criteria such as the ML test score which assesses the quality of a model in production. Monitoring of changes in the underlying data can also trigger swapping of production models to ensure the best-fit model is informing business decisions.
The reproducibility in a Machine Learning workflow means that every phase of data preparation, ML model training, and ML model deployment produces identical results given the same input. This basically ensures uniformity in delivery and setting a production standard in ML workflows and is a critical element of the MLOps lifecycle.
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