Mastering Model Retraining In Mlops Medium

Model retraining is a critical component of any robust MLOps stack, yet it is often overlooked. In this comprehensive guide, I’ll cover what model retraining is, why it’s needed, different retraining approaches, triggers, and best practices. Retraining refers to the process of creating a new model version by re-running the training pipeline on new data. This updates the model to reflect changes in the data over time. Without periodic retraining, model performance deteriorates due to: Retraining mitigates these effects by incorporating new data.

There are two core reasons to retrain machine learning models: In the dynamic landscape of machine learning (ML), models degrade over time due to concept drift, necessitating continuous learning. Automating model retraining is crucial for maintaining model performance and ensuring reliability in production environments. This tutorial guides readers through building an automated model retraining pipeline using MLOps practices, focusing on retraining triggers, monitoring, and deployment. Readers will gain hands-on experience with tools like MLflow, Prometheus, and Kubernetes. The pipeline includes data collection, model training, evaluation, deployment, and monitoring.

Automation uses CI/CD pipelines and cloud services. Use cloud-based training services like AWS SageMaker or Azure ML for scalability. Encrypt data and secure API endpoints with authentication. Only ~40% ML algorithms are deployed beyond the pilot stage.1 Such low rate of adoption can be explained with the lack of adaptation to new trends and developments such as economic circumstances, customer habits... Model retraining ensures adapting the ML models that are used in business decision-making as well since the predictive accuracy of deployed models also changes and degrades as incoming data changes. Explore what model retraining is and why you need to retrain your models:

Model retraining refers to updating a deployed machine learning model with new data. This can be done manually, or the process can be automated as part of the MLOps practices. Monitoring and automatically retraining an ML model is referred to as Continuous Training (CT) in MLOps. Model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters or variables used in the model. It adapts the model to the current data so that the existing parameters give more accurate and current outputs.

This allows businesses to efficiently monitor and continuously retrain their models, ensuring the most accurate predictions. When preparing your model for production in a machine learning operations (MLOps) solution, you need to design for retraining. Generally, there are two approaches to when you want to retrain a model: In either case, you need to design for retraining. To easily retrain your model, you should prepare your code for automation. Ideally, you should train models with scripts instead of notebooks.

Scripts are better suited for automation. You can add parameters to a script and change input parameters like the training data or hyperparameter values. When you parameterize your scripts, you can easily retrain the model on new data if needed. Another important thing to prepare your code is to host the code in a central repository. A repository refers to a location where all relevant files to a project are stored. With machine learning projects, Git-based repositories are ideal to achieve source control.

In general, it is important to clearly understand your business requirements and the problem you are trying to solve when determining the best approach to automate the retraining of an active machine learning model. It is also important to continuously monitor the performance of the model and make adjustments to the retraining cadence and metrics as needed. And this whole process can be deployed in 2 environments: Automating the retraining of a machine learning model can be a complex task, but there are some best practices that can help guide the design. The metrics used to trigger retraining will depend on the model and its usage. Each metric will need a threshold to trigger retraining when model performance falls below.

Some ideal metrics to trigger model retraining are: Copyright ©document.write(new Date().getFullYear()); All rights reserved pastenow.net

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