CI/CD for Machine Learning

For most software applications, Continuous Integration and Deployment (CI/CD) has been a popular procedure for a long time. Machine Learning systems may do the same thing, providing continuous and automated training and implementation of ML models.

Using CI CD for machine learning applications provides an end-to-end pipeline that closes the feedback loop at every stage and keeps ML models performing well. It can also connect engineering and science tasks, reducing friction between data, modeling, processing, and return.

What is CI/CD (Continuous Integration and Deployment)?

Continuous Integration and Deployment is a DevOps technique that allows you to produce code and quickly deploy it to production, whether it’s for a customer or an application. As a result, you’ll have a pipeline that is automated and that allows you to quickly write code and get it into production.

There are several phases to Continuous Integration and Deployment in software development: You begin with a product request and a design, then code it, create it, and run numerous tests. After the testing process is finished, the project will move from CI to CD. It starts with defining the update, then moves on to implementation, operations, and application monitoring in development.

MLops CI CD is a continuous process of reviewing, reassessing, identifying problems with the ML model, and then going back to change the machine learning model based on the new data. It automates the ML pipeline (building > testing > deploying) and eliminates the need for data scientists to participate in the process, making it more available to a broader audience and less vulnerable to human error, since the feedback loop ensures that the ML models’ accuracy and efficiency are constantly improved. This helps with monitoring machine learning models.

CI CD for ML

The main advantages of CI CD practices when creating a machine learning pipeline is its scalability. You can get away with not using activities like Continuous Integration and Deployment while operating on smaller scales, maybe only on a few versions. However, most companies that are implementing machine learning CI CD pipeline today need more complexity and scope. At the enterprise level, building models necessitates conducting hundreds of experiments at the same time, which becomes increasingly difficult to handle without a solid structure. This solid structure is often the source of secret technological debt and mismanaged DevOps issues. This is where Continuous Integration and Deployment activities come in handy, as they provide a basis for continuous improvement, ensuring that the machine learning models are still working in output, with improved model efficiency and accuracy, and you can see a steady improvement over time.

In a machine learning pipeline, Continuous Integration and Deployment practices automate the process of model preparation, model research, and model deployment, streamlining the CI CD workflow and allowing ML pipelines to operate at larger scales.

Data collection, data testing, resource management, and DevOps assistance in the form of large-scale compute resources are all part of machine learning pipelines. You must ensure that your models can produce production-ready, reliable results that will develop over time while using various forms of infrastructure, whether cloud or on-premise. Your models, on the other hand, are never permanent structures; they are constantly evolving in response to new data, as model decay necessitates the need to retrain your models. Continuous Integration and Deployment can be used to create a continuous feedback loop, ensuring that the models are up to date and correct without requiring your constant monitoring, interference, or attention.

Testing. CI/CD. Monitoring.

Because ML systems are more fragile than you think. All based on our open-source core.

Our GithubInstall Open SourceBook a Demo

Of course, data is the starting point for a machine learning pipeline. To ensure that your data is suitable, data validation and various data checks are needed. Then there’s model training, which involves experimenting with various algorithms to find the best match for the model. Additional model testing occurs before products are deployed to production. The deployment and prediction stages must be completed safely, and then a feedback loop must be established in order to have the data from your predictions validated in order to determine whether model retraining is necessary. An automated model may need to be retrained on a regular, hourly, or even minute-by-minute basis, depending on a number of factors. This retraining process is controlled through continuous integration, or CI, keeping in mind that the data may be subject to various regulations, such as GDPR, or other necessary constraints. Statistical tests and anomaly detection will help you keep a tighter grip on your Continuous Integration and Deployment pipeline, ensuring that your data is reliable and your models’ predictions are correct. Machine learning pipelines that are continuously refined and improved have a distinct advantage. Keep in mind that maintaining a reliable, production-ready pipeline necessitates testing and monitoring accuracy. Having an end-to-end platform reduces the need for comprehensive MLOps and data science friction, allowing for a fully automated Continuous Integration and Deployment ML pipeline to be implemented.

The model training phases of the machine learning pipeline add a lot of ambiguity. You’ll need a versatile tool to test a variety of algorithms and hyperparameters, and you’ll need to be able to predict adjustments and adapt your pipeline accordingly.