Do you think a machine learning project is a brand-new kind of project in the software development realm? Do you believe a machine learning project generally has distinct hurdles compared to a standard software project? And lastly, do you feel that the above two questions are more relevant when it comes to applying DevOps or MLOps to a machine-learning project?
If all three answers from your side are yes, then relax, you are thinking right :)
Let’s brush up few simple concepts before diving deep into technology.
What is Continuous Delivery?
Continuous Delivery(CD), a software engineering strategy, automatically prepares code changes for production deployment. It comes under DevOps or more specifically MLOps.
In order to make system changes more manageable, continuous delivery, a method of software development, has software developers who share a code base, integrate their code into the main branch, and release their product on a frequent basis.
When done correctly, it ensures better software quality and steadily increases productivity. As a result, software product development is focused on continuous delivery.
A related concept is Continuous Integration(CI). Software engineers who use continuous integration constantly merge their code change into a common repository, followed by automated builds and testing. Continuous integration’s main objectives are to detect and fix issues more quickly, enhance software quality, and shorten the time it takes to validate and publish new software upgrades.
Continuous delivery expands on continuous integration by distributing all code alterations to a testing or production environment following the build process.
What is Continuous Delivery Pipeline?
A continuous delivery pipeline is a sequence of steps that must be followed to deliver a new software version with better accuracy, reliability, and sustainability. It has mainly four components described below:
Source Code — To start the continuous delivery pipeline process, code changes are pushed to a source code repository.
Building — The source code is created into packages and executables along with any dependencies.
Testing — Automated tests are conducted to verify that the code works as intended; this serves as a safety net to ensure that issues are rapidly found before the code is pushed to production.
Deployment — When there is an executable instance of the software that has successfully passed all the automated tests, it is ready to be deployed to production.
We have now enough background to discuss the challenges in implementing continuous delivery in the context of machine learning projects.
Challenges in Implementing Continous Delivery for ML Projects
As a machine learning project is different than other common software projects; a few common challenges faced by machine learning projects from the continuous delivery perspective as below:
- Reproducibility challenge: Machine learning projects are more experimental than traditional software projects like web applications or mobile apps. To ensure that you can reuse the code and duplicate the model’s performance on the same dataset, the problem in this situation is tracking and maintaining the reproducibility of these tests.
- Testing challenges: Compared to other software systems, an ML system has more operationally complicated regions because it incorporates data, models, and code. To ensure that the ML model performs satisfactorily on a holdout test set, you must test and validate the data and models in addition to the usual unit and integration tests.
- Deployment challenge: The deployment of a multi-step pipeline that automatically retrains and launches a different service into production is also necessary to deploy machine learning projects. This pipeline makes the continuous delivery process more complex by requiring you to automate steps for training and validating new models before deployment.
Having discussed the common challenges, let us discuss how these challenges can be solved.
Implementing Continuous Delivery for an ML Project
- Reproducibility solution: Open-source data versioning tools can remedy the issue immediately. These tools keep account of the commands used to run the machine learning pipeline and the dependency graph, making it possible to replicate the procedure in different settings. Therefore, we can reproduce the same model. For example, we can use an open-source Data Science Version Control tool to execute the model training procedure.
- Testing solution: Various testing methods can be incorporated into the machine learning workflow. Several different automated test types can add value and raise the general quality of your machine learning system, even though some components are inherently non-deterministic and difficult to automate. For example, despite the non-deterministic nature of ML model performance, Data Scientists typically gather and track various metrics to assess a model’s performance, including accuracy, confusion matrix, precision, recall, etcetera. For example, an open-source Python framework called DeepChecks is used to verify ML Models and Data. Users may essentially test the ML pipeline in various different stages.
- Deployment solution: As the scale increases and we construct a machine-learning solution, things can quickly get complicated and unorganized. Tools that can manage complicated workflows and stop chaos and complexity are necessary to automate the delivery pipeline. For example, a Python tool called Luigi makes it easier to create intricate batch task pipelines. It manages workflow management, visualization, handling errors, command line integration, and many other things.
We mainly discussed a few challenges and their possible solutions in implementing continuous delivery for machine learning projects. However, please note that there are many other solutions to these challenges and that there might be many other challenges.
It takes more than just deploying a machine learning model for prediction to implement machine learning in a production context. By setting up a continuous delivery system for machine learning, you can quickly iterate based on changes in your data and business environments and automatically develop, test, and release new machine learning pipeline implementations.
Happy Learning :)