Modern software development teams routinely use continuous integration (CI) and continuous deployment (CD). CI is concerned with how a project should be automatically and continuously built and tested in various runtimes. Continuous delivery is necessary to make it simple to push new code into production that passes automated testing.
Using CI/CD tools in ML projects can be extremely beneficial. These tools assist you in quickly locating errors and contradictions in code and, in the long run, reduce downtime costs. In real-world projects, it can be difficult for specialists to choose high-quality machine learning models consistent with enterprise project management schemes. As a result, deadlines are missed, and budgets are exceeded.
To complete everything by hand is excruciatingly painful. You need to set aside and maintain a separate server, make sure the necessary software systems are available, create runtime environments, create data backup copies, and so on.
Delegating these responsibilities to third-party services is convenient. Numerous tools are available to help with this adoption, many of which are tailored to machine learning engineers.
What Is the Definition of Continuous Delivery?
The concept of continuous delivery is founded on the concept of continuous integration (CI). Continuous integration is automating all contributors’ work into a single repository. Before the software can be released, it must first be built and tested by a team whose size is determined by the project’s scope.
This DevOps practice enables developers to frequently merge code changes into a centralized repository, from which builds and tests are run. Before integrating the new code, automated tools are used to validate its reliability. This is where machine learning algorithms play, allowing the team to test their commits in real-time and detect bugs, misbehaviors, potential exploits, and even confusing code that can be refactored.
Continuous delivery (CD) constantly updates our product in a production environment. It does not have to be agile in the strictest sense, but it is a departure from traditional waterfall release schedules. Continuous delivery, as opposed to a major release or slow and large updates, means pushing out your product as quickly as possible and delivering new content in small chunks.
What Is AI & Machine Learning Automation Testing?
AI and machine learning automation testing is the process of developing a test automation framework to automate software system testing. This is an essential feature for testing the quality of the software or its systems, and as such, high testing standards are required.
Software engineers perform automation testing with AI with little to no human input. The automation tester executes several test cases in an automation test environment to verify the functionality of software systems. These test scenarios are also known as tests. Selenium, JUnit, RSpec, and other tools can be used to run the test suite.
Automation testing necessitates interaction between automation testers and the application. A failed test can be handled in various ways, ranging from outright rejection to suggesting new solutions. Depending on the scope and scale of the project, test cases can be written in any number of languages.
Assuming a function returns an object from a list, the test discovers that passing a number greater than the list length results in an error. The automated tester discovers this is due to the developer’s failure to include an exception handler (a rookie mistake, but a common one). As a result, the author receives an automatic message explaining how to resolve the error.
AI and machine learning can be integrated into three key areas of our CI/CD pipeline:
- Creating tests for your application automatically. This can help ensure that your application is always up to date and following the most recent changes.
- Analyzing your application’s codebase and identifying areas that are likely to break in the future. This data can be used to prioritize which areas of the codebase should be thoroughly tested before each release.
- Monitoring the performance of your application in production and identifying potential issues early on. This can assist you in avoiding potential outages or poor performance for your users.
Best AI and Machine Learning Software Testing Tools
There are numerous AI and machine learning software testing tools on the market. However, choosing the best tool for continuous delivery pipelines takes time. Popular AI and machine learning software testing tools include:
- TensorFlow
- Keras
- Scikit-learn
- Microsoft Azure ML Studio
- Jenkins
- Bamboo
- GoCD
The Advantages of Using AI and Machine Learning in Your Business
There are numerous advantages to implementing AI and machine learning software testing tools in your company.
Among these advantages are the following:
Accuracy: AI and machine learning software testing tools can help you improve the accuracy of your test results. This is because these tools can assist in identifying errors and issues that human testers would otherwise miss.
Increased efficiency: AI and machine learning software testing tools can assist in automating repetitive tasks. This allows your testers to focus on more important tasks.
Quality improvement: Artificial intelligence and machine learning software testing tools can help you improve the quality of your software products. This is because these tools can detect errors and issues that human testers would otherwise overlook.
Cost savings: AI and ML software testing tools can help you save money on software development projects. This is because these tools can automate repetitive tasks, saving you money on labor costs.
What Comes Next?
Continuous delivery is releasing new content in small chunks rather than all at once. Continuous integration is the first step, which automates the process of integrating code changes into a central repository. AI-powered automated testing is used to ensure the software system’s functionality.
This AI must be trained using previous data, which takes time and effort. Still, once completed, it provides a very powerful and flexible model that can be repurposed for different projects or areas of the same project.
Artificial intelligence and machine learning software testing tools are here to stay. To build the AI, you will need software engineers and data scientists to collect the data, build the model, test it, and find it. This could imply growing your team or outsourcing your work. In any case, AI and machine learning are potent tools for businesses looking to up their game and optimize their pipelines.
