Machine Learning & AI: Future To Continuous Integration & Delivery
The progress in technology is making a better tomorrow for us! From the last decade, AI and Machine Learning (ML) is automating everything around us, and continuously deploying bigger and smarter processes.
It is true automation saves time, gives us progressive innovations and somewhere is a boon for human society. However, between all-new innovations and technologies, the failure of systems at the end-user side is still a concern.
Today’s modern custom software development company in USA and the rest of the world is incrementally enhancing the software features. We can see great consumer & enterprise software/applications whose deployment, integration, and delivery is once again revolutionizing the software industry.
The complete credit goes to the new development methodologies, Continous Integration (CI), and Continous Delivery (CD). Mainly, the CI/CD who has changed the process of automation, development & deployment phase to deliver safe, secure, and reliable software products.
Likewise, the DevOps (Development & Operations) has also become a standard in the industry. By encouraging the new culture of software working, DevOps emphasis more on software process, data automation, and error reduction.
“Continuous Integration & Delivery is all about this, which is being helped by Machine Learning AI.”
We use online banking services, online payment solutions, face recognition technologies, and many more. Also, the new AI-enabled cybersecurity solutions that keep our data secure at different online platforms.
Though AI & ML features have extended their capabilities, the challenging task to integrate them isn’t over yet! More importantly, deploying integrable AI + ML models has kept our feet on the toe.
The Challenge Is Open & To Deploy Model, We Would Need CD & CI;
Introduction To CD and CI
Continuous integration and Continuous Delivery are the processes where your DevOps team easily pushes a code in the main code branch without impacting any single changes made by those developers who are working parallelly to your team.
CI/CD is a process where one can change the codes frequently!
Continuous Integration: Definition
CI includes frequent integration of codes into a shared repository. These integrations happen daily. The automated test cases everyday build new code sequences, verify each change, and then send it to production.
“The main aim behind the automated testing is to make the code bug-free and error-free.”
Top 5 Benefits Of Continuous Integration:
- Early bug detection
- Reduces bug count
- Automating the process
- Transparent development process
- Cost-effective process
The continuous integration process is similar to the Azure Machine Learning service that is practiced by many Microsoft software development companies in USA.
“This process offers a Cloud-based environment to develop, test, deploy, train, manage, change, and track different AI + ML models.”
Continuous Delivery: Definition
CD, as the name suggests, is all about delivering the right product! Whatever the changes were made in continuous integration, continuous delivery leads them to production.
“Additionally, all the changes land at the end-user side quickly in a safe & secure manner. CD makes the deployment easy, predictable, planned, and scheduled.”
Top 5 Benefits Of Continuous Delivery:
- Reduce risks; make deployment easier and faster
- High-quality secure application
- Reduces testing, build and deployment cost
- High exploratory, usability, security, and performance
- Testing methodologies increase the product’s quality
Continuous Integration vs Continous Delivery
See AI and Machine Learning greatly take part in handling the data industry. When it comes to CD/CI, their model is all about giving the best software product with 0 error & 100% efficiency.
The changes made in the code during continuous integration before automated testing ensures that the new code is ready to move into the next phase. In other words, the changes are successful and code fulfills the needs of the main code branch.
Continuous delivery ensures that the codebase is in 100% deployable condition, successfully passed the automation tests, and will result in faster application development & deployment lifecycle.
Between integration and delivery, there is a phase of deployment or better say “the continuous deployment” that tells you to deploy your application to test environment and production at any time!
Every day the main codebase undergoes thousands of changes, but the companies prefer those changes which can continuously deliver new codes.
“The tech giant companies like Facebook, Youtube, Netflix, etc. are the best examples of releasing new codes every week.”
Machine Learning & AI: Future To CI & CD
Before understanding the importance of artificial intelligence and machine learning in the CI/CD process, there are several KPIs - key performance indicators related to software applications. We must understand them first! These KPIs are as follows
- Business: revenue, order volume, order throughput;
- Performance & Availability: response time, throughput, stalls, uptime;
- Resource: CPU, memory, I/O;
- Quality: events, exceptions, and errors.
Though the data of all these KPIs is for CD, it helps in increasing the quality of software products! Most of the applications have their own health check webpages, KPIs, and more!
However, what’s more, required is to use KPIs, Data, and Analytics during every production deployment. For this reason, Machine Learning is in use!
AI & machine learning in Python has proved their exceptional data handling capacities. While doing so in CI/CD, they identify regressions, failures, anomalies, and automatically verify the production deployment.
This feature of AI and ML is the reason why many top Python web development companies in USA prefer the combination of AI + ML + Python in the data industry!
How can AI/ML avoid CI/CD Errors?
Machine Learning is an advanced system that learns automatically by accessing data. ML is entirely focused on development & deployment. As a result, ML and AI enhance the power and ability to release.
“AI and machine learning acts more smartly and accurately and have become better tools & technologies. They self-learn with the data and need minimum human interventions.”
Yes, these so-called advanced technologies are integrable and extend the automation way far beyond its capabilities. Furthermore, the CD/CI improve the processes and performances of the final product or applications with zero errors.
Following are the two major benefits which AI and ML give to CI/CD:
- Regressive Performance: Once the deployment is done, testing your application and checking its performance is necessary. If you apply ML testing, then your efforts reduce to 85%.
- Regressive Quality: Though the on-time delivery of an application is necessary, one cannot forget quality maintenance. Machine learning easily picks on the errors and always gives you an updated error-free product.
Future Opportunities Of AI/ML For CI/CD!
Adopting both technologies ML & AI in Continuous Delivery and Continous Integration can help in developing more intelligent enterprise software. Automating every process in your organization enables you a more strategic business.
These technologies put you to the path of learning, implementing, and developing for continuous improvement. Many top mobile app development services providers in USA, follow this approach and are getting successful results in the form of agile and sustainable deliveries.
In addition to it, Machine learning and artificial intelligence are shifting the CI/CD process towards making a cross-functional collaborative structure that only delivers value.
With the support of these technologies and continuous improvement methodologies, you have the liberty to rethink towards organizing the whole business structure for better outcomes.
“Therefore, the CI/CD with the new data infrastructure level, AI and machine brings the culture of the Continous Intelligence for the future of the custom software development industry!”