Imagine a software solution that learns and adapts to your needs. That’s the power of intelligent applications fueled by AI. These smart apps go beyond basic functionality, using machine learning (ML), natural language processing and other cutting-edge AI techniques to personalize your experience, make predictions and automate operations.
The improvements that this kind of automation offers developers and, broadly, the developer experience, bring us to another innovative approach: platform engineering. Platform engineering is an emerging discipline that describes how software platforms are designed, developed and maintained to scale DevOps across multiple teams.
Naturally, businesses are clamoring to tap into this potential: By 2027, according to Gartner, 75% of organizations will have switched from multiple point solutions to DevOps platforms to streamline application delivery — up from 25% in 2023. Thus, many organizations are looking to integrate AI into platform engineering to accelerate time to market and improve the software development cycle and developer experience.
Let’s discuss the following:
- The rise of intelligent developer platforms.
- AI-augmented software development life cycle (SDLC) in platform engineering.
- Benefits and challenges of AI-enabled platform engineering.
The Rise of Intelligent Developer Platforms
According to Gartner Technology Adoption Roadmaps for IT Leaders, internal developer portals were cited as the most frequently piloted technology in 2022-2024 to improve the developer experience. Internal developer portals serve as the interface through which developers can discover and access internal developer platform capabilities.
While these portals provide self-service capabilities to improve developer experience, there is still an increasing demand for solutions that offer optimal efficiency, reliability, adaptability and cost reduction.
Many organizations have turned to platforms that leverage AI to streamline the development process for their IT operations. As a result, platform engineering teams now seek intelligent developer platforms that can automate tasks, provide AI-generated documentation, analyze code for errors and even generate code or development suggestions based on inputs.
These intelligent platforms promise a frictionless, self-service developer experience with as little overhead as possible, and are rapidly becoming the backbone of digital transformation.
AI-Augmented SDLC in Platform Engineering
Since AI and ML hit the technology space, the use of AI augmentation has seen a remarkable rise in several development tools and platforms, like GitHub’s Copilot, Google’s Bard and OpenAI. AI augmentation intertwines with the entire software development life cycle, from planning, designing, development, testing, deployment and continuous maintenance, to enable developers to create apps more effectively and quickly. Let’s look at a few examples.
AI-Powered Code Generation and Optimization
AI and ML platforms are augmenting every facet of the software development cycle, empowering developers to automate a broad range of tasks, boosting productivity, reducing costs and offering new growth opportunities. This is evident in AI-driven software development harnessing advanced large language models (LLMs) and ML algorithms, which show remarkable code generation, code review and testing capabilities to improve developer experience.
AI models trained with these deep learning techniques and massive datasets often act as personalized coding assistants and intelligent collaborators, providing insights, suggestions and automation to streamline development. This is beneficial to engineers who primarily concentrate on designing and building efficient and reliable platforms for developers and product engineers. Enabling AI to access the developer environment — source code, issue tracker and accessible documentation can help the platform team create a more compelling internal developer platform.
Intelligent Infrastructure Management
AI-augmented platforms will accelerate, scale, automate, and optimize SDLC and infrastructures at every level. For instance, AI-augmented platforms are now used to streamline how organizations optimize their computing, storage and network resources. From compute power to storage management and network optimization, they enable organizations to fine-tune their operations, enhance performance demands and navigate the complexities of modern IT infrastructure.
AI-Driven Data Integration and Analysis
AI and ML are transforming how data is prepared, managed and ingested by automating the data processes, introducing agility and efficiency in analyzing extensive datasets. Incorporating AI capabilities into data integration provides a ready solution by automating data preparation. AI systems can use natural language processing (NLP) to analyze data source descriptions, user queries and metadata to improve data discovery.
Personalized Developer Experience
AI-enabled platforms automate routine tasks and provide intelligent code suggestions, saving developers time and enhancing creativity and innovation. As a result, more developers are keen to harness AI to boost their coding efficiency and problem-solving abilities. This is evident in GitHub’s 2023 survey, which reveals the impact of AI on developer experience.
Benefits and Challenges of AI-Enabled Platform Engineering
Integrating AI into SDLC offers exciting possibilities for enhancing productivity and satisfaction for platform and developer teams. However, as with anything, there are cons as well.
Let’s see both sides.
Pros
- Since they easily analyze customer feedback, usage data and other relevant data sources, AI-augmented platforms enable businesses, platforms and developer teams to make decisions based on more accurate data.
- They provide real-time insights and performance monitoring for your applications, suggesting improvements to any issues that could occur.
- AI-augmented platforms automate certain tasks, such as data analysis and product testing, thereby reducing the need for human resources in the product development process.
- AI-enabled platforms improve discoverability and efficiency in the development environment, providing support for developers, platform engineers and managers.
Cons
It’s crucial to acknowledge that AI comes with certain limitations alongside these benefits. People in charge of implementing AI in platform engineering must have a deep understanding of the security issues that AI can introduce. Such issues can be inherently related to the quality and reliability of the source data that trained the model.
For example, if the model has been trained using code and patterns that were considered robust and secure and then a zero-day bug is found on that code, all the applications built with the help of that model are vulnerable.
Security issues can also be related to misuse of the AI. For example, a developer who is working on a new microservice built in a language they are less familiar with might ask the AI to translate a function from a different language without giving the AI the context and without knowing that the function is an antipattern for the target language.
To mitigate these risks, platform engineers have to:
- Build a solid monitoring system for security issues.
- Be ready to manage security problems when they occur.
- Continuously work to refine the source data, deleting and solving known issues.
- Train the AI according to new best practices and business needs.
- Create golden paths that prevent critical processes from being entirely managed by the AI without human supervision.
Wrapping up
Platform engineering and AI are new waves of technological innovation that can massively transform how developer teams work. They are driving a fundamental shift in the way organizations approach software delivery in today’s SDLC. Thus, many organizations are eager to jump on this industry trend to increase their competitive advantages and productivity, save money, and improve the developer experience.
If you are looking for the right solution to build your internal developer platform (IDP), bring order to your cloud native application and improve developer productivity, explore Mia-Platform.
The post Unleash AI in Platform Engineering to Streamline Software Delivery appeared first on The New Stack.
Platform engineering teams now seek intelligent platforms that can automate tasks such as documentation, find bugs and even make coding suggestions.