This article was originally published on The New Stack.
Recently, Platform Engineering has made waves within IT organizations, establishing new practices for DevOps and software development teams. Indeed, studies have predicted the mass adoption of this emerging technology approach in the coming years.
Platform engineering reshapes all aspects of the software development life cycle by addressing the complexities of IDEs, plugins, toolchains, repositories, environment creation and incompatibilities. AI will be no exception, introducing the benefits of automating and simplifying workflows for platform and developer teams.
Integrating machine learning (ML) and AI into DevOps and Platform Engineering has the potential to revolutionize software development and infrastructure management. By leveraging AI’s analytical prowess, platform engineers can optimize resource allocation, identify bottlenecks and pave the way for seamless scaling — all with unprecedented precision and speed.
Evolution of AI in Platform Engineering
While continual technological advancements have brought numerous advantages to organizations and developer teams, they have also introduced increasing complexity to the software development life cycle (SDLC). Developers who once primarily focused on coding suddenly became responsible for the entire SDLC.
Platform engineering emerged as the golden path to alleviate some of developers’ hardships, abstracting this complexity. It encompasses the well-defined, standardized methodologies developers adhere to within an Internal Developer Platform (IDP), ensuring a seamless developer experience and efficient operations.
AI has been introduced into Platform Engineering to enhance all these benefits and further empower developers. AI in Platform Engineering automates routine tasks such as managing code changes, testing software, complex integrations and handling security concerns. This frees up developers to focus on more creative and strategic work, enhancing developer experience and consequently reducing exponentially the cognitive load.
Features of AI-Powered Platforms
AI-powered platforms offer a variety of features that teams can use to streamline workflows and improve efficiency and application security. These include:
Improved Developer Experience
One of the main purposes of Platform Engineering is to improve the developer experience. An IDP powered by AI can provide developers with a pair programming tool, speeding up their work and enhancing the quality of their code. AI can also free developers from less valuable activities if associated with no-code interfaces and citizen developers that take care of such tasks. This allows for a more democratized and accelerated management of the entire SDLC.
Self-Service Provisioning and Configuration
AI algorithms can automatically analyze past usage patterns, real-time demands and resource availability to allocate resources like servers, storage and databases. AI-powered platforms can ensure reliable infrastructure, eliminating the need for manual configuration and provisioning and saving platform engineers valuable time and effort. Since these platforms have been trained on vast data models that enable them to understand individual developer needs and preferences, they can provide resources when necessary. As a result, they can be used to customize development environments and generate configurations with minimal manual effort.
Automated Security and Compliance
Organizations gather an increasing amount of data daily. As a result, businesses must handle and manage a large amount of data and personal information, ensuring it remains secure and protected. Now teams can reduce the risk of noncompliance and associated penalties by automating crucial processes like records management and ensuring that tasks are carried out in compliance with industry governance protocols and standards, a plus in high-regulated markets. AI-driven anomaly detection can help detect and prevent fraud by analyzing records and transactions and identifying and preventing security threats.
Growing Worries about AI in Platform Engineering
While AI holds immense potential to revolutionize Platform Engineering, its integration presents several challenges and raises concerns that demand careful consideration:
- Addressing potential biases and ethical concerns in AI-powered platforms: As AI capabilities evolve, ethical concerns regarding algorithm bias, fairness and possible misuse arise. The output will likely be biased if the data input is biased. However, developers, managers and tech leaders can prioritize fairness by actively working to ensure data sets used for training AI models are diverse and unbiased.
- Ensuring data privacy and security throughout the development life cycle: With the increasing amount of data going through organizations, there are rising concerns that AI could be used to fuel security breaches and expose private information. Integrating AI into platforms can further introduce new security vulnerabilities, which malicious actors could potentially exploit. Thus, robust security measures like penetration testing and vulnerability assessments have become critical in mitigating our platforms’ potential risks and unintended consequences.
- Balancing automation with human oversight and control: As AI systems become more sophisticated and integrated into various platforms, ensuring a harmonious relationship between automation and human involvement is crucial. However, the maturity and trustworthiness of this wave of technology are uncertain, causing some resistance among adopters and increasing concerns among users. Therefore, developers and platform engineers must determine how best to work with intelligent systems to derive the best results.
How Can Platform Engineers Maximize the Impact of AI?
As the trend of AI continues to grow, so does its role in Platform Engineering. Here’s how organizations and platform teams can maximize AI’s impact.
Democratization of AI Development
Since the advent of AI, development, implementation and usage have been limited to large organizations and tech giants with substantial resources such as Microsoft, Google and Apple. As a result, AI product development often requires specialized skills, including data science, machine learning and programming.
Organizations can spend time training and educating team members with limited AI expertise and incorporate more user-friendly AI platforms with a little learning curve. They can also provide developers with a library of pretrained AI models covering common tasks like infrastructure optimization, resource allocation prediction and anomaly detection.
Prioritizing AI Platform Security
It’s important to ensure trustworthiness and reliability in our data pipelines. This means implementing robust security measures throughout data collection, storage and processing to prevent breaches and unauthorized access. We can train our AI models using adversarial examples to make them more attack resistant. Organizations can also use techniques like input validation, model stacking and dropout layers to ensure that models are more reliable. This will invariably foster more trust in AI’s outputs and avoid biases or manipulation.
Future Outlook
Interest in Platform Engineering and AI are at an all-time high in 2024. According to Gartner’s prediction, by 2026, 80% of large software engineering organizations will establish Platform Engineering teams as internal providers of reusable services, components and tools for application delivery. With the growing demand for efficiency, improved productivity, and performance from developer and platform teams, the generative AI market is projected to grow from $11.3 billion in 2023 to $51.8 billion by 2028 at a compound annual growth rate of 35.6%, according to reports from Research and Markets.
We are witnessing the impact of AI on Platform Engineering and software development and how it shapes the future of these fields. The synergy between these technologies is a transformative shift in the software development life cycle that organizations and developers are eager to embrace.
