AI has shown its potential to disrupt industries and functionalities in today’s world. Specifically, AI in DevOps is slowly on the rise. AI is considered one of the driving forces when it comes to accelerating DevOps efficiency. By leveraging AI to its fullest capacity, DevOps teams can see a clear difference in their performance, thus improving operation cycles to create a compelling customer experience. DevOps also benefits from automation as it frees the team from performing mundane tasks and redirects their time to reach maximum efficiency. Naturally, AI/ML is the only way to streamline otherwise time-consuming processes and is prone to human error. Startups from around the world have started introducing products and tools that can be used to improve the efficiency and reliability of software development while improving the time DevOps teams spend on handling repetitive tasks during deployment. AI has increased data accessibility, enhancing teamwork and efficiency within DevOps teams. With AI supporting DevOps teams, companies can build products faster and cheaper.
Broadly, AI in DevOps can be used for testing, code reviews, code quality, performance monitoring, and application security. For instance, AI-enabled DevOps tools are currently used as chatbots, virtual assistants, and AI-enabled monitoring and testing tools. As AI promises to offer a better customer experience, there is a lot of scope for upcoming startups to create pathbreaking tools exclusively for DevOps teams. Today, there are several companies that are creating path-breaking solutions that are loaded with AI capabilities aimed to simplify some of the workloads the DevOps team is facing.
Here is a list of startups that are out to simplify DevOps through AI.
Kubiya is a unique DevOps tool that was recently launched out of stealth mode. Developed for intent-based operations, Kubiya can be used to turn conversations into operations. The chatbot draws leverages conversational AI to become an interactive, personalized assistant for DevOps teams. Developers can raise requests with the assistant and can be assured of having the task performed without much back-and-forth. The SaaS model can be used at individual, team, or organizational levels. On the platform operator side, Kubiya uses AI again to enable platform operators to create workflows that spin up cloud resources in minutes. Using this, platform operators can save hours and hours of manual tasks in a day. Kubiya’s AI is smart enough to understand complex requirements and spin up cloud resources with fine-grained configuration.
Disconnected development teams are often left with multiple release pipelines and delivery practices. Launched in 2020 as a collection of individual products, the Digital.ai DevOps Platform combines Agile planning with software delivery and end-to-end intelligence. The platform is used to scale software development teams and can be used to unify, secure, and generate predictive insights across the entire software lifecycle to improve business value.
3. iTuring MLOps from CyborgIntell
CyborgIntell is an enterprise AI software company from India that offers various AI-centric products. One of their products is iTuring MLOps. The solution is developed to work with CyborgIntell’s DevOps platform to allow businesses to operationalize AI models for their business. The platform can be used to deploy any ML model to the production environment. iTuring MLOps can be used to monitor model health, score transactions, connect data and predictive load models, and review and optimize results.
4. MLEM from Iterative
MLOps platform Iterative launched MLEM, an open-source Git-based machine learning model management and deployment tool to bridge the gap between ML and DevOps teams. The tool can be used to extract meta-information for ML models and simplify deployment. Since the current SaaS solutions create a divergence, the tool facilitates sharing of models between business units and teams, thus creating a single source of truth that can be used as modular building blocks to store and track ML models throughout the development lifecycle. The tool can be easily integrated into a company’s existing MLOps tech stack.
Founded in 2015, CloudFabrix focuses on creating autonomous enterprises. To that end, CloudFabrix offers Robotic Data Automation Fabric (RDAF) DevOps Solution Pack that can be used to automate everyday DevOps tasks. With easy integrations available for Kubernetes, Bitbucket, Docker, TeamCity, Red Hat, and Jenkins, RDAF can be used to improve business agility, customer satisfaction, and ROI.
Anyscale Inc launched Ray, an open-source framework for distributed ML that offers a unified compute platform to simplify the need for several compute resources. The platform facilitates users to scale compute-intensive ML workloads while reducing effort through native libraries. With the help of AI, the platform allows developers to scale and build everything without the need for multiple resources. This open-source project is primarily for the Python programming language.
Modular is focused on developing a next-generation AI developer platform that collates AI frameworks front-ends and improves portability and access to various cloud environments and hardware backends. Started in 2022, the aim of the platform is to create a developer workflow that results in quicker time-to-market for customers.
Slim.AI platform aims to give control to developers with Slim tools to develop containers and push them into production to ensure the DevOps team can perform their tasks seamlessly. Slim.AI understands the various shortcomings in container security and offers a platform with a developer-first approach. The platform can be run on existing software without any additional code, thus giving developers time to build applications instead of learning a new ecosystem. Integrating the platform into the development workflow ensures developers can access various tools and insights to automate container processes.
Future of AI in DevOps
AI in DevOps has been genuinely beneficial as it helps automate repeatable processes, create workflow optimization, monitor system performance, improve customer engagement, and remain cost-effective. However, before implementing AI in DevOps, it is imperative to consider various parameters, including the quality of data available, access to new and updated data at all times, AI-based decision-making processes within the organization, and the ethical issues surrounding AI.