DevOps and MLOps have become important for ensuring seamless development, deployment, and management of machine learning and development workflows in today's software world. Open source plays a role in these domains by offering flexibility, cost-effectiveness, and fantastic community support.
DevOps is a combination of software development (Dev) and IT operations (Ops). It focuses on building workflows and monitoring the deployment of software projects to ensure they are fast, can accommodate users, and are ready for users anytime, any day. Meanwhile, MLOps or machine learning operations, is an extension of DevOps principles, but it is focused on machine learning projects. It focuses on learning, deploying, and maintaining models in a machine-learning system.
In this article, I’ll introduce you to 5 open source tools that will enhance your DevOps and MLOps practices in 2025.
Let’s get started! 🚀
KitOps - The missing link in your AI pipeline
KitOps combines different parts of your machine learning projects, such as code, datasets, configurations, and models, into a package called as ModelKit. This tool provides a lightweight solution to deploy your applications on platforms like Docker or Kubernetes.
KitOps works by bundling your AI or ML project into a ModelKit. The ModelKit can be pushed to a registry and shared with your team. They can easily pull the project to validate the datasets or deploy a model.
Here’s how a KitOps workflow looks
To use KitOps, you need to set up their CLI on your machine using this installation guide and confirm that it has been installed properly.
kit version
Using the kit init command, you can create a Kitfile in your current folder. This file will act as a blueprint for your AI project, defining your model, dataset, and dependencies. Once the Kitfile has been created and configured, use the kit build to build ModelKit based on the configuration in the Kitfile.
To push your ModelKit to the KitOps registry, use the kit push command.
kit push <registry_url>
To download (pull) your ModelKit from the registry, use the kit pull command.
kit pull <registry_url>
Why use KitOps?
✅ KitOps packages your entire project’s component into a one shareable asset for easy pulling or deployment.
✅ The ModelKits can be deployed on any cloud container with little to no stress.
✅ KitOps lets you write Python scripts that could help automate or manage your machine learning workflows. If Python is your main language of choice, it’s a no-brainer.
With KitOps, you don't need to stress over infrastructure challenges. It streamlines the process so you can focus more on your AI or ML project rather than its deployment workflow.
You can learn more about KitOps from their documentation and understand how it suits your use case.
This project has 607 stars on GitHub and is improving and simplifying machine learning workflows daily. you can join the community
Kubernetes - Your production-grade container orchestration tool
Kubernetes, often known as K8s, is a container orchestration platform built to automate project deployment into containers, scale them, and manage them. This tool was initially developed by Google, but it is now maintained by the Cloud Native Computing Foundations (CNCF). Over the years, K8s has become the standard for container orchestration in today’s cloud-native environments.
Kubernetes offers a solution to managing the difficulties of modern distributed systems. It lets developers focus on building the application rather than the complexity of deploying it.
To set up a Kubernetes cluster, you will need to create a YAML configuration file that defines where you want to deploy your application, which should look like this:
apiVerion: apps/v1
kind: Deployment
metadata:
name: my-app
spec:
replicas: 3
selector:
matchLabels:
app: my-app
template:
metadata:
labels:
app: my-app
spec:
containers:
- name: app-container
image: my-app-image:latest
ports:
- containerPort: 80
You can also monitor and manage your clusters through Kubernetes commands like kubectl get pods and kubectl get services, scale them, and troubleshoot them.
Some awesome features of Kubernetes:
✅ Kubernetes hides your application's infrastructure layer to make it portable across on-premises data centers, cloud providers, and hybrid environments. For example, if you have an application for a Kubernetes cluster in on-premises data centers, you can easily deploy the application to AWS, Azure, or Google Cloud because Kubernetes handles the differences in networking and storage behind the scenes.
✅ Kubernetes has a self-healing mechanism. When pods or services fail, it restarts or moves the workloads in the crashed pod to a healthy one that can accommodate the load. This makes your container run smoothly without any manual bug fixing and saves you that ‘Friday prod crash moment’.
Kubernetes is a powerful yet versatile tool for managing workloads in containers within cloud environments. It’s an important tool in a DevOps or MLOps toolkit.
You can learn more about K8s in its documentation and how it can help your deployment journey. This project currently has 112k stars on GitHub, and they offer training, which you can find here.
Pulumi - Automate, secure, and manage everything in your cloud
Pulumi is a cloud platform that lets developers and infrastructure (SRE) teams define, deploy, maintain, and manage cloud resources using your favorite programming languages like .NET, Python, Go, C#, and Java.
To use Pulumi, you need to download and install the CLI tool and use the pulumi new to start a new project and define your cloud resources using a language of your choice. For example, you can define an S3 AWS bucket in Python like this:
import pulumi
from pulumi_aws import s3
bucket = s3.Bucket('my-bucket')
Also, you can review your changes using the pulumi preview and apply said changes to the cloud by using pulumi up
The best parts of Pulumi
✅ Pulumi lets you manage cloud resources across multiple providers such as AWS, Azure, Google Cloud, and Kubernetes.
✅ Pulumi has a feature called “Pulumi insights” that uses AI to improve security, compliance, and cloud infrastructure.
✅ Pulumi manages your secret data securely with built-in secret management encryption.
Pulumi’s infrastructure as code (IaC) approach could streamline cloud resource management, making it a great alternative to traditional IaC tools like Ansible or Terraform.
You can learn more about Pulumi through its documentation and how it fits your product’s cloud requirements. The project currently has 22k+ stars on GitHub, and they offer platform engineering workshops.
Dagger - Build your pipelines in minutes
Dagger makes writing CI/CD (Continuous Integration and Continuous Deployment) pipelines easy. It is built to improve or enhance software development pipelines by letting developers create them in languages like Go, TypeScript, and Python.
Dagger offers a solution that replaces the need to write complex CI (Continuous Integration) scripts when building pipelines for software projects. To get started, install an SDK in your preferred programming language, which provides access to Dagger functions for designing and managing pipelines. A key advantage of Dagger is the ability to test pipelines locally, allowing you to identify and resolve issues before deployment. This helps reduce debugging time in production. Once validated, Dagger enables seamless deployment of your pipeline across various CI systems, including Kubernetes, Podman, and OpenShift.
Some cool features of Dagger
✅ Dagger offers an interactive terminal interface that lets you debug once your pipeline fails or stops abruptly. This makes fixing pipeline failures much easier.
✅ Dagger has an amazing caching system that helps you avoid unnecessary rebuilds, reruns, and retests, making the CI/CS process much faster.
Dagger takes a unique approach to creating CI/CD pipelines. It is also super-efficient, reliable, and easy to maintain. You can learn more about Dagger through its documentation.
Currently, Dagger has 11k+ stars on GitHub, and they have Daggerverse, which has Dagger functions that you can use to enhance your DevOps experience.
Jenkins - The leading open source automation server
Jenkins is an automation server that improves the process of continuous integration and continuous delivery (CI/CD) of software projects. It works by automating the stages of the software development life cycle (SDLC) - building, testing, and deploying software applications by improving how fast and reliable the CI/CD processes are.
Jenkins is a Java-based platform that is compatible with any operating system, including Windows, Unix-based systems, MacOS, and Linux. Therefore, to use it, you need Java installed on your machine. You can install Jenkins using the installation documentation and configure the system settings and security options. Jenkins has many plugins that allow you to integrate it with many version control systems, build tools, and cloud platforms.
What makes Jenkins Different?
Besides being the leading and most used automation server with so many plugins, here are some unique features that make this tool stand out:
✅ Jenkins lets developers create pipelines as code. Using code, you can define pipelines, share the code for reviews, set up versioning, and collaborate in the CI/CD processes stage of your building.
✅ Jenkins supports distributed builds across multiple machines, which improves performance and accommodates your project’s growing technical requirements. It evenly distributes the workload across machines to prevent crashes.
Jenkins is such a useful tool in any developer’s or DevOps toolkit because it helps you achieve a streamlined, automated CI/CD process and improves the quality and speed of the software.
The project currently has 23k+ stars on GitHub and has opportunities for you to join their online or in-person meetups and geek out on DevOps and cloud.
And that’s a wrap!
These are 5 open source tools for DevOps and MLOps workflows you should definitely know. The tools covered in this article each have their unique strengths, which can boost your DevOps and MLOps journey, whether you are a beginner or a pro in this field.
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Got other awesome open-source projects in mind? Drop them in the comments!
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Top comments (10)
Great List!
Thanks for checking this out!
Thanks for including KitOps!
KitOps is really awesome🫡
Awesome list
thankyou
Great article 🔥
Thankyou Sophia!
Awesome Tools
You forgot mlflow.