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DevOps (Development Operations) and MLOps (Machine Learning Operations) are almost the same and share a wide variety of tools. As a DevOps engineer, you will deploy, maintain, and monitor applications, whereas as an MLOps engineer, you deploy, manage, and motor manufacturing models into production. So, it is beneficial to learn about DevOps tools as it opens a wide array of job opportunities for you. DevOps refers to a set of practices and tools designed to increase a company’s ability to deliver applications and services faster and more efficiently than traditional software development processes. 

In this blog, you will learn about essential and popular tools for versioning, CI/CD, testing, automation, containerization, workflow orchestration, cloud, IT management, and monitoring applications in production. 

1. Git

Git is the backbone of modern software development. It is a distributed version control tool that allows multiple developers to work on the same codebase without interfering with each other. Understanding Git is fundamental if you are getting started with software development. 

Learn about 14 Essential Git Commands for versioning and collaborating on data science projects.

2. GitHub Actions

GitHub Actions simplifies the automation of your software workflows, enabling you to build, test, and deploy your code directly from GitHub with just a few lines of code. As a core function of DevOps engineering, mastering Continuous Integration and Continuous Development (CI/CD) is crucial for success in the field. By learning to automate workflows, generate logs, and troubleshoot issues, you will significantly enhance your job prospects. 

Remember it is all about experience and portfolio in the operations related careers. 

Learn how to automate machine learning training and evaluation by following GitHub Actions For Machine Learning Beginners.

3. Selenium

Selenium is a powerful tool primarily used for automating web browser interactions, allowing you to efficiently test your web application. With just a few lines of code, you can harness the power of Selenium to control a web browser, simulate user interactions, and perform automated testing on your web application, ensuring its functionality, reliability, and performance.

4. Linux

Since many servers use Linux, understanding this operating system can be crucial. Linux commands and scripts form the foundation of many operations in the DevOps world, from basic file manipulation to automating the entire workflow. In fact, many seasoned developers rely heavily on Linux scripting, particularly Bash, to develop custom solutions for data loading, manipulation, automation, logging, and numerous other tasks.

Learn about the most commonly used Linux command by checking out Linux for Data Science cheat sheet.

5. Cloud Platforms

Familiarity with Cloud Platforms like AWS, Azure, or Google Cloud Platform is essential for landing a job in the industry. The majority of services and applications that we use every day are deployed on the Cloud.  

Cloud platforms offer services that can help you deploy, manage, and scale applications. By gaining expertise in Cloud platforms, you’ll be able to harness the power of scalability, flexibility, and cost-effectiveness, making you a highly sought-after professional in the job market.

Start the Beginner’s Guide to Cloud Computing and learn how cloud computing works, top cloud platforms, and applications.

6. Docker

Docker is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow a developer to package up an application with all of the parts it needs, such as libraries and other dependencies, and ship it all out as one package.

Learn more about Dockers by following the Docker Tutorial for Data Scientists.

7. Kubernetes

Kubernetes is a powerful container orchestration tool that automates the deployment, scaling, and management of containers across diverse environments. As a DevOps engineer, mastering Kubernetes is essential to efficiently scale, distribute, and manage containerized applications, ensuring high availability, reliability, and performance.

Read Kubernetes In Action: Second Edition book to learn about the essential tool for anyone deploying and managing cloud-native applications.

8. Prometheus

Prometheus is an open-source monitoring and alerting toolkit originally built at SoundCloud. It enables you to monitor a wide range of metrics and receive alerts in real-time, providing unparalleled insights into your system’s performance and health. By learning Prometheus, you will be able to identify issues quickly, optimize systems efficiency, and ensure high uptime and availability.

9. Terraform

Terraform, an open-source infrastructure as code (IaC) tool developed by HashiCorp, enables you to provision, manage, and version infrastructure resources across multiple cloud and on-premises environments with ease and precision. It supports a wide range of existing service providers, as well as custom in-house solutions, allowing you to create, modify, and track infrastructure changes safely, efficiently, and consistently.

10. Ansible

Ansible is a simple, yet powerful, IT automation engine that streamlines provisioning, configuration management, application deployment, orchestration, and a multitude of other IT processes. By automating repetitive tasks, deploying applications, and managing configurations across diverse environments – including cloud, on-premises, and hybrid infrastructures – Ansible empowers users to increase efficiency, reduce errors, and improve overall IT agility. 

Conclusion

Learning about these tools is just the starting point for your journey in the world of DevOps. Remember, DevOps is about more than just tools—it is about creating a culture that values collaboration, continuous improvement, and innovation. By mastering these tools, you will build a solid foundation for a successful career in DevOps. So, begin your journey today and take the first step towards a highly paid and exciting career.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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