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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, while as an MLOps engineer, you will deploy, manage, and put manufacturing models into production. Therefore, it is beneficial to learn about DevOps tools as it opens up a wide range of career opportunities for you. DevOps refers to a set of practices and tools designed to increase a company's ability to deliver applications and services more quickly and efficiently than traditional software development processes.
In this blog, you will learn about essential and popular tools for version control, CI/CD, testing, automation, containerization, workflow orchestration, cloud, IT management, and application monitoring 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 code base without interfering with each other. Understanding Git is essential if you are getting started with software development.
Learn 14 essential Git commands for version control and collaboration on data science projects.
2. GitHub Actions
GitHub Actions makes it simple to automate your software workflows, allowing you to create, 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 to success in the field. By learning how to automate workflows, generate logs, and troubleshoot problems, you will significantly improve your job prospects.
Remember it's all about experience and portfolio in operations-related careers.
Learn how to automate machine learning training and testing by following GitHub Actions for Machine Learning Beginners.
3. selenium
Selenium is a powerful tool that is mainly used to automate 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 basis of many operations in the DevOps world, from basic file manipulation to automating the entire workflow. In fact, many experienced developers rely heavily on Linux scripts, particularly Bash, to develop custom solutions for data loading, manipulation, automation, logging, and many other tasks.
Learn about the most commonly used Linux command by checking out the Linux for Data Science cheat sheet.
5. Cloud platforms
Familiarity with cloud platforms such as AWS, Azure or Google Cloud Platform is essential to landing a job in the industry. Most of the services and applications that we use daily are deployed in the Cloud.
Cloud platforms offer services that can help you deploy, manage, and scale applications. By gaining experience in cloud platforms, you will 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, major cloud platforms and applications.
6. coupler
Docker is a tool designed to make it easier to create, deploy, and run applications using containers. Containers allow a developer to package an application with all the parts he needs, such as libraries and other dependencies, and ship it all as a single 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, deploy, and manage containerized applications, ensuring high availability, reliability, and performance.
Read the book Kubernetes in Action: Second Edition to learn the essential tool for anyone deploying and managing cloud-native applications.
8. Prometheus
Prometheus is an open source monitoring and alerting toolset originally built on SoundCloud. It allows you to monitor a wide range of metrics and receive alerts in real time, providing unparalleled insights into the performance and health of your system. By learning Prometheus, you will be able to quickly identify problems, optimize system efficiency, and ensure high uptime and availability.
9. Terraform
Terraform, an open source infrastructure as code (IaC) tool developed by HashiCorp, allows you to provision, manage, and version infrastructure resources across multiple on-premises and cloud environments with ease and precision. It supports a wide range of existing service providers as well as custom internal solutions, allowing you to create, modify and track infrastructure changes securely, 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 hybrid, on-premises, and cloud infrastructures, Ansible enables users to increase efficiency, reduce errors, and improve overall IT agility.
Conclusion
Learning about these tools is just the starting point of your journey in the world of DevOps. Remember, DevOps is about more than just tools – it's about creating a culture that values collaboration, continuous improvement, and innovation. By mastering these tools, you'll build a solid foundation for a successful career in DevOps. So, start your journey today and take the first step towards an exciting and highly paying career.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
