Python for DevOps

Python is a popular, approachable programming language. It’s easy both to write your own Python code and to understand the code other people have written. Python is open source and the Python community has developed a large number of tools, libraries, frameworks and support groups. Even though Python is a great language for people just learning to program, it’s not a toy. Thanks to its well-designed syntax, modularity, support, and ecosystem, it’s easy to scale up from simple projects to complex ones. It is common to use Python for DevOps to tackle everything from web scraping to machine learning to managing infrastructure in the cloud.

Python for DevOps and Infrastructure Automation

One of the primary uses of Python is to automate tasks that are being done manually. Automation is a core tenet of DevOps, so you’ll find that many DevOps practitioners use Python to run automated scripts for a variety of tasks such as triggering the CI/CD pipeline on specific events, configuring cloud infrastructure. In fact, you can do almost anything in DevOps using the Python programming language. A few other common examples of using Python for DevOps are creating automation tools to check on servers, creating tools to gather application statistics, and creating tools to check on network devices.

Python for MLOps

Along with automation, Python has proven to be very popular with data scientists. The reasons we’ve already mentioned for Python’s general popularity apply here, of course. Another reason is the development of a process that has many similarities with DevOps. That process is called MLOps and, as you can guess from the name, it’s a process that applies DevOps practices, such as continuous delivery, to machine learning models.

DevOps practitioners aim to efficiently write, deploy and run enterprise applications. They break down silos between software developers and IT operations teams. DevOps emphasizes collaboration and communication among team members.

MLOps is similar. Along with the data scientists who curate datasets, build AI models and analyze them, there are also ML engineers who complete the deployment setup and, along with the data scientists, monitor the models. MLOps means doing whatever is necessary to make the whole ML build-deploy-monitor lifecycle as smooth and as safe as possible.

Python is an ideal language for MLOps because it’s popular not just with data scientists but with engineers who deal with cloud infrastructure. A common language means that scientists and engineers can work together to get the best results.

Benefits of Using Python for DevOps

We’ve talked about how well-suited Python is for MLOps but, of course, it’s been very popular with DevOps engineers for years. The IEEE Spectrum’s sixth annual interactive ranking of the top programming languages puts Python firmly on top. This popularity is driven in no small part by the vast number of specialized libraries available for it, which makes Python suitable for all sorts of applications. For example, in the domain of artificial intelligence, the Keras library is a top choice among deep-learning developers. Keras provides an interface to the TensorFlow, CNTK, and Theano deep-learning frameworks and tool kits.

Python is also known for its easy-to-remember and direct syntax, which makes it easy for the developer to build fast. Python is a great language for scripting, deployment automation, and web development. This makes it one of the most suitable languages for DevOps. Whether you’re in web development, want to work in data science, or simply want to enhance the backend of an existing app, Python is the language to use.

Choose the Right Platform to Learn Python for DevOps

Whether you’re already an experienced ops practitioner or a data scientist who’s learning about cloud infrastructure, the right platform can help you get the most out of your Python skills for deploying and managing cloud infrastructure with DevOps. Look for a platform that:

• Lets you use your existing Python skills and development tools to deploy and manage cloud infrastructure as code.
• Use software development tools such as IDEs like PyCharm, package managers like PyPI, and Python test frameworks.
• Test and deploy cloud applications and infrastructure together through CI/CD pipelines, with integrations for your existing CI/CD tools
• Use any cloud provider you choose.

The right platform means you don’t need to learn domain-specific languages that are tied to a particular cloud provider. It will help you provision and manage your infrastructure and it will provide features such as state management and concurrency management. That’s where tools like Pulumi come in – allowing you to manage infrastructure with languages and constructs you already know.

Pulumi Corporation

Pulumi’s Cloud Engineering Platform unites infrastructure teams, application developers, and compliance teams around a unified software engineering process for delivering modern cloud applications faster and speeding innovation. Pulumi’s open-source tools help infrastructure teams tame the cloud’s complexity with Universal Infrastructure-as-Code using the world’s most popular programming languages and communities, including Python, Node.js (JavaScript, TypeScript), Go, and .NET (C#, F#, VB). Get started for free today or check out our on-demand workshops and tutorials for getting started with IaC and Python.