DevOps methodologies are rapidly increasing and generating vast and diverse data sets across the life cycle of entire application including development, deployment, and performance management. Only a robust analysis and monitoring layer can particularly harness this data for the ultimate DevOps goal that is end-to-end automation.

The rise of machine learning and its related capabilities, such as artificial intelligence and predictive analytics, has pushed organizations to explore implementing new analysis models that mainly rely on mathematical algorithms. The overall impact of these tools on data-driven automation is still limited due to busy teams of DevOps and a lack of practitioners who genuinely understand machine learning, AI, and predictive analysis.

The black box approach runs counter to conventional machine learning procedures and enables the analyst to adjust the algorithm iteratively until it becomes sufficiently accurate. Today, it is essential for DevOps engineers to know about the working of infrastructure, how to utilize DBaaS, and how to code in the cloud. Since most DevOps engineers are not mathematicians, adding machine learning algorithms to this skill set is not an easy thing.

Applying machine learning in DevOps

Despite the obstacles and challenges, the adoption of machine learning is only going to grow as high salaries push a number of IT engineers into this space. Although several DevOps vendors have added machine learning to their products, this does not exempt enterprises from the need to write their code in order to optimize their automation capabilities.

Many logs take up gigabytes of storage per week when there is too much data to manage. Most of the data generated in DevOps processes is on application deployment, server logs, and transaction traces results in application monitoring. The perfect way to analyze this large scale of data in real-time is to use machine learning. Let’s have a look at how machine learning enhances the practices of DevOps.

Look beyond the threshold

The DevOps teams analyze the entire data set as there is a plethora of data. They set thresholds for this reason as a condition for action. They primarily concentrate on outliners instead of focusing on substantial data chunks. Here, the problem exists as outliners usually provide indications but they don’t paint the detailed picture.

Learn from the history of data

DevOps teams do, occasionally, make mistakes. The professional originations of DevOps cannot resolve the problems encountered while they are in action. Machine learning systems can help them analyze the data and show what happened in recent time. It can verify from daily trends to monthly trends and provide a bird’s eye view of the application at any point in time.

Monitoring tools

Professional DevOps teams use more than one tool to view and act upon given data. Each specific device has its application monitoring ways on distinct grounds considering parameters like the health and performance of the application. These machine learning systems are capable of collecting inputs from all these tools and paint an integrated view.

Measuring orchestration

If your requirement is to measure the orchestration process adequately, then you can use machine learning to determine the team performance. Limitations may result due to reduced orchestration. Therefore, looking at these characteristics can help you with both tools and processes.

Looking for faults

It deals with patterns of the investigation. Developers need to be proactive about looking for faults. If you have realized that these systems deliver specific readings in the failure event, a machine learning application can search for the particular patterns of a specific kind of fault. In that case, if you comprehend the underlying cause of the failure, you can find a way to prevent it from happening.

Drilling down to the root cause

Providing groups with a chance to set right performance or availability issues bodes well for the quality of the application. Most often, teams don’t research failures completely since they center on getting back online as soon as possible. In case, a robot gets them running fine; the cause mostly gets lost. Be careful not to let this slide.

Conclusion

Without the advent of big data, AI and machine models would just remain as models and would have never been implemented. IoT and cloud computing have an inter-reliant relationship. Likewise, the real-time effectiveness of machine learning systems relies on the DevOps processes that provide agile software development. Hence, applying machine learning to DevOps enhances their capability to perform cloud-based operations more efficiently.

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DevOps is a way of thinking; it’s an approach, not a specific set of tools. And that’s all well and good – but it only gives you half the picture. If we overstate DevOps as a philosophy or a methodology, then it becomes too easy to forget that the toolchain is everything when it comes to DevOps. In fact, DevOps thinking forces you to think about your toolchain more than ever – when infrastructure becomes code, the way in which you manage it, change it is constantly.

Skills Up survey: Python is the primary language used by those working in DevOps

Because DevOps is an approach built for agility and for handling change, engineers need to embrace polyglotism. But there’s one language that’s coming out as a crucial component of the DevOps toolchain — Python. In this year’s Skill Up survey, publisher Packt found that Python was the primary language used by those working in DevOps. Indeed, it was a language that dominated across job roles – from web development to security to data science – a fact which underscores Python’s flexibility and adaptability. But it’s in DevOps that we can see Python’s true strengths. If DevOps is a modern, novel phenomenon in the software world, it’s significant that Python is the tool that DevOps practitioners share as a common language.

But why Python?

Clearly, flexibility plays an important role, but more specifically, it’s the accessibility of Python that explains its popularity in Packt’s research. This comes back to the increasing importance of polyglotism — if you’re working in a DevOps role, you need an adaptable skill set; Python is a language that forms a solid foundation for anyone curious about technology, committed to exploring new languages and tools; the fact that it isn’t a hugely taxing language to learn means it doesn’t require the level of commitment that a specialist language may need.

However, there’s a lot more to it than just accessibility – perhaps the key reason Packt found Python to be such a popular language for DevOps engineers is that it’s a great language for scripting – and scripting means automation. And, to go full circle, if DevOps is about anything at all, it’s ultimately about automating things and improving efficiency.

The fact that some of the key configuration management tools like Ansible and SaltStack are written in Python underscores just how useful the language is when it comes to infrastructure automation and orchestration.

It’s worth looking at how Python compares with a language like Ruby. The two are often compared, they’re both pretty accessible, and are both used in applications built by a large range of organizations. They’re also both languages that feature in the DevOps toolchain. There’s little to choose between them, and, by and large, you’ll be able to do many of the things you can do with Python with Ruby.

But it’s when you look at the syntax that you can begin to see why Python may be winning out – Python is much more direct than Ruby – as the piece above puts it:

Python takes a more direct approach to programming. It’s main goal is to make everything obvious to the programmer. This sacrifices some of the elegance that Ruby has but gives Python a big advantage when it comes to learning to code and debugging problems.

If you’re working in DevOps and agility is the aim of the game, this simplicity and directness is invaluable. In fact, some have commented on the decline of Ruby – which could go some way to suggest why Python is winning in the popularity stakes.

But this shouldn’t turn into a popularity contest – the key point for anyone, whether they work in DevOps or otherwise – is that you need to use the best tools for the job. It simply seems that Python is becoming the best tool for the job in a range of areas. It has clearly captured the software zeitgeist, bringing its syntactical zeal and can-do attitude to a huge range of problems.

So, as the toolchain opens out, with developers and engineers taking on decision-making responsibilities, Python might just be a stabilizing language. Because it can be used in so many different ways, it allows you to remain open to new technical possibilities. And what, really, is more valuable than adaptation when it comes to DevOps?

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