Organisations are looking to AI models to bring out digital transformation in business. But, understanding which kind of models are most suited to the business needs is crucial

As enterprises are discovering the benefits of artificial intelligence (AI), they realise the journey to AI is long and bumpy. Many CIOs (chief information officers) want AI to quickly transform their business without identifying which processes will perform better with AI. In an ideal world, one can pick any process, infuse it with AI and then discover the pros and cons during the journey. The best way to learn is on projects rather than researching through theoretical case studies.

But there needs to be some order to the madness. Can we generalise some patterns that could make it easy for business owners to apply AI? Let’s discuss some scalable, enterprise-relevant AI patterns.

Discover new processes: This is about finding new opportunities afforded by AI. Consider the example of a defect in a machine, degrading over time. An experienced mechanical engineer can deduct the condition of the machine from the sound generated. What if there was a process wherein the mechanical engineer documents what he ‘hears’ and how he maintains the machine? This is where an acoustic AI model can be created, which can analyse sound samples of the machine to predict failures. It’s common sense for an engineer that a noisy machine is the first sign of mechanical failure; shouldn’t this important data can be put to value?

Imagine driving down a highway and an alert pops up on the dashboard saying, “Possible less lubricant”. It confirms the driver’s gut feeling that there’s something wrong in the car.

Most of the acoustic models today use humans to classify the data fed into the model; over time, the model learns to classify on its own. For example, we need to gather at least 10,000 sound clips of failed and normal ball bearings to classify the anomalous sounds and detect the issue. Not an easy task, but it can tremendously help in predicting failures in mines, underground subways, nuclear plants and highly critical sites unapproachable by humans.

Reinvigorate old processes: This pattern improves existing processes by introducing AI. For instance, almost every organisation collects data from the employees’ badges, which provides information on access control and employee movement. Reinvigorating this process by adding occupancy sensors and then adding AI will help derive deeper insights such as the number of people per floor. This data can be fed into an AI model to predict the occupancy rate and help organisations reduce the cost associated with each desk and decide how much office space should be leased or vacated. The ability to predict churn of clients, machine failure, energy usage etc are all examples of how old processes can be reinvigorated with new AI models.

Unlock data: Organisations can derive value from their data by applying AI. For example, machine learning algorithms can be used to detect fraud in financial transactions or even an asset defect, which would otherwise go unnoticed by humans. One Machine learning model can be fed time-series data to discover patterns of anomalies, while another can be fed asset manuals to find contextual text of the faults. One of the widely used examples of applying AI to businesses is handling unstructured data in the form of texts, videos, and tweets. Several organisations across industries have benefitted from this pattern, including telcos with millions of call records, banks with loan records, manufacturing units with work orders etc.

Opening new channels: This is another area where we have seen several businesses apply AI successfully. This essentially means starting a new channel of interaction with customers or employees using AI-based virtual assistants with natural language processing technologies. Unlike the dated IVR system, this new channel is helping organisations reach their clients and service them in unique ways.

We can pick any AI process and it’s sure to fall in one of the four patterns mentioned above. What is needed is the right understanding of which process to choose and then applying the right AI methodology to solve the business problem. Once the process is chosen, along with the the right algorithm and data quality, one also needs to check for bias in the model. Explanation of why a certain recommendation is the most right one needs to be included too.

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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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