The world of technology is always changing – but changes have pretty much stormed in during the past year or so, for obvious reasons. Cloud support services have nonetheless been at the forefront to establish business continuity, even for the smallest of players. IT offshoring in select destinations (such as software development in Sri Lanka, for example) has functioned like clockwork even during the most trying of times, thanks to ever-pouring requests and demands to digitize user journeys and other business objectives.
Owing to an overhaul in business processes, technology also has been promptly influenced, motivating it to shift gears so that what is crucial for consumers, can be met. Contactless technology is in, and so is remote work. These may be advancements in and of themselves, but they both signify a strong sense of autonomy; people now have the liberty to live, work and study according to their own preferences and schedules more than ever before. Add to this the fact that consumers are always rendered spoilt for choice in a digital world that is so abundant with options – if something doesn’t seem right even in the slightest, users aren’t tentative to jump ship and pursue another, more relevant product or service.
From building customer-centric digital products to reprioritizing business goals, much is abuzz in the world where technology and business converge. The heightened sense of autonomy brings us to the core of this article’s topic, which is AI and machine learning. With increasingly longer and complex user journeys (which also need to be embedded with a contactless functionality) nothing is possible without a system that can think for itself – and learn as it goes. AI and machine learning are therefore the answer to this problem, as technologies have been improved to be more autonomous than they were ever before.
Chatbots are a prime example, as queries can be addressed without any interventions from a human agent. While this frees employees to focus on more complex queries, it also speeds up resolution times for enhanced customer service. Content suggestions when composing email is also a key trend, as leading email providers help to write accurate and relevant email copy when words are typed in. How about AI-based content generation? Simply insert a paragraph or two and watch it get paraphrased within a matter of minutes (if not seconds) so that all your content is unique. The same concept also applies to automatically generating code, simply by scanning a dataset and understanding what the objectives should be.
These are just some examples, of course. While the manufacturing industry has long since been at the forefront of adopting AI, other industries are also jumping into the bandwagon. Going beyond the conventional assembly line, the use cases for AI have now spread as far as healthcare and education, including the media. There’s no sector AI hasn’t touched today, and it’s a technology that is bound to keep growing – with this year also being no exception to this rule. While trends surrounding AI are many, here are some of the biggest.
MLOps is a conglomeration of machine learning and deployment, where both specialties come together to develop applications, deploy them and then keep iterating for continuous delivery. In other words, it’s the same as DevOps – but specifically aimed at machine learning. As more companies focus towards adopting applications that are powered by AI and machine learning, there is a growing need to also build such applications from scratch. An application of this calibre needs its own development pipeline and process, hence the advent of MLOps.
The existing Software Development Lifecycle (SDLC) isn’t going to suffice, which is why MLOps has been devised to cater to the unique requirements of building a machine learning application. Unlike the SDLC, the chain of processes involved in MLOps is more erred towards data, and will characteristically begin by determining what the final business objectives are. From that point onwards, data needs an architecture, along with full-scale engineering before it is ready for modelling. The architecture stage needs to ensure that the correct data sets are sourced, while ensuring data is accurate and compliant (where applicable).
Thereafter, data engineering needs to ‘clean up’ this data as and how required, so it is ready to be fed into the training module. Any configurations on an administrative level are best addressed over here, such as cloud services required to host and process data. Establishing dedicated business partnerships with leading cloud providers (such as being an official AWS partner, for example) gives companies full-scale access to all services. This way, companies can rely on a single provider for all their needs, no matter how bespoke they are.
After all this is done, then comes the fun part i.e. training the model! At this point, feeding all the data gathered will reap outcomes which, depending on quality, will require the code to be reiterated for further enhancement. This way, the model is ‘trained’ to perform better. So when it is eventually deployed, it can mimic its self-learning capabilities with the variety of data that it encounters.
All in all, MLOps is essential, considering the level of effort that goes into building an application that is based on AI and machine learning. On top of that, MLOps is also prone to what is referred to as ‘drift’, where changing circumstances can subsequently change the type of data that is inputted – and is something which the application isn’t accustomed to processing. This can certainly cause inconsistencies, which is why a robust MLOps strategy is imperative to keep machine learning applications functioning at peak performance.
Similar to how an entire development lifecycle has been devised to build AI and machine learning applications, the professionals needed to make all this a reality have also diversified. Data scientists are still in high demand, what with a growing need pertaining to understanding intricate datasets with code that is formulated from subject matter expertise. While data scientists have long since been in high demand, the steady rise of an MLOps culture together with drifting has also given birth to other key job titles. Cloud architects and data engineers are two such roles, since the preparations that happen well before actual model training takes place are tasks that are complex, and require expert insight.
The field of software development has also always been in high demand, with companies always on the lookout for professionals. However, the level of intricacy required to build dedicated machine learning applications have also led rise to machine learning engineers. Unlike its regular software engineering counterpart, machine learning engineers are specialized to develop machine learning applications, together with any of the data-related specificities that are necessary for building digital products which can autonomously suggest and self-train in the wake of user interactions
BERT is an algorithm which takes Natural Language Processing (NLP) to a whole new level. Conducting deep learning on entire sentences, BERT analyses based on context. Previously, NLP only functioned as far as analysing individual words. But BERT is able to map entire sentences, with context playing a massive role in how accurately a sentence is analysed.
This is highly useful for SEO, since longer search queries can be comprehended to provide accurate results. On the other hand, numerous words can be spelled and pronounced in the same way – but they hold completely different meanings, based on how and where they are being used. BERT is also redefining how content is optimized for the web, so it can be detected by crawlers for queries that are relevant.
Deep learning mechanisms such as BERT are also powered by AI, so that datasets which are fed in ‘teach’ the program what needs to be done. The cycle repeats, as the program learns from the initial dataset, and then uses that knowledge to create logical conclusions on newer but relevant datasets. Therefore, BERT is one such algorithm that is advancing at a steady pace with the likes of AI, and has already begun to improve results delivered by search engines for longer queries
Just like BERT, GAN is also a form of deep learning. However, it falls under the branch of unsupervised learning – which means that outputs are not monitored to train the model. GANs more specifically are a form of generative modelling, which (as its name suggests), generates input data based on what it has been introduced to previously. In other words, the model learns the calibre of data that is relevant in order to offer certain outputs, and artificially creates input data to be re-fed into the system.
This is how the generative model works. Alongside this model, the adversary or discriminatory model then judges the outputs made by the generative model. If the discriminatory model considers something that has been derived from fabricated data, factual (and this tends to happen most frequently) then the generator model is concluded to be delivering outputs which are as good as the real thing – but with ‘fake’ data.
It is GANs which have been the source of what is now known as ‘deep fake’ videos. Emulating people, places, objects and situations so realistic that it is impossible to identify inauthenticity, it’s clear to see just how effective GANs are. But this form of unsupervised deep learning hasn’t only been confined to such notorious uses. GANs are highly useful for research purposes, especially for data augmentation and in events where input data is minimal. Therefore, GANs are highly resourceful machine learning systems which can influence the quality of data modelling, since these can be trained better with more relevant (albeit generated) data.
The whole premise surrounding AI and machine learning is to automate digital functions. So how does the concept of automating AI and machine learning sound? With AI and machine learning having reached a stage where objectives can be met with little to no coding knowledge, the aspect of building AI applications by those who are otherwise not programmers is something that is very much possible. Start with a relevant dataset, and configure an AI program which will meet your objectives without any formal programming or data science experience.
In other words, while AI and machine learning was put to use by manual intervention, it has now come to a stage where even this initial build/configuration process can be streamlined with little to no coding knowledge. True, nothing can replace a comprehensive and extensive application development process for AI and machine learning use cases, but low-code programming platforms can be a great starting point for smaller or less experienced teams. The low-code program can also be considered as a prototype, for enhancing the program further in the future
As our world begins to increasingly depend on the digital landscape for even the most basic needs, AI and machine learning has come a long way since its industrial use cases. With even the smallest and most boutique businesses relying on some form of AI to streamline operations, there’s much to look forward to in the world of autonomous and self-learning technology.
For one, software development lifecycles have been altered for machine learning applications, thanks to the data-centric operations that need to take place before models are officially trained. On top of that, deep learning variants such a BERT and GAN assist to further enhance the way we interact with digital products. While BERT improves the quality of longer search queries with the use of context-based analyses, GAN fabricates input data in order to train the model better, especially in use cases where input data is minimal.
As AI and machine learning steadily proliferate the commercial and consumer digital space, the need for professionals who are specialized in necessary fields are also of growing importance. This includes, but isn’t limited to data scientists – while cloud architects and data engineers are also of top priority.
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