We live in an age where technologies have evolved big enough to make revolutionary changes in various industries across the globe. Businesses today enjoy the luxury of powerful technologies that can trigger digital disruption in a short time, provided they are simply wielded well. Among the hottest technologies today is Machine Learning (ML) – something that’s become invaluable for growing and established businesses.

With ML, businesses will be able to uncover insights from the data they generate, and predict outcomes which subsequently leads to remarkable changes in the way they function and grow.

The technology has been around for a while, and is constantly evolving to become more sophisticated. Yet, its full potential has not been explored beyond self-driving vehicles, fraud detection, and predictive analytics of retail trends.

Nevertheless, it’s set towards a future with tremendous impact in our world.

Here are a few interesting forecasts on where machine learning is headed to.

Improved algorithms

ML makes use of unsupervised algorithms to perform predictive analytics on datasets when only input data is available. Supervised learning is when the output variables are already known, which makes unsupervised algorithms quite close to the concept of artificial intelligence. The machine itself learns how to identify complex processes and patterns without direct human intervention.

Unsupervised algorithms can find hidden patterns, groupings, and more which wouldn’t be possible if they were supervised. The approach is already in practice, but we will see great improvements to unsupervised algorithms in the coming years resulting in more accurate predictions.

Quantum computing would be adopted more

ML employs a number of classical techniques that can be enhanced by leveraging quantum computing and its benefits. Quantum ML algorithms are potentially capable of triggering a major evolution of machine learning resulting in faster data processing and faster information synthesizing. Drawing insights would be much easier with quantum computing facilitating heavy-duty computational capabilities.

Advanced cognitive services

Present day cognitive services consist of many components including machine learning SDKs, APIs etc. which allow developers to make their applications smarter. Intelligent applications will be able to carry out complicated tasks like vision recognition, speech detection, speech understanding etc.

With the technology constantly evolving, we can expect advanced cognitive services in the form of highly intelligent applications that will not only be able to speak, hear, and understand but also reason with the situation and interpret users’ needs effectively.

Advancements in robotics

Machine learning and AI are what’s going to drive robots in the future. In the coming years, the advancements in machine learning will lead to increased use of robots. The robots would obviously be smarter with self-supervised learning, multi-agent learning, and remarkable cognitive capabilities. They will be able to accomplish more complicated tasks, and will go mainstream in a short time.

Conclusion

Still considered to be nascent, machine learning is inarguably one of the most disruptive technologies in the world today. The forecasts mentioned in this blog explore only just a fraction of ML’s potential. The complexity of the technology and the difficulty in comprehending a great ML adoption approach make many businesses reluctant to use machine learning. But it’s all about to change soon.

If you have queries regarding ML or need an ML-based digital solution, feel free to start a conversation with our experts.


While the rapid evolution of technology is proving to be of great benefit to businesses, it’s still not easy for them to cope with the not-so-subtle changes or overcome the plethora of challenges. Keeping pace with evolving technology in itself is not easy, even for bigger businesses. In addition, they need to figure out effective and optimal ways to deal with the large amounts of data involved in their IT operations, changing customer demands, process inefficiencies etc.

Out of all these aspects, it’s the data that hold the key for a business’ rapid growth on par with evolving technologies and dynamically changing market conditions. But when enterprises keep relying on traditional data monitoring and management systems, the increasing volumes of data would pose a problem. Moreover, they’d be unorganized and insensible for the most part, leaving the enterprise’s data management staff to do some heavy lifting.

To deal with these issues around data, data management, or rather ‘service management’ in an enterprise, modern-day technology grants intelligent computing mechanisms and practices.

…and this is where AIOps originated.

But what is it?

AIOps or ‘Artificial Intelligence for IT Operations’ is a term popularized by Gartner. It’s a combination of data analysis and machine learning technologies designed to make internal management systems of enterprises more sensible or ‘smart’.

Why businesses need AIOps

Many businesses already prepared for the spike in data in advance, and set up various systems in place to handle the data flow on a daily basis – from project management methodologies to sophisticated IT management systems. The existing monitoring systems are capable of alerting the management when issues arise. But the problem arises when there is just too much data, and the system starts generating thousands of alerts every minute. The personnel are in for major persistent headaches.

In this scenario, AIOps platforms would combine big data and AI/ML functionalities to either enhance or partially replace a number of IT Operations tasks and processes which include performance monitoring, event identification and correlation, analysis, service management, and automation.

So essentially, AIOps adds one more layer over the enterprise’s platforms – something much smarter, and capable of simplifying operational and managerial tasks.

Here are a few notable advantages.

  • No need for organizational silos – In enterprises that generate overwhelming volumes of data, procuring relevant real-time data for analysis can be tricky. But if the AIOps platform is directly linked to the project/data management platform, it can gather necessary data and derive insights without the need for any silos.
  • Prioritizes issues for easier management – AIOps, unlike traditional systems, analyzes data to understand and prioritize issues, and then highlight issues based on their priorities to the management. This way, the authorities can focus on the issues that require immediate attention rather than putting a lot of effort assessing threat levels and risks to categorize issues.
  • Solves problems automatically – Doesn’t apply to all problems, but it’s still a highlighted capability of AIOps. Every time an issue is solved, the AIOps-based system records the methods involved. It understands the type of problem and an optimal and effective solution, making things easier for the IT staff. The system can be automated to solve certain specific recurring problems.

Digital transformation with AIOps

The world sees digital transformation of enterprises as a shift from legacy systems to modern dynamic frameworks that facilitate improved business agility, faster growth, and better decision-making. But when you look at it closely, you will notice that major changes from digital transformation take place particularly around three key areas – the business model, customer experience, and operational process.

AIOps, in essence, can be linked to these areas to generate insights from the data collected from them. Leveraging both big data and AI helps enterprises eliminate repetitive tasks, and makes internal systems more adaptable and responsive to change. Because it predicts sources of potential risks or threats, the enterprise can proactively plug security gaps and minimize risks. This action can also improve customer satisfaction.

To conclude, AIOps facilitate Continuous Integration and Continuous Deployment (CI/CD) for IT functions, making it a great addition to an enterprise’s accelerated IT ecosystem.

Conclusion

Despite Gartner’s deep analysis on the platform, AIOps still hasn’t gone mainstream yet, probably due to the fact that IT Ops team are still hesitant to use new-gen technologies that replace the ones they are already familiar with. However, the hard truth is that IT ecosystems of enterprises will inevitably change as long as technology continues to evolve.

Resisting the change would only do the enterprise more harm than good in the long run. AI and big data have already proven their mettle individually. Coupling them both is what AIOps basically does, and it can guide the enterprise to a better future through better decision-making and operations.

You can get AIOps and other similar AI-driven solutions from AoT Technologies, as we’ve been helping enterprises leverage these technologies for a while now. Interested in knowing how a custom machine learning-based solution can secure your business or empower it? Contact us now.

Image Designed by Freepik


The advent of mobile technologies and smartphones brought forth a major cultural shift in people’s lives. A similar paradigm shift is now going on in mobile app development, due to an older technology making a majestic comeback – Machine Learning. Though ML’s been around for a while, its potential wasn’t, or rather couldn’t, be explored a couple of years back. The technology is quite advanced now, enough to enable mobile apps to not require explicit programming to perform certain tasks.

A Machine Learning algorithm allows the mobile app to collect and analyzes lots of data to come up with precise conclusions. The system learns automatically after detecting app usage aspects and improve the app’s experience while it performs.

Easier said than done.

To do all this, the app requires sophisticated ML algorithms integrated into it as well as access to sufficient amounts of data sets, enabling the system to analyze previous experiences so as to make better decisions in the future. In addition, the system will adapt to new data. The apps essentially become more intelligent in the process.

Key takeaways for ML enthusiasts and developers

If you think it’s not yet time to build a Machine Learning application for your business, you would be taking the words back soon. McKinsey’s reports indicate that the total funding of ML apps is over $6 billion worldwide. Many businesses around the globe have already started preparations to effectively utilize the technology to get ahead of competitors.

That said, here are a few things that ML enthusiasts should always keep in mind.

  • Utilize all the data available – When it comes to leveraging machine learning for app development, developers should take care not to use sub-sampling, and utilize every data they have access to. Feeding more data to the algorithm helps it deliver more accurate results and predictions.
  • ML method could spell success or doom – The project’s success often comes down to the ML method selected. Unless there are large amounts of data involved, it’s best to stick to simple ML models for better predictions.
  • Pay attention to the parameters – Because the parameters and the methods should be constantly monitored, it’d be great to have a qualified data scientist onboard.
  • Improper data collection can cause problems – The efficiency of ML depends on the data used to train it. Improper data collection including incorrect labels, fragmented, poorly featured etc. can impact the efficiency of the ML algorithm.
  • Take client business model into account – The client’s business model should be considered before building ML algorithms for their benefit.

Top ML Frameworks for Mobile App Developers

ML’s rising momentum also owes it to the many great ML frameworks available at present and the support from popular cloud services. With many technologies including the cloud providing good back, app developers can now build ‘smart’ apps with cognitive learning capabilities, on-device processing, and minimum lines of code.

Here are the top ML frameworks that developers use to build ML-powered intelligent apps.

Google Tensorflow

At the top of this list is Tensorflow, Google’s contribution to create innovative Deep Learning models. The tech behemoth is reported to have invested a lot in AI/ML technologies, and is now letting everyone get a taste of the tech with its framework.

Tensorflow is based on computational graph that comprises of multiple nodes in a network, and each node is basically an operation that executes either a simple or a complicated function. Many popular Google services use Tensorflow to provide users with an intuitive experience. The same can be done for any mobile app now with the framework.

Apple’s Core ML

Apple’s machine learning framework Core ML was launched in the WWDC 2017, and is one of the main reasons why many iOS apps today are capable of performing tasks that human eyes do. Text & barcode detection, object tracking, face tracking etc. are just a few of Core ML’s supported features. In addition to machine learning, the framework also offers NLP APIs that can understand text.

Microsoft Cognitive Services

With Google and Apple rolling out powerful frameworks for ML, Microsoft couldn’t stay far behind. The Microsoft Cognitive toolkit features an ML framework offering sophisticated Deep Learning algorithms that can be used for multiple purposes including app development. Popular Microsoft services like Skype, Cortana, and Xbox were developed with the Cognitive toolkit.

One of its biggest advantages is that it allows use of popular programming languages like C++, Python, and Brainscript to build Deep Learning models. These models can be used to empower apps for Windows and Linux platforms.

Caffe

Out of the frameworks mentioned above, Caffe is the most unique. It was originally developed by Berkeley AI Research at UC Berkeley. The framework underwent many further refinements from community contributors. That’s what makes it unique. Caffe is open source and licensed under BSD-2 Clause.

Caffe is widely used for image classification, recommender systems etc. owing to its Convolutional Neural Networks (CNNs). A pre-trained model of Caffe is also available, called Model Zoo which can perform many tasks. With Caffe, developers can build apps for Windows, Mac, and Linux platforms.

Conclusion

The tech world considers Machine Learning to be one of the major factors that displays a business’ competence in just a few years. The technology is also expected to contribute to the Internet of Thing’s much anticipated mainstream dominance.

As of now, businesses can gain great competitive advantages by leveraging ML’s potential via enterprise mobile apps, not to mention better decision-making. The technology is worthy of investment, however only a few developers are actually qualified enough to mold the technology to fit into specific business models. Then there is an obvious need for great amounts of data for ML to make a significant difference.

AoT is one of those companies that can harness the potential of ML for your business through robust and secure custom enterprise mobile apps. We have already earned competence in new-gen technologies like AI/ML and IoT, complementing our already reliable expertise in mobile application development. Send us your queries to understand what ‘smart apps’ from AoT can do for your business.

Image Designed by Freepik


Even the best technology in the world can’t bring success if it’s not put in a useful, desirable form. This particularly applies to modern-day software and mobile applications. Even if the solutions are capable of serving their purpose, they should meanwhile be able to provide a solid experience to their users. This makes UI/UX designers as important as developers today. UX of websites and apps are important to Millennials as well, who are in turn the major customers of eCommerce businesses.

An eCommerce site should give potential customers a seamless, hassle-free shopping experience every time without even slightly confusing them. Even the smallest glitch or hassle can negatively impact customer engagement resulting in increasing bounce rates. A seamless experience on the other hand can impress buyers enough for the site to be bookmarked for revisit.

All of this makes you wonder what exactly constitutes a good user experience. There are various UX design elements that influence UX on a website. For an eCommerce website, the key is in innovation. The brand should explore avenues to figure out new ways to improve user experience.

Here are a few tips that could come in handy in giving users a great experience on an eCommerce website.

Personalized product recommendations

Amazon started it and people just loved it. The technology behind it however goes deep – analyzing users’ behavioral data and providing them recommendations on what they should buy, both on the website and on social media platforms. This, most certainly, boosted the users’ experience on Amazon, and can make for a great feature in eCommerce websites that also increases conversion rates.

With such a feature, the business can recommend products that match users’ most recent searches, and even suggest products that can be clubbed with a user’s purchase. Machine learning and big data are at the core of this feature.

Increased page loading speed

Page loading speed is one of the most important factors that drive user experience. Rapid technological advancements made super-fast internet accessible to pretty much everyone today. People are used to getting things faster now, which consequently developed an intolerance to slow digital experiences. 2 seconds have become the acceptability threshold for website loading now.

This can be challenging for eCommerce websites that have a lot of data including high quality multimedia files. Boosting page loading speed for such websites should be given top priority. A better web hosting plan can considerably increase page loading speed. So can a limited use of external scripts and JS & CSS files, compressed images, and cached data.

Product comparison feature

It won’t be easy for customers to choose from thousands of products in an eCommerce website. That’s a problem the business can solve by adding a product comparison feature on the website. On-site product comparison is also a means of providing a seamless shopping experience, allowing customers to make confident purchase decisions after comparative reviews. The only important thing to remember here is including a “buy” button on the comparison pages.

Guest checkout

Demanding customers to be logged in for making a purchase may not always be the good idea for an eCommerce business. The customers may be looking to make a quick purchase, when the site asks them to login with their credentials. They may search for an easier alternative if they cannot afford the time. This is where guest checkout can be helpful to users.

This also makes it easier for new customers to make a purchase on the website, without logging in or registering though they can be given the option to register or login once they’ve completed a purchase.

Cart abandonment targeting

Users abandoning carts before finishing a purchase presents a great challenge to eCommerce enterprises. There are potentially many reasons for the abandonment. It could even be that they forgot that they added a lot of products into the cart after a long browsing session.

The website should have a feature to track and target these abandoned carts by sending personalized emails to those users either reminding them of the cart or offering them discount on the products.

Conclusion

The bottomline is that user experience should be given top priority in eCommerce websites, and there are multiple ways to boost the user experience. The latest standards include microinteractions, providing an omnichannel experience, and product comparisons. Every feedback should be taken as an opportunity for improvement. Every improvement made should be A/B tested for success. That’s one of the best ways to progress in today’s competitive market.

AoT can help you do all this. We focus on user-centric eCommerce website and mobile app development, ensuring your customers get the best digital shopping experience. Feel free to talk to our experts to craft the website or mobile app that meets your requirements and your customers’ desire.

Image Designed by Freepik


A decade ago, it was very challenging for enterprises to adopt to the mobile ecosystem. Mobile devices were only gaining prominence then. Fast forward to 2018, and we see enterprises competing to stay ahead in the mobile platform. Mobile apps are now the best medium for businesses to build a relationship with and influence their customers, which would subsequently increase sales and boost business growth. Mobile is not new anymore to anyone.

To stay ahead of competition in the mobile world, enterprises will have to keep tabs on hot trends and evolving new-gen technologies that can either add more features to an app or augment the existing ones. One particularly important factor today is the intelligence of the mobile app which would go on to define the future of mobile app development.

Mobile app intelligence

Machine learning and AI have undergone major improvements over the years. Though they are both still considered to be in their infancy, the technologies certainly are making a difference in user experience and how enterprises can serve customers better. For this purpose, mobile apps need to be intelligent; as in they should possess cognitive capabilities. This epiphany has opened up a new competitive ground for businesses.

Let’s see what we can surmise about the potential future of mobile app development in light of the growing influence of AI and ML.

Cognitive intelligence

Modern apps can automate certain tasks. But apps of the future should be smart enough to learn from the exercise with its own logic, and subsequently improve its capabilities to serve better. The advancement in big data and modern-day cloud’s ability to make efficient big data analysis possible makes the whole idea feasible. Future applications will be made smarter by implementing big data and AI technologies to strategically use data to improve customer experience throughout their purchase journey. This is the outcome that enterprises are striving for.

Omni-channel experience

Consumers use many communication channels today with multiple touchpoints including social media, chatbots, web apps and websites, review sites etc. If a business can synchronize the data from all channels, it can ensure a seamless experience for the customer across said channels i.e. a consumer would be able to start a potential purchase journey from one medium and seamlessly continue it in another medium. With mobile being one of those channels with the highest market potential, it can be the core that manages seamless omni-channel transactions. Omni-channel user experience would be an ideal feature for an app of the future.

Continuous improvement

Traditional IT infrastructure is no longer capable of meeting the changing market dynamics of the modern world. That’s where the cloud made a difference. Future-proofing for businesses today means they need to ensure continuous improvement of their IT systems and processes, and customer experience.

In the case of enterprise mobile apps, real-time insights on consumer behavior also provide the ideal path to evolve. The app should eventually be capable of adapting to dynamically changing consumer preferences, and serve them across different channels.

Conclusion

Enterprise apps are no longer a build and forget project. It’s grown beyond just a necessity into something that paves the road to futureproofing a business. With many other technologies like the cloud, IoT, AR, and VR retaining their ranks in the top trends lists, and businesses shifting focus to customer experience, mobile apps are heading for a terrific transformation.

AoT technologies has a comprehensive knowledge on the impact of new-gen technologies on software and mobility solutions. We could be the key to making the enterprise app that could give your business the edge it needs. Contact us to learn more.

Image Designed by Freepik