With a new year around the corner, mobile app developers around the world would be gearing up to meet and overcome new challenges, and augment their skills. Generally, app developers will be keeping an eye on new technologies to figure out techniques that could help them develop applications better and faster.

Amidst all the excitement, while researching new technologies that influence app development, developers tend to forget about the one major aspect that helps an app succeed in its humble beginning in an app store – intuitive UI.

A mobile app doesn’t stand a great chance to be successful in a highly competitive marketplace. It needs to be unique when it comes to serving its purpose, and users should simply have a great time using the app. After all, first impression is what helps an app gain traction in the store. Thousands of apps in the Google Play Store disappear into oblivion in on a daily basis; a major reason for this being the lack of a good UI.

That said, here are a few UI design trends that developers should keep in mind in 2019.

Illustrations for user engagement

Content is still the king when it comes to user engagement. And content can take many forms. New trends suggest the use of illustrations instead of bland text content to convey messages to app users and keep them engaged. The idea is to tell stories using images and videos. Meaningful illustrations that provide information of value to users can impress them more than a plain text guide. Illustrations, used wisely, can transform a regular app into a vibrant one resulting in a great first impression.

Overlapping effect and gradients

Overlaps create a structured user interface adorned with colors, text or image. Due to the fact that displays have better color production today, there was a significant increase in the use of gradients in mobile app UIs. Elements with gradients make things look more natural. We will certainly see more app developers utilize overlapping effects with bold colors and gradients in 2019.

Virtual Reality

Virtual Reality (VR) made it to this list because it shows great promise. We saw AR making itself known this year as one of the hottest app development trends. We will be seeing VR do the same in 2019 as more VR devices keep getting introduced to the market and made available at reasonable price tags. VR enhances user interfaces in a way that would intrigue people to check out the app once and keep on using the app for the unique experience.

Typography

It’s not just the colors and images that should be given attention in a user interface. The font matters as well. An appealing font for a heading or description can tempt users to go through the text. Typography can complement a good narrative adding to the user experience. While the text style catches the attention of app users, narrative illustrations engage them. This way they won’t miss important info that a brand wants them to see. Typography and unique text styles would play a key role in app user interfaces in 2019.

Frameless screens

This year, there was a rather strange app design trend that turned many heads – frameless screens. Many designers went with an app design that doesn’t have frames, and the responses from users were positive. Despite the good start, not all designers were willing to take the risk of using frameless screens in apps this year.

But now that mobile phone manufacturers have started coming forward with frameless smartphones, more app designers can be expected to adapt to new trends and try out ‘frameless’ screens in 2019. Many modern devices also have curved edges that naturally impose design restrictions including sharp edge elements. Frameless screens can shine here creating a fresh look and a unique experience for mobile users. Its applications in both mobile app design and web app design makes this trend one of the best design trends in 2019.

Voice-assisted UI

Advancements in AI and Machine Learning have helped chatbots and communication assistants become the most popular trends in 2018. Many websites and mobile applications have also started using voice communication to enhance user experience. As bots and voice interfaces become common, app developers will be thinking more about leveraging voice-assisted user interfaces to serve multiple different purposes in an app, delivering a more interactive experience to users.

Conclusion

For the app development industry, 2019 will be about innovative combinations of a number of great app design and development trends. There will be a renewed emphasis on designers’ knowledge on new trends and techniques. As always AOT keeps ourselves updated on all new app design & development trends so our clients can get the best of new technologies.

Let us know your queries. Talk to our experts today.

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Over the years, businesses utilized a number of advancements – both technological and industrial, to accelerate their growth and increase their profits. Of all that which businesses have leveraged so far, one particular advancement stands out for its massive potential – Data Analytics.

With extensive data analytics, businesses can uncover valuable insights from the tremendous amounts of seemingly senseless and random data they generate. Armed with this information, organizations can tweak their business strategies for better results.

What makes this ‘big data’ awesome is the fact that it shows great promise for all types of enterprises irrespective of their sizes. Through big data analytics, enterprises can not only increase the revenue generated but also identify security vulnerabilities and gaps in their services and products. Big data is still an evolving technology however, and like every evolving technology, it is also shrouded by a lot of myths and misconceptions.

This blog debunks a few of the most popular myths that deter businesses from checking out big data.

Myth 1: All that matters is size

The first thing that anyone can think of when hearing the term ‘big data’ is the size. As such, there seems to be a myth that the main defining trait of big data is the sheer size or volume of data involved. This is not the case. Though size is certainly one of the main characteristics of big data, it isn’t the only important one.

The volume of data involved demands sufficient storage systems. But big data also requires high velocity data processing mechanisms to deliver relevant, more up-to-date outcomes. The data come from various sources which means the outcome may vary from what was expected. The data being handled by big data should be also be veracious and the insights from these data should provide value.

From all these facts, we can infer the most important traits of big data other than size or volume.

Velocity. Variety. Veracity. Value.

Neglecting these features can result in an improper implementation of big data that can complicate simple solutions and deliver irrelevant results while wasting resources.

Myth 2: Big data is a collection of high quality data waiting to be processed

Big data is a collection of a lot of data sets that can include both relevant and irrelevant data. All that data don’t necessarily have to be of high quality. As a matter of fact, without proper data filtration mechanisms, big data analysis can result in a lot of data quality errors.

Many organizations opt not to clean the data before processing to avoid the hassle of dealing with such large amounts of data. Data cleaning also requires the organization to invest more in big data. This practice most likely ends up with big data failing to do what it’s expected to do. For big data analysis to be effective, the data should be cleaned. This helps the system provide more accurate results.

Myth 3: Big data predictions about a business’ future are highly accurate

This myth is based on the assumption that the volume of big data is directly proportional to the accuracy of big data analysis results. The more data one feeds into the system, the more accurate the results will be. But that’s a myth however. Data certainly hold great value for modern businesses but there are other factors that are just as important if not more.

A big data system will never be able to accurately predict a business’ future by simply analyzing data. Factors like economy, technological advancements, human resources etc. directly influence the success of a business, and data simply won’t be factoring in everything that directly or indirectly influence the business’ success. What big data can do is extrapolate future possibilities by comparing and analyzing historical data.

If the business uses real-time data instead, the system will be delivering predictions based on a probability theory i.e. the results still won’t be certain. However, it’s true that the accuracy of forecasted results will be higher if the data fed are relevant and of good quality.

Myth 4: Big data is expensive

That’s the myth all right. This is the fact – big data ‘was’ expensive. Back when big data started turning heads for its potential, only big organizations and government bodies stepped forward to invest in it. The investments weren’t small either as organizations required large-scale data centers and talented big data professionals to wield big data properly.

But time has changed. As big data garnered praise from all directions, many vendors stepped forward reducing the licensing costs of big data tools and making them much more affordable to small medium-sized businesses. In addition, new tools and techniques are also being introduced to help smaller businesses perform big data analyses.

Myth 5: Big data is centered on machine learning technology

Machine learning certainly is associated with big data. But big data isn’t centered on machine learning. New generation big data analyses make use of complex machine learning algorithms to derive sensible and relevant insights from massive data sets. That’s the whole concept of Machine Learning – using data to uncover information that can help an organization or an individual make meaningful decisions. Both technologies are used in conjunction to deliver expected results. Big data can exist without machine learning.

Myth 6: Big data is all about analytics

What’s strange about big data is that it has multiple definitions, and most of them are accurate. A simple Google search would get you at least a dozen different yet valid definitions of big data from various sources. While some consider it as massive data sets, others see it as an analysis technology that can process massive data sets. The fact is that big data is something much bigger than data analysis with many capabilities to solve a variety of problems with data.

Conclusion

For big data to truly live up to its name in an enterprise, it should be seen as something more than a lot of data being processed. It needs to be managed and overseen by experts who can tell myths from facts.

If your enterprise is planning to leverage big data and is looking for expert guidance or automated solutions, you are at the right place. AOT’s deep expertise in a wide array of technologies including big data and AI/ML can ensure maximum big data ROI. Drop us a message to get started.

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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.

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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.

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