Almost every tech companies are now aware that this is the age of DevOps; evident from the widespread adoption of the methodology to improve business productivity, resiliency and scalability. The most popular cloud computing services like Amazon Web Services (AWS) and Microsoft Azure offer services to optimize DevOps environments of organizations in more ways than one. Today, organizations can choose the right set of cloud services that grant them the benefits of a public cloud infrastructure to meet their business goals or, in some cases, exceed them.

But obviously such services won’t fit all kinds of companies or all apps used by an organization. But there are ways to optimize DevOps ecosystems on the cloud. And this blog will explore the benefits and limitations associated with this optimization on AWS infrastructure.

AWS & DevOps

AWS makes it very easy to set up services; simply by starting an account. The tech giant has formed partnerships with hundreds of service providers over the years to offer users access to a number of useful tools – Slack for collaboration, GitHub for version management, Splunk for data visualization etc.

With AWS, application development, testing and maintenance of applications are much easier. The service also comes with impressive data storage capabilities. In a nutshell, an organization can start with a fully developed, optimized infrastructure. The pay-as-you-go pricing model is the cherry on top.

What makes AWS DevOps different

Despite all its vast benefits, there are still many companies that don’t use AWS for DevOps. This is primarily attributed to the fact that AWS offers a public cloud environment with access to a plethora of tools. Because many off-the-shelf solutions are available, many companies opt for custom solutions that they can develop and run themselves while many other companies are simply reluctant due to their concerns on running DevOps in a public cloud infrastructure.

Here are a few of those concerns.

  • Security – AWS offers reliable security particularly for your organization’s data – giving you control over who uses that data and how they use it. But many companies often have their own policies regarding the maintenance of sensitive data which simply doesn’t fit with AWS.
  • Customization – Developing software themselves allows companies to customize it anyway they like to meet their needs. Essentially, they can build apps that fit their business processes instead of changing their business processes to fit their tools.
  • Training – Many companies opt to invest in training their team for third-party apps instead of investing in developing and maintaining a custom solution.


A hybrid one, of course.

One that has the best of both worlds – lying in between running a bespoke software solution and consuming the benefits of the public cloud.

For example, an organization can invest in a private cloud where they can run managed software essentially giving them more control over both data storage and security. But they will have to manage the cloud infrastructure themselves. And the customizability would be limited unlike with a custom solution.

AWS allows an organization to run their custom codes along with their services while using their cloud infrastructure. A one-size-fits-all solution doesn’t exist, and it isn’t possible to deploy and host everything on AWS. But for an organization that wants to leverage DevOps, AWS can be a great asset providing security, scalability and customizability within a reasonable budget.

Get in touch with the experts at AOT to implement DevOps today.

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What we’ve known for years as mobility has evolved into what’s popularly termed ‘Smart Mobility’. Computing is not constrained to desktops and laptops anymore as new ‘smartphones’ arrive with hardware power that rivals their desktop and laptop counterparts. The convenience granted by mobility have sparked a trend where people started browsing through their mobile devices. Now it’s the norm.

Organizations have started to realize the significance of an enterprise mobile app not only to serve customers but also for streamlining business processes, managing human resources, and reporting to stakeholders.

Modern Day App Development

Despite the dominance of mobile applications in both the tech world and the day-to-day lives of people, there are many apps that fail as soon as they are launched. Thousands of applications fail at what they had been designed to do owing to more than one reason.

Not surprisingly, one of those reasons is the back-end of the app. As more businesses invest in a ravishing front end for their mobile app, some neglect the significance of a robust backend. While the front-end makes the app visually appealing and grants a pleasing experience to users, it’s the back-end that does the heavy lifting; comprising databases, services etc. and influencing the app’s performance quotient.

This is why it’s important to choose the right back-end technology for a mobile app.

Tips to Choose the Right Back-End Technology Stack

Ensuring expertise

Regardless of the technology used for the app’s back-end, everything ultimately comes down to how efficiently the back-end is built. And the key to getting the best results out of a robust back-end is expertise. So before choosing a back-end technology stack, it’s wise to ensure whether the in-house developers have the expertise to handle the stack. If you are outsourcing the development, make sure the development company you hire has the expertise to efficiently utilize the back-end technology you choose.

Consider time-to-market

For many organizations, time-to-market is a critical factor they can’t neglect. Back-end technology directly influences an app’s time-to-market. The right back-end technology stack facilitates quick integration of features which can be beneficial for projects with impending deadlines.

If the brand that owns the app doesn’t have a time constraint, the developer can go for more exciting technology stacks that allow them to explore and get creative with the back-end likely resulting in standout features for the app.

Consider non-functional requirements

Many non-functional requirements like scalability, security, performance, usability, compliance, disaster recovery, documentation etc. also should be taken into account when choosing a technology stack for the app’s back-end. A reliable technology stack known for its success rate may not offer the best performance for the app.

Third-party integrations support

If the app’s user base is expected to increase significantly within a short time, the developers would have to polish existing features and add new ones that improve the app’s interface, service offerings, and functionality. All these additions need to be integrated rather quickly. Furthermore, other developers would want to integrate the subject app’s feature set into their own apps. Without the right back-end technology, such confidential incorporations wouldn’t be possible.

Back-end in the cloud

The cloud enables on-demand access to a plethora of configurable computing resources – from hosting servers and networks to storage, applications, and services. Every major cloud service including AWS, Microsoft Azure, and Google Cloud offers various design patterns or engines (application frameworks) that function as app back-ends.

The cloud- based back-end solutions offer several benefits such as:

  • Data synchronization across all app platforms (mobile and desktop)
  • Handling various offline scenarios
  • Sending notifications and messages
  • Reducing and managing front-end data storage
  • Throughput and Network Usage optimizatio


Cyber-security is a major keyword for modern day mobile apps. Every year, new cyber-threats emerge shaking the security foundations of almost every IT service connected to the internet. With mobile apps becoming invaluable for businesses, even the smallest security breach could potentially result in irreparable damage.

There shouldn’t be any exploitable vulnerability in an enterprise mobile app. With the right back-end technology and ample testing, this shouldn’t be a concern. There’s also the fact that relying on outdated or older technology stacks may open up security gaps in an app. Latest technologies are generally considered more secure.


Mobile app development is a steadily growing industry that’s encouraging the development as well as evolution of mobile technologies. As technologies advance, the capabilities of mobile apps evolve to newer dimensions. From amongst the vast number of technologies, every business investing in mobile apps should overcome the challenge of figuring out the right technology stack for their apps.

If your business needs help with this, get in touch with the app development experts at AOT. We can help you identify the best mobile technologies that would augment your business in the form of a mobile app.

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If you are reading this, chances are that your business has finally decided to shift to the cloud. We won’t say you are late because there are so many businesses out there still reluctant to migrate to possibly the only technology that can assuredly secure their future – the cloud.

Stats show that organizations that have already invested in the cloud is likely to increase their use of it in the next few years.

Last year, Forbes forecasted that 80% of all IT budget would be spent on cloud solutions by the summer of 2018.

Though the present stats aren’t out yet, we suppose it’s safe to assume that Forbes was right for such is the momentum of the cloud today.

Though companies have generally seen a lot of blog posts and articles about the benefits of the cloud, they still might find it challenging to determine what cloud service they should use in their organization. For many organizations, this choice comes down to three of the biggest cloud platforms in the world – Microsoft Azure, Amazon Web Services, and the Google Cloud Platform.

Comparing the three to find the best of the bunch is rather pointless. All three are popular and widely adopted for more than one reason. They all have their fair share of pros and cons. The truth is that it’s the organization that needs to choose the right kind of cloud service that matches their business strategy and goals.

To make it easier for you, this blog will explore the characteristics of these 3 cloud platforms.

But before we begin, here are a few things to keep in mind.

The cloud provider should understand your business and its objectives – The cloud service provider that’s right for you should understand your business, its objectives, and what it aims to achieve with the cloud.

Your current architecture – Your business architecture should be compatible with your cloud provider’s. Their architecture needs to be integrated into your workflows. So compatibility should be given top priority. For instance, if your business already uses Microsoft tools, Microsoft Azure is the way to go. At the end of the day, you want seamless, hassle-free integration.

Data center locations – This factor is important if the app your business is going to host on the cloud is sensitive when it comes to data centers and their locations. For a great user experience, the geographical location of the data center hosting the app is pivotal especially if the business has branches across the globe. Your service provider should have data centers in various locations that are far from each other ideally.

With that, let’s get down to the main topic at hand starting with…

Compute services

Microsoft Azure – Azure is widely preferred for its ‘Virtual Machines’ service. Its key offers include excellent security, an array of hybrid cloud capabilities, and support for Windows Server, IBM, Oracle, SAP, Linux, and SQL Server. Azure also features instances optimized for AI & ML.

AWS – AWS’ main service is the Elastic Compute Cloud with a plethora of options including auto-scaling, Windows & Linux support, high-performance computing, bare metal instances etc. AWS’s container services support Docker and Kubernetes as well as the Fargate service.

Google Cloud – Though Google Cloud’s compute services don’t come close to its two biggest competitors, its Compute Engine is still turning heads with its support for Windows and Linux, pre-defined/custom machine types, and per-second billing. Google’s role in the Kubernetes project and considering the fact that Kubernetes adoption is increasing rapidly gives the Google Cloud an edge over others when it comes to container deployment.

Cloud tools

Microsoft Azure – Microsoft’s heavy investment in AI reflects on Azure as the platform provides impressive machine learning and bot services. Other major Azure cognitive services include Text Analytics API, Computer vision API, Face API, Custom vision API etc. Azure also offers various analytics and management services for IoT.

AWS – AWS competes with acclaimed services like the Lex conversational interface for Alexa, Greengrass IoT messaging service, SageMaker service for ML, Lambda serverless computing service etc. Amazon also unveiled AI-related services like DeepLens and Gluon.

Google Cloud – The services and tools for Google Cloud seem to mainly focus on AI and ML. We can also assume that since Google developed TensorFlow – a huge open source library to develop ML apps, the Google Cloud has a slight edge over its rivals when it comes to AI and ML. Other great features include natural-language APIs, translation APIs, speech APIs, IoT services etc.

Making the choice

Though all three are dominant in the cloud services industry, Google Cloud still seems to be trailing behind the other two. But the tech giant’s partnership with Cisco, the company’s hefty investment in cloud-computing services, and focus on machine learning may give the Google Cloud more traction very soon.

Microsoft Azure, on the other hand, initially lagged behind AWS but is now considered the most dominant cloud service provider in the world. If your business relies on Microsoft platforms and tools, it’s going to pair well with Azure. But Azure’s focus on Microsoft’s own Windows puts Linux on the backseat despite Azure’s compatibility with the open source OS. So if your business is associated with Linux, DevOps, or bare metal, Azure may not be a safe bet.

This leaves us with AWS. With its massive scale and a broad array of services and tools, AWS can easily give Azure a run for their money. Though Microsoft’s efforts are starting to pay off catapulting Azure to new heights, AWS is consistently growing every year. However, if your business is looking for a personal relationship with your cloud provider and expecting an attentive service, you may find AWS disappointing. Amazon’s massive size itself makes offering such a service practically impossible.


These providers can help your business with pretty much every type of digital service it needs to stay ahead of the curve in today’s dynamic market conditions. If you think these providers don’t match your business objectives, you can still seek assistance from smaller boutique cloud providers. The bottom-line is that modern businesses are going to need the cloud backing them to efficiently adapt to a technologically advanced future.  If you require assistance regarding cloud adoption and migration, the experts here at AOT can help make it easier for you. Give us a ring to learn more.

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Are businesses getting to grip with Artificial Intelligence and Machine Learning?

AI and ML have been creating a lot of buzz in the tech realm for the last couple of years. However, many businesses still aren’t clear about the long-term benefits of these technologies.

According to a new report by Microsoft named ‘Maximizing the AI Opportunity’, nearly two-thirds of business leaders lack clarity when it comes to potential returns from artificial intelligence.

The report surveyed 1000 business leaders in the UK. It may seem surprising but there are a lot of companies out there that still consider AI as an unjustifiable expense with more limitations than benefits. Many industry experts point out that AI shows promise but adopting it in now in its present state may lead to unpleasant surprises.

That said, businesses that have invested in AI in return for something did see tangible benefits. Many organizations don’t even realize that they are using AI in some form. As a matter of fact, getting started with AI is rather simple really.

This blog includes a few best practices enterprises can follow to get started with AI in the right manner.

Identify business problems

Before investing in AI, an enterprise should make sure that they have identified and individually assessed each of their business problems. This way it will be easier for them to determine if a specific business problem can be solved efficiently with AI. The problem can be anything from handling a lot of online customer chats to freeing up employees’ time so they can focus on core tasks; AI can solve pretty much all of it.

Assess enterprise readiness to adopt AI/ML

Once the enterprise identifies the problems that they can solve using AI, the next step is to assess the enterprise itself for its readiness to build and manage AI or ML-based systems.

Major cloud service providers like Microsoft Azure and AWS offer on-demand ML services for a number of purposes including image processing and speech recognition. In addition, tools and ML-centric software frameworks are also available for the enterprise itself to build custom ML models or in-house AI-based systems. For any of these systems to work, data play a crucial role.

The main question to ask here is whether the enterprise generates and captures the right kind of data to train predictive ML models to deliver the right results. Even if it collects the right kind of data, there should be copious amounts of them for the AI system to process. Both quantity and quality of data matter when AI-based analytics is involved.

Assess in-house skills to handle AI

Before getting started with AI, enterprises should make sure they have the right talents to manage their AI systems. It’d be a wise approach to assess in-house skills to handle the AI project. This way the enterprise would also be able to identify missing skills that they will need in their mid to long-term run with AI systems.

Microsoft’s report also found that many business leaders are not sure as to how they can provide employees with the skills needed to adapt to and manage the disruption caused by AI. There’s also the fact that employee roles might change once AI starts doing its job. This requires the organization to put considerable effort into training employees when needed and retain sufficient skills for stable functioning of the AI ecosystem.

Experiment with AI

AI’s only started being of use to businesses. As such employees may be hesitant to engage with it. It’s up to the organization to foster a work culture where employees can experiment with the implemented AI systems. The employees can start small, get familiar with the technology, learn how it works, and then tinker with it to get an idea of what it can and can’t do. The company can gather feedback from them and scale up to leverage AI better.

Ensure ethical handling of AI

At the end of the day, data-driven AI/ML systems are only as good as the data fed to them. Invalid, biased, or wrong data will drive an ML algorithm to deliver useless results. Many companies make this mistake of feeding flawed data into the system leading to poor results and ROI.

All factors considered, it’s a great practice for a business to use an AI/ML manifesto to ensure that the technology is used ethically and securely. The manifesto should lay out specific guidelines to overseers and everyone else in the company using the AI-system.


AI and ML tech are at their infancy even now and demands diligent human oversight. The technology should be nurtured to deliver unbiased, responsible outcomes that can give businesses a great edge in the market and trigger disruption. If the company is ill-equipped to leverage AI but is willing to invest nevertheless, it’s better to seek service from an established AI expert.

That said, it’d be a pleasure for AOT to provide you with assistance when it comes to new-gen technologies like AI, ML, and IoT. Feel free to dial us up to learn more about AI for businesses.

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Scalability and agility are critical for the growth of startups. Back in the days, figuring out the right proportion of these two factors required considerable investment; that is till the advent of Amazon Web Services. Present day technologies can now enable startups to make use of low-cost, scalable services from the start allowing them to grow unhindered. That’s exactly what AWS does.


Amazon Machine Learning

This service makes it easier for startups to use machine learning technology regardless of the skill levels of developers. They will be able to create machine learning models without having to learn complex algorithms and the technology itself. The AWS service performs predictive analytics in real-time generating billions of predictions on a daily basis.

AdiMap, a data science startup, uses Amazon Machine Learning to cost-effectively provide valuable financial data to its customers by building predictive models for data extrapolation. The service considerably reduced their investment on hardware resources while gaining the scalability they needed.

G3 Instances for Virtual Reality

The digital world has been expecting a wide range of VR products and services from Amazon for some time now, and the conglomerate announced the impending arrival of a web service that comes close – G3 instances. It’s the latest of Amazon’s EC2 Accelerated Compute instances that provides resizable compute capacity in order to support graphics-heavy applications.

Considering the fact that VR and AR are two of the hottest trends businesses can leverage in the technology realm, this move from Amazon opens new doors. G3 instances makes it easier and faster for developers to configure infrastructure, while also allowing them to perform and execute complex 3D visualization modeling and analyses like design, medical-image processing etc. in a shorter time.

Businesses will be able to configure the web services using AWS Management Console, though the service is officially available only in the US and Ireland at present.

Microservices on AWS

For startups, a microservice architecture can do wonders, which explains why many businesses have started to adopt this trend worldwide. Though managing one microservice is quite straightforward, managing many can be a tough challenge despite all its benefits.

A growing startup will have to invest time figuring out ways to overcome operational, organizational, and architectural challenges posed by the microservices they use. AWS and its built-in features turned out to be an optimal solution. Main benefits include reduced operational complexities, efficient management of APIs, and scaling deployments.

PubNative, a rapidly growing startup, allows mobile publishers to earn revenue through native advertising. They use microservices on AWS to scale with the company’s growth while reducing response times.

According to Amir Friedman, the Director of Engineering at PubNative, the company can now handle more traffic and successfully reduced their EC2 costs by more than 70% despite using smaller EC2 instances.


Internet of Things has been around for a while, but only recently did startups leveraging IoT emerge on a large scale. For such businesses dealing with internet-connected devices, AWS IoT will provide secure bi-directional communication between the IoT-driven devices and AWS Cloud.

The service also comes with CLI, API to build and manage IoT applications, and SDKs. Other services like AWS Lambda, Amazon Machine Learning, Amazon Kinesis etc. can also integrate with AWS IoT.

Springworks, a Swedish startup, developed the SPARK technology (IoT) aiming to connect 20 million cars across Europe by 2020. They relied on AWS to enable their developers to get the features to market in a short time period. Building their IoT platform on AWS Cloud allowed them to scale without limits while benefiting from the robust security features of the web service.

Kristofer Sommestad, Springworks CTO, pointed out that AWS integration accelerated their releases by close to 300%.

Big Data Analytics with AWS

Predictive analytics proved its worth in the last few years. For startups, predictive analytics can do wonders. Its market broadened considerably after the emergence of big data. The emergence of AWS, on the other hand, broadened the accessibility of predictive analytics which was considered quite expensive before.

An IDC report predicted that the marketing of big data will grow over $32 billion by 2017.

ABI research estimates that big data spending will cross $110 billion by 2018.

Evidently, every statistical research indicates dominating growth for big data. With the AWS making it more accessible, startups will be able to build highly scalable big data applications without investing on hardware or infrastructure resources.

The Yelp case study is a testament to this. The corporation runs hundreds of Amazon EMR jobs to handle over 30 terabytes of data on a daily basis. The big data analytic framework from Amazon subsequently helped them save $55,000 in upfront hardware costs, and begin their operations in a matter of days instead of months.

Stating the Obvious – AWS

Amazon Web Services cloud solutions are aimed at helping businesses scale and grow, by providing the flexibility and the services to build applications regardless of use cases or industry. The world’s most accomplished startups like Airbnb and Slack leverages the potential of AWS for scalability.

Q4 2016 Earnings Report show that AWS generated more than $3.5 billion in revenue, an 8% of the tech giant’s total revenue for Q4.

The company recently released over 300 new services and features last year, opening more doors for businesses, especially startups.

Let’s take a look at a few AWS services and solutions that show massive potential.


The present digital age presents a plethora of opportunities for startups to grow in innovative and cost-effective ways. AWS is apparently at the forefront holding the majority of the market. However, the key is all about leveraging AWS to promote business growth. Misunderstanding the core concepts might do more harm than good, adding to the complexity and reducing time to market consequently.

When dealing with new-gen technologies like big data, virtual reality etc., it’s critical to understand how AWS can help businesses wield them in a way that only enhances their operations, rather than complicate it further. Scalability is, nevertheless, no concern. For startups, the first steps with AWS matter, which is why it’s a good call to approach a capable AWS consultant to teach them the tricks of the trade, and help them utilize its full potential.

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