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.

Conclusion

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.

Conclusion

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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A while back, the Internet of Things created a lot of buzz with many technology experts deeming it as a revolutionary technology with massive potential to digitally transform enterprises. The hype encouraged many companies to jump on the IoT bandwagon and invest in the technology for a promising future. However, not all of those companies gained revenue from their IoT projects.

As a matter of fact, a recent Capgemini report revealed that less than 30% of organizations generate service revenue from their IoT projects.

If the organization is simply taking a leap of faith with IoT, chances are that their IoT efforts could end up being a cost center instead of a revenue generator. They will need a great IoT monetization strategy to make sure they are not losing more than they are making with the technology.

Here are a few tips to monetize IoT projects effectively.

IoT devices + value-added software

Instead of creating programmed IoT devices, many companies have started creating reprogrammable devices. The software for these devices can be updated over the air, which means the devices can be potentially programmed to do more things than what they were initially intended to do.

This strategy also makes it easier for the company to generate revenue. The devices can be built so that value-added software can be enabled in the field. The software can be sold or linked to the hardware for a price.

Leverage SaaS

Companies investing in IoT will have to connect their devices to some network using some kind of technology. Choosing what technology to go for in order to connect the devices won’t be easy. Many companies evaluate a number of platforms that can get this job done for various kinds of services including Microsoft Azure for Cloud, Jasper for connectivity etc. Many others take a safer approach of inventing their own combination – whatever works best for them.

But this isn’t always a great idea. Sure it’s a safer approach but this path requires even more investment from the firm. There’s no monetization opportunity here. We simply included this to warn companies not to resort to building tech that already exists in various popular platforms. Instead they should leverage Software-as-a-Service (SaaS) to quickly and consistently deploy and iterate without investing a lot.

Give room for third-party developers

Some companies go a mile further by giving third-party developers access to their ecosystem. Oftentimes this ends up as a great investment. Third-party developers can bring innovation into the mix – things the company’s in-house team may not have thought of, and add value around the company’s core products and services. The key is to make it easy and appealing for developers to innovate on your company’s IoT platform. Innovative platforms tend to competent and easier to monetize.

Join or create a marketplace

All that effort and innovation would be pointless if customers can’t find your company’s IoT products/services. The IoT products a business wants to sell – be it apps or connectivity platforms should be displayed at a marketplace where customers can find them. If they can’t find such marketplaces to join, they can create an automated, integrated and scalable marketplace themselves. This naturally leads to more revenue.

Optimize the monetization of assets

Companies investing in IoT projects normally don’t contemplate monetizing their investments on day one. But the growing IoT market eventually makes them think they should have thought about it in the beginning.

Software is one of the major factors that determine an IoT project’s success. So companies normally opt for a basic business model where they sell the software or give it away as a freemium product. In such cases, billing can be quite costly. Fulfilment and licensing won’t be automated as well. The company won’t be focusing on showcasing or promoting the software.

A better approach would be to move the services and software on to the cloud so customers can purchase and use them on-demand. As the value of the data generated by this software increases, the software itself will start to make more revenue. The company should make sure the billing models deployed are new and that the billing system is future-proofed.

Conclusion

If the company realizes the true value of their IoT project, they should be able to successfully and consistently create that value from the technology. But monetizing that value requires diligence and leadership in order to align the company’s goals with the service or product and their sales strategy. So either they are going to need highly qualified IoT veterans in-house at the top to successfully monetize projects or they need to team up with IoT experts.

If you’d like to explore the opportunities and benefits of an IoT partnership or develop an IoT mobile app, contact the experts at AOT today.

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The cloud kept evolving over the years, and ‘Multi Cloud’ is widely anticipated to be its next evolution. Public and hybrid clouds have become much more important in modern IT infrastructure owing to the rising prominence of Software-as-a-service (SaaS). Multi cloud is expected to fill more gaps in the coming years.

Multi cloud

It’s not to be confused with hybrid cloud, and is basically a combination of a number of cloud technologies from multiple public clouds to meet the changing needs of businesses in the modern age. Multi cloud typically is not specific to a single vendor. Hybrid cloud, on the other hand, is a cloud architecture that blends public and private clouds.

The rise of multi cloud began when enterprises tried to avoid dependence on a single public cloud provider, and instead choosing specific services from each public cloud provider.

Last year IDC predicted that over 85% of IT organizations will adopt multi-cloud architectures by 2018.

One of the biggest benefits of adopting a multi cloud approach is that it boosts innovation. The right combination of cloud technologies enable different departments in an IT organization to adopt cutting-edge applications both to balance workloads and to accelerate digital transformation. The cloud is known for the flexibility it grants an enterprise. When multiple cloud technologies are combined, the same flexibility would be present while offering optimal conditions for the best performance.

If it’s an eCommerce business, there can be a highly scalable cloud platform and a different cloud technology to balance as well as meet the large storage demands of a data-intensive workload.

Behind the multi cloud trend

Cloud computing, with each evolution, became more sophisticated as well. Back when it began, the vision for the technology was to place workloads on a single cloud be it private or public. Times have changed. Today, hybrid cloud architecture grants more flexibility and benefits to businesses in addition to many choices that augment how the business digitally operates in more ways than one.

There are many viable public cloud options now including Amazon Web Services and Microsoft Azure. Tech corporates like Google and Oracle have joined the fray, presenting enterprises with many options. With so many options available, many enterprises started experimenting by combining various cloud technologies either through architectural processes or through ‘shadow IT’ where groups in an enterprise used public cloud services without explicit organizational approval. Regardless of the method adopted, many organizations today use multi cloud infrastructures.

However, managing multi cloud environments presents a lot of challenges and complexity that many organizations may struggle to tackle. With help from cloud service brokers or using cloud management tools, they can somewhat reduce the complexity though they will only be able to use a subset of features from each cloud instead.

Multi cloud management and deployment

Though multi cloud provides more flexibility, control, and security, the downside is that there would more to manage as well. The cloud may have grown out of its infancy, but multi-cloud is still relatively new. There’s so much more to explore which makes the management and deployment of multi-cloud environments a hassle despite its benefits.

Here are a few expert tips to keep in mind when adopting a multi cloud strategy for your enterprise.

  • Map the network to see where the multi cloud can fit – Different lines of businesses are best served by different cloud vendors. So it’s important to have a clear picture of your overall system and its management to figure out where the cloud can fit in and make things better.
  • Devise a flexible purchase process – To avoid cost impediments to using different cloud services from different vendors, it’s wise to come up with a purchase process that’s flexible as the cloud services that would be used. It’s also important to analyze whether each service is delivering value that’s worth its cost.
  • Use cloud management tools to keep track of costs – Cost optimization should have top priority when leveraging multi cloud for the enterprise. There are tools available that can perform accurate cost analysis of workloads when placed in different clouds.
  • Automate policy across your multi cloud ecosystem – When using multiple cloud services, especially from different vendors, an efficient approach is to have a single standard of policies. They should be applied automatically to each environment covering various areas including virtual servers, workloads, data storage, traffic etc. Such a configuration also makes it easier to apply updates so that they propagate seamlessly across the environments.

Conclusion

Public, private, hybrid, multi, pragmatic hybrid: the cloud comes in many forms today. And it’s not their names you should be focused on. The key is to understand what each offers, and learn how each benefits your enterprise. If you require help implementing the right kind of cloud strategy to your business, AoT offers our vast expertise. We can help your business get the best out of cloud computing with innovative, custom cloud solutions. Want to learn more? Give us a call.

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AI is propagating across various sectors including software development signifying its progression into the mainstream. Every day, more enterprises choose to leverage AI technologies to make a positive difference to their difference.

The survey by Native Science found that 38% of enterprises already use AI technologies, and it will grow over 60% by 2018. So far, there’s been more success stories of AI adoption than disasters. New gen cloud technologies, cloud services, and advancements removed that technical barrier which existed up till recently.

However, only a few organizations realized the true core of modern AI – Machine Learning (ML). To leverage AI, they will have to understand ML. And to leverage ML, they will need raw data. This is one of the reasons why big data is rising in demand. As an IT consultant and a professional software development company, we are including a few useful tips for businesses planning to adopt AI software to reach new heights.

  1. Without big data, you won’t go far

For AI to be capable of executing tasks, the AI model must be trained on a broad data set. The more data you have, the better the results would be. Making use of specifically tailored software without technical limitations to collect, process, and analyze big data in real time can be one of the greatest assets an enterprise can have in these times.

  1. AI applications should be real time or at least close to real time

Most businesses that have adopted the technology use it for real-time applications that include fraud detection, speech and face recognition, chatbots, autonomous vehicles, digital assistants etc.

  1. Cloud-powered AI has more potential

Machine learning obviously requires considerable resources to function the way it should. As mentioned before, it can be trained to process huge volumes of data. But the truth is that it requires more resources to be trained than to process data. This is the reason why implementing ML models was a challenge for many business.

But now that cloud technologies like Microsoft Azure, Amazon Web Services etc. are available, the demand for computing resources to use ML is not a challenge anymore. Businesses won’t have to invest on infrastructure and assembling a powerful server to implement machine learning.

With cloud services, AI software can use all the resource it needs and only when it needs them. Businesses, on the other hand, need only pay for the resources they use.

  1. AI and ML are different, but the latter completes the former

At the core of ML software are strong algorithms. This combination and more make up the AI technology that benefits an enterprise. However, it’s not just about the algorithm. There are other factors that influence the success of AI in a business ecosystem.

  • Training data sets
  • Integration into business processes

If the organization doesn’t generate a lot of data, AI wouldn’t be of much use, its potential will lay dormant, and it will likely make mistakes. Depending on business needs and goals, the ML model can be either trained to use the data and generate a desired output or just scour through the data to find or identify trends, dependencies and patterns.

To give it access to all the data in a business ecosystem, the AI software should be integrated to all vital business processes.

  1. AI software using ML needs to be retrained frequently

An MI-based AI solution is not a one-time effort, and this could probably be the only downside to using AI. Artificial Intelligence is still not the digital replica of human intelligence i.e. an AI solution cannot multitask. It is designed to focus on a single task and give accurate results.

For instance, an AI solution designed for analytics cannot be made to function as a digital assistant. However, an AI that functions as a digital assistant can have analytic features though it still only performs the tasks of a digital assistant.

If the AI solution is to be used for a different kind of task, the system should be retrained.

A business environment is dynamic. New competitors may show up and new trends may appear. All of these changes subsequently tends to change the nature of the tasks that a business needs to solve. This means the ML model in use would have to be retrained to cope with the changes.

Internal factors like changes in the organizational or operational structure, new corporate strategies, reconsidered business goals and KPIs etc. can also be a reason that demands retraining the ML core of the AI solution in use.

  1. AI – ML solutions still need to be driven by humans

No matter how great the algorithm is, AI-based systems still make mistakes either minor or major. Rare occurrences may not be a big deal for some businesses. But if it is too frequent, the impact would likely be too big to ignore. This is a risk that enterprises are not yet ready to ignore which is why the machine learning software’s architecture is designed to go with human governance.

The bottom line is that businesses need to be aware of where and how the AI solution makes decisions. To identify pain points of the software and to analyze the decisions made by the AI, humans should be kept in the loop. AI software should be transparent and would have to be periodically monitored. This transparency supports business accountability and audit subsequently aiding in either verifying or preventing liabilities to the business.

Afterthoughts

AI is apparently promising indeed but also demanding. With the cloud, big data, and blockchain becoming part of the world, the potential of AI is more visible to enterprises that want to give themselves a boost to outpace competitors by being innovative. We hope the idea of AI powered businesses has been conveyed along with how the technology can be properly leveraged by businesses.

We, at AOT Technologies, are well equipped to educate you on and integrate AI_ML Technologies into your business. You can have complete confidence in us on delivering a customized solution for you. Please feel free to contact us.

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