Some companies may choose to completely move to the cloud or choose a hybrid model. The hybrid involves only moving a portion of the infrastructure to the cloud while keeping other operations on-premises. A VPN connection is typically established for the on-premises to the cloud secure communication.

Manufacturing companies may choose to deploy the data storage to the cloud for data sharing and analytics, while keeping on-premises or legacy SCADA systems running intact. In such scenario, the company may choose to deploy an edge-to-cloud data collection along side the legacy system. The edge-to-cloud gateway may also expose APIs for secure direct to the cloud communication for running internal applications.

Why Companies Choose to Migrate To The Cloud?

Here are the main reason why companies choose to migrate to the cloud:

Cost Reduction

By going to the cloud, companies may not need to purchase expensive server equipment, keep it up-to-date with software updates, and pay substantial electricity bills. Besides, you cut operational expenses as your DevOps specialists and system administrators don’t spend time on backups and hardware maintenance. Cloud providers offer pay-as-you-go pricing, meaning you only pay for the computing power you use.

Security

Keeping on-premise servers secure is very involved. The system administrators have to keep the servers up-to-date constantly, scheduling downtime and applying security patches. The network administrators stay busy monitoring the network for any security threats. Most of these issues are eliminated by going to the cloud. The reliable cloud providers regularly upgrade their services to the latest standards and regulations.

Scalability

It is very hard to cost effectively design an on-premise system and that can quickly respond to peak demands and lower capacity when it’s necessary. On the cloud, all that is done automatically. The capacity automatically grows with the demand. Actually, most deployments throttle down the capacity to keep the costs in check. On the cloud, you only pay for the resources that you consume.

Reliability

Most cloud vendors have service-level agreements that guarantee 99% uptime and they have highly trained staff to address any issues immediately. The cloud vendors typically bear responsibility for backups and disaster recovery, which saves a lot of time and money.

Innovation

From a business growth perspective, the cloud brings limitless opportunities for organizations to innovate and expand services quickly. New services and workflows could be easily integrated with existing ones already running on the cloud. Most cloud vendors provide pre-built services for faster deployment and development thus saving time and money.

Availability

Cloud computing lets you and your team access cloud applications from anywhere in the world at anytime. Most cloud providers provide geographical deployment points for the services improving speed and availability. Since cloud services and applications can be accessed from anywhere, it can help companies to transition or offer remote work opportunities for employees.

Championed as ‘The next big thing in IT Operation’, AIOps uses artificial intelligence and machine learning to collect and perform real-time analysis on a system’s data. This can help system administrators to infer probable root causes for problems, potentially, alleviating them and keeping the system running smoothly and optimally.

Gartner defines AIOps as a platform that utilizes “big data, modern machine learning and other advanced analytics technologies to, directly and indirectly, enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight.” AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”

Nothing compares to an experienced engineer who knows the unique quirks and intricate nuances of a company’s IT estate, except maybe AIOps. Man’s superiority as a monitoring device is now being challenged by this real-time monitoring system that can learn and oversee a company’s hardware and network infrastructure, as well as the software running on it, so well that it can proactively advise on problems that are not only occurring but might be about to become problematic. In a predictive asset maintenance kind of way, AIOps can deliver control over chaos. AIOps can manage an IT system on a whole new level, a level that is far beyond the capabilities of man.

Why do you need AIOps? Well, today’s IT systems have become so incredibly complex and many of today’s IT monitoring tools are single-focused diagnostic tools that look only to the past and have no predictive capabilities. AIOps, however, can take in massive amounts of data from a multitude of systems, aggregate the data in real-time, then detect a cause and effect pattern that most humans would miss as well as offer solutions to potential problems before they become too costly or mission-critical.

The benefits of an organization utilizing AIOps are multifold.  First and foremost, a reduction in operational noise, which will help increase performance, and any analytical model more accurate. AIOps will bring organization to normally chaotic IT systems. ML algorithms within an AIOps system can capture data, meta-tag it, classify it, detect anomalies, then predict trends, determine causality, and then potentially heal the system. AIOps workflows can become part of the company’s ongoing system operational intelligence that can help keep problems proactively at bay. IT departments won’t be inundated with microservice alerts. Operations will get detailed information about potential issues that threaten mission-critical services. AIOps allows collaboration throughout the company.

Today, almost any system-level metric can be added as a variable in an AIOps analytical model, which can help build a root-cause analysis of potential issues. Overall, the business will get end-to-end visibility into their critical processes, which should help identify problems that might cause future incidents. One of the biggest benefits of AIOps is it provides insight into future events and therefore mitigates downtime.

Because of the complexity of modern IT systems, cause-and-effect is not always an easy thing to capture and/or correlate. However, AIOps can act as a situation room sitting atop a company’s IT infrastructure, standing at the ready to proactively squash any issue or problem that might arise. AIOps systems can even act in a self-fixing way. All-in-all, AIOps will help companies make better IT decisions. They will become more agile, more productive, more reactive, and their analytical models will be more accurate. It is AIOps to the IT operations rescue, and this probably won’t be the first time AI eclipses man.

In the world of IT, it seems that just about everything is being moved to the cloud. Cloud solutions like AWS, Azure, and many others provide companies with incredible benefits that simply aren’t possible with localized infrastructure. When evaluating whether the cloud is the right place for your business technology it is possible to pick and choose what is migrated, and when. For many companies, the best place to start is migrate your DevOps to the cloud. Cloud based software and app development will offer your organization some great benefits that make it a perfect fit. Take a few minutes to learn about some of the biggest advantages of migrating your DevOps to the cloud today.

Powerful Automation

While automation can be setup and used in any environment, it is natively available on most cloud-based DevOps platforms. For example, you can compile code, create test environments, run load tests on software, and much more automatically in this environment. It is also fast and easy to use automation to perform functions using multiple different configurations, which is a powerful tool for software development. If a developer is working on a web portal for customers, for example, they can automate tests that will use every version of every major web browser to ensure things function properly. Cloud based DevOps automation not only speeds things up, but produces better results as well.

Near Instant Setup of Test Environments

When it is time to test new software, you won’t have to manually access a specific server, manually install the software, and then begin the testing. With DevOps on the cloud, you just select what type of environment you need for testing, and the infrastructure is automatically created in seconds. This will provide you with a dedicated test environment where you can safely run any tests you need. It is even possible to have the cloud infrastructure create a test environment that simulates a production environment, so your testing is as accurate as possible.

Easy Collaboration

Modern software development is almost always a collaborative process. Business apps are often large and complex so it would not make sense to have just one person working on them alone. Cloud technologies are known for making it easy for people from distant geographic locations to access the same systems at the same time. For DevOps, this means you can have your development team working from anywhere that is convenient. Many companies will have some developers in an office, others working from home, and still others on an offshore team. Cloud based DevOps makes this collaboration easy and effective.

Pay for System Resources As You Need them

When developing or testing software, you need to have a platform on which it can run. For on premise DevOps, this means purchasing hardware (often multiple different setups) for these activities to run on. As anyone in IT knows, hardware is expensive and becomes obsolete in only a few years. When DevOps is on the cloud, you can stand up a new system instantly, and only pay for the specific resources you are using. To make it even better, you only have to pay for those resources while you are actually using them. Once you are done with a project or a test, simply stop using them and there will be no more cost. This feature of cloud DevOps alone will often cover any costs associated with a migration.

Rapid Deployment

For many organizations, this will be the biggest reason why software and app development should be migrated to the cloud. Once development has been completed and testing has been finished, the software can be deployed extremely quickly. If the software will be accessed on the cloud itself, the deployment will take just minutes to get it pushed out where it needs to go. Even if the software will be run locally or at an on premise datacenter, however, cloud DevOps makes it easy to push out software and updates anywhere they need to go. Many companies will even take advantage of continuous integration solutions such as Bamboo or Jenkins to handle the deployment automatically.

Make Cloud DevOps a Part of Your Digital Transformation

There is no doubt that cloud technologies will help to improve your overall DevOps strategy. Whether you are already planning on moving your systems to the cloud or not, transitioning your development is a smart move. It will help to speed things up, improve quality, and reduce the overall cost. On top of all of that, this type of migration is something that the developers themselves typically love.

Read more benefits about migrating to the cloud:  Eight Reasons Why Your Business Should be Migrating to the Cloud

In order for the transformation to be a success, the organization needs to come up with processes that will help to overcome conflict related to the digital vision. While this will certainly have technical aspects to it, the solutions must also include:

Creating a List of Digital Vision Goals

Conflicts on the digital vision of an organization often come down to various people or teams not accurately understanding the actual goals. For example, one team may be looking at their specific needs as the goal of a digital transformation. Instead, the actual digital vision should be focused on the needs of the business. What specific IT departments want in this process will simply be processes or procedures that are in place to help support the digital vision.

Create a list of goals for a digital vision right from the beginning so that there is no confusion. Some examples of vision goals that organizations have for a digital transformation include:

• Digital Security Compliance – A significant security breach can result in a loss of trust by customers, fines from governmental agencies, and other significant financial loss. Digital security should almost always be a key aspect of any digital vision.
• Agile Software Development – Your developers are almost certainly being asked to produce more apps faster than ever. Using Agile methodologies can help them to prioritize work based on business need.
• Cost Reductions – Reducing expenses is almost always an important part of a digital vision. This can be done through cloud migrations and other advancements.
• Improved Customer Experience – Creating a portal that your customers can use is a great way to improve their experience. A digital vision will often include specific details about how to improve the customer’s overall experience.
• 3rd party software to fit your needs – Software, such as Smart Field Forms are great ways to digitize workflows.  Or custom solutions leveraging cloud products.

Of course, these are relatively generic goals. A company will need to evaluate their own specific goals based on the industry they are in, their current technical configuration, and many other factors. Keeping the focus on the long term, ‘big picture’ goals will help to guide the entire digital transformation effort.

Designate a Digital Transformation Leader

While most decisions throughout a digital transformation will be made by teams of people, it is important to have someone who can make final decisions. Ideally this will be someone who either has experience with digital transformations or has a strong technical background as well as an understanding of the business.

Some companies will list the chief information officer as this individual. While this can certainly be a good option for mid-sized companies, larger organizations may want to choose someone who can focus their time on this project. The digital transformation leader should be intimately involved with the process so they don’t need to be brought up to speed whenever a decision has to be made. In addition, this person should not be a part of any specific team so that their decisions are not biased.

Create a Strategic Roadmap

Along with the digital vision for the organization, creating a strategic roadmap is also something that should be done early on in the process. In many cases, the obstacles in creating a digital vision will be similar to those of a roadmap. Getting everyone on the same page with this step will help to facilitate the rest of the transformation.

Developing a comprehensive strategic roadmap will provide guidance for various teams to take over the coming months. While it is good to have something written up, it is also important to remain flexible so teams can make adjustments as needed based on the rapidly changing environment.

While machine learning and AI have been around in various forms for quite a while, it is only in recent years that they have really been used for common day to day tasks. This is largely because these technologies are only now becoming affordable enough for the average business to take advantage of them. This can be done either by standing up internal systems to power AI (still typically reserved for large corporations), or more commonly, harnessing machine learning or AI through a cloud platform. For example, companies that use Amazon Web Services (AWS) as part of their cloud infrastructure can access the AWS AI systems as needed. With these tools available, those in DevOps need to understand how they can use machine learning and AI to take their jobs to the next level.

Automating Common Tasks 

Developers spend a large portion of their day performing repetitive and mundane tasks that are associated with the main goal of developing software. In addition to being tedious tasks, they are often places where human error can cause problems. For example, AI tools can analyze code for many different types of errors and automatically correct them. As any developer knows, it is not at all uncommon for something as small as forgetting to close a bracket to cause significant problems. AI tools not only identify these types of common errors but can accurately fix them without developer intervention.

Helping to Create More Efficient Code

Another area where AI and machine learning can help with the DevOps process is by helping to identify (and in many cases, fix) inefficient code. When working on complex projects that go through years of updates and patching, it is not at all uncommon for even the best developers to use inefficient code. While this typically won’t cause a program to fail, it can increase run times and increase the number of lines of code quite significantly. Using machine learning, the system can actively analyze code for inefficiencies, such as coding in a process multiple times rather than making a call for a subroutine. Depending on the developer’s preference, this can either be automatically fixed (in some cases) or the developer can be alerted so they can determine the best course of action.

More Robust Testing for Cleaner Releases 

Testing code can be an extremely time consuming and difficult process. It is not enough to simply run a new program to confirm it works. To the extent possible, developers need to go through and perform every conceivable task that an end user would perform to see if it works. In addition, this should be done in multiple different environments to make sure there aren’t any conflicts or other problems that shouldn’t exist.

Rather than doing this manually, or worse, publishing software for users to access as a form of testing, an artificial intelligence system can do it for you. AI can run millions of simulations across thousands of simulated environments in the amount of time it would take an individual to run just a few. This type of deep testing will dramatically increase the end user experience with new software, software patches, or updates to existing programs.

Discover End User Needs

Understanding the needs of the end user is one of the most important parts of DevOps. While asking users what features they need or what problems they run into is a good idea, it is not always fruitful. End users typically don’t really know what is possible for developers, so they either don’t know what features to ask for or they ask for something entirely outside the scope of a given system. Using machine learning, it is possible to gather massive amounts of data on the activities that end users are doing with a program. This will allow developers to incorporate features tailored to their exact needs.

Machine learning and Artificial Intelligence are revolutionizing many aspects of technology. Those who work in DevOps need to make sure that they are taking full advantage of this rapidly advancing technology. The more that it is used, the more effective it will be at helping developers push out the best software possible.