Leveraging IaC (Infrastructure as Code) in Business 2023

Imagine a world where setting up and managing complex infrastructure becomes as simple as writing code. This is the premise for Infrastructure as Code (IaC), which is a concept that has gained tons of traction recently as it’s changing the way we manage and deploy infrastructure. The way it works is that it defines and manages infrastructure using code as opposed to manual configuration, which means everything - from servers and networks to databases and configurations - can be described and provisioned through code.

Business Setup with Infrastructure as Code

To give you an example of how it works in a business environment, consider the setup stage of a new application. It’s a process that’s traditionally been very tedious, but with this approach, developers can work with infrastructure experts to articulate the necessary resources and configurations through code. This code - written in languages like Terraform or CloudFormation - becomes the ultimate reference for infrastructure setup.

This all happens through parameters such as server specifications, networking rules, load balancer settings, and database configurations which are well-defined within the codebase. The code's inherent version control allows us to track changes over time. This comes in especially handy when we need to replicate the setup for testing, staging, or scaling purposes. In fact, replicating environments becomes as simple as executing a piece of code.

IaC Scalability

Back to that, we can’t overlook the scalability aspect. Think about when an app sees a surge in demand with a conventional setup, one where infrastructure is managed manually. Often, this would entail downtime and manual adjustments which would interfere with the user side and the app's reputation for reliability. 

In IaC the entire infrastructure setup is encapsulated in code, detailing every component's configurations and interdependencies. When demand surges, a mere adjustment to the code initiates actions; automated provisioning spins up new instances, load balancers allocate traffic, and network configurations adjust on the fly. All of this happens in a controlled and rapid manner, with minimal to no disruption to users.

What Does IaC Do For DevOps?

IaC plays a crucial role in adopting DevOps methodologies and enabling continuous integration/continuous delivery (CI/CD). IaC alleviates much of the provisioning burden from developers, who can now execute scripts to prepare their infrastructure. Consequently, application deployments no longer hinge on infrastructure setup, and system administrators are freed from managing any time-consuming manual tasks.

CI/CD hinges on perpetual automation and uninterrupted monitoring throughout the application's lifecycle – from integration and testing to delivery and deployment. However, automation relies on uniformity. Inconsistent deployment and configuration practices between development and operations teams can undermine the deployment of the automated application.

The DevOps ethos thrives on the synergy between development and operations, mitigating errors, manual interventions, and disparities. IaC becomes a bridge between these teams, offering a unified description of application deployment that aligns with DevOps principles.

This uniformity extends to all environments, including production. IaC ensures that every deployment replicates the same environment consistently, eliminating the need for unique configurations that are hard to reproduce manually. 

Developer Tools for IaC

Let's delve into some of the key IaC tools:

Terraform

This is a cornerstone of IaC, Terraform enables you to define infrastructure as code using its declarative syntax. Whether you're managing cloud resources, networks, or databases, Terraform simplifies everything from provisioning to updating with a single command. Also, its open-source and Terraform Cloud options are flexible. 

AWS CloudFormation

For those within the Amazon Web Services ecosystem, AWS CloudFormation provides a dedicated path to IaC. It lets you define AWS resources using templates, reducing manual management and enhancing consistency across your cloud infrastructure.

Google Cloud Deployment Manager

Google Cloud Deployment Manager revolves around declarative YAML formats and supports Python as well as Jinja2 templates. By employing these tools, you can configure and reuse resources with ease, fostering efficiency and reducing redundancy.

Crossplane

Crossplane integrates well with Kubernetes, enabling you to manage infrastructure across clouds, clusters, and on-premises environments. Simply put, it’s an open-source tool that extends Kubernetes' power to infrastructure orchestration.

Vagrant

If you’re focused on the development environment, Vagrant is going to be your top choice. By defining environments as code, developers can share setups across team members, ensuring that everyone works in the same development environment. 

Spacelift

Spacelift is a developer-centric CI/CD solution tailored for cloud-agnostic Infrastructure as Code. It revolves around the concept of managing infrastructure as code, and of course, automating the management of your infrastructure setups. With Spacelift, you can integrate IaC into your workflows pretty easily, ensuring consistent infrastructure deployment while focusing on your core development tasks.

The Takeaway

IaC empowers development teams to focus on core tasks while automating infrastructure management. With the ability to replicate environments consistently, businesses can ensure deployments are reliable and effective.

To stay ahead in the rapidly evolving technology landscape, businesses have got to consider adopting IaC and exploring the available tools that best suit their needs. By implementing IaC, businesses can optimize their infrastructure management and accelerate their digital transformation journey. Does this sound interesting? We can help you get started on this journey by identifying any deficiencies in your current workflows. Check us out today and initiate your transformation with us.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

Using AI to Enhance Cloud Computing

Cloud computing has become an integral part of the modern business landscape since it provides platforms with the ability to scale. As the reliance on cloud infrastructure grows, ensuring its reliability and availability becomes paramount. This is where Artificial Intelligence comes in and helps create these ideal platforms.

The relationship between AI and cloud computing is symbiotic, where AI enhances the infrastructure in terms of what the cloud can do and how reliable it is and cloud computing gives AI the resources and environment it needs to thrive. Here’s a quick example of how this works: 

If you woke up one day as the Chief AI Officer at Amazon, and they said to you “We need you to scale our AI capabilities to meet the increasing demand and ensure our cloud infrastructure remains reliable," where would you start?

Commonly, at the root of these limitations in a platform is the level of demand that it encounters which puts major emphasis on areas such as resource management. As a Chief AI Officer, you’d want to first assess what isn’t being optimized, which, if scalability and reliability are in question, means that something is underutilizing the cloud. With that in mind, these are some resources that might go into remediating the issue:

Forecasting demand: Based on Amazon’s user usage patterns, you can predict workloads and have the system allocate resources as needed. When it comes to underutilizing the cloud, it's best to implement an auto-scaling mechanism like AWS Auto Scaling that is meant to ensure the right amount of computing power is given consistently with fluctuations in demand. 

Predictive maintenance: The cloud infrastructure is a very complex system with so many different interconnected components and dependencies. For this reason, you’ll want systems that know when issues are going to happen before they do. You can have the algorithms analyze data from sensor readings, server logs, or even performance metrics, the idea is that the system recognizes patterns and can anticipate potential issues.

That’s a glimpse at the reliability side, but now we need to address scalability more in-depth:

Edge computing: As a Chief AI Officer, edge computing is going to stand out as a crucial aspect when addressing scalability. It introduces a paradigm shift in how both data is processed and services are delivered, and it plays a fundamental role in optimizing the cloud's infrastructure. Through edge nodes, AI algorithms will process at the source, minimizing the need for data transmission to centralized cloud servers.

Hybrid and Multi-cloud: When implemented with AI, a hybrid and multi-cloud strategy can be great for distributing workloads, in addition to aiding in what we looked at with predictive maintenance and demand forecasts.

Cloud Computing meets AI and ML

Everything we’ve looked at so far is still scratching the surface in terms of leveraging AI in the cloud. With what we know about the cloud infrastructure, this is a quick look at some tools to watch for:

AI-Driven Auto-scaling: Optimizes resource allocation based on real-time demand patterns.

AI-Enabled Network Optimization: Reduces latency and manages traffic in large-scale cloud environments.

AI-Powered Predictive Analytics: Anticipates workloads and performance issues.

AI-Enhanced Security: Identifies and responds to real-time threats, improving cloud security.

Federated Learning: Allows decentralized machine learning across multiple cloud servers while preserving data privacy.

AI-Driven DevOps: Automates testing, code optimization, and deployment.

Quantum Computing Integration: Uses advanced computational power to quickly solve problems.

Explainable AI: Enhances interpretability of complex AI systems.

AI-Optimized Cost Management: Recommends cost-saving strategies based on cloud usage.

AI-Driven Natural Language Processing (NLP): Improves user experiences with cloud services by understanding natural language queries.

Tech Stacks to Support Integration

You want to leverage AI as effectively as possible in the cloud. To do that, you need a tech stack that prioritizes the following components:

AI frameworks and libraries: Get hands-on with TensorFlow, PyTorch, and Scikit-learn. They offer awesome pre-built algorithms for tasks like image recognition and natural language processing.

Cloud platforms: AWS, Azure, GCP – know your way around them! Get familiar with virtual machines, containers, and serverless computing for scalable AI apps.

Big Data tools: Don't shy away from Apache Spark, Hadoop, or Kafka. These are going to be your best friend for handling massive data sets. 

Containerization: Docker and Kubernetes are your pals. Use them to package and deploy AI models.

Edge computing infrastructure: We mentioned it before but make sure to design edge nodes for local data processing and real-time responsiveness.

Hybrid and multi-cloud management: Learn how to balance workloads across different clouds and on-premises infrastructure.

Security and compliance tools: Stay on top of encryption, access controls, and monitoring to safeguard data.

Data storage solutions: Amazon S3, Google Cloud Storage, and Azure Blob Storage are your data allies.

Real-time data processing: Master Apache Kafka or AWS Kinesis for streaming data handling.

Monitoring and analytics: Set up Prometheus, Grafana, or CloudWatch to keep an eye on AI model performance and resource usage.

Moving Forward With AI in The Cloud

AI-driven decision-making is the future of the cloud landscape. As we've explored, the synergy between AI and cloud computing is the cornerstone of next-gen platforms. To scale AI effectively in the cloud, you’ve got to be able to navigate a dynamic landscape that merges the potential of AI with the scalability of cloud computing. This is where experts come in and help, often saving you time and money that would be far better spent on scaling the solution than just trying to figure it out. 

If you think this is something your company could benefit from, and you’d like to learn less about the why and more about the how, reach out to us so we can get you started on the right foot.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

How You Can Develop an App with AI Technologies

Building a mobile app is traditionally a six-figure job and requires anywhere from 3 - 10 experienced developers. However, recognizing the dramatic shift the world is facing when it comes to strategizing and innovation, integrating AI in app development is becoming important for three reasons: accessibility, efficiency, and innovation. 

So what’s changed? 

Well, if we compare what we had when creating apps 5 years ago to what we have now with AI, we can see several key changes such as:

Advancements in AI Frameworks: AI frameworks and tools have made it easier for developers to integrate AI functions into their mobile apps. These frameworks allow for the implementation of machine learning, natural language processing, computer vision, and so on. The best part; using them doesn’t require extensive expertise in AI.

No-Code/Low-Code Platforms: No-code and low-code platforms have gained popularity over recent years, which makes it easy to create functional mobile apps. These platforms often come equipped with pre-built AI components that can easily be integrated into the app.

AI as a Service (AIaaS): Cloud-based AI services offer simple APIs that developers can leverage when adding AI functionalities to their apps. These AIaaS platforms handle the training and deployment of AI models, which reduces the complexity behind developing AI-powered applications.

User Experience: Integrating AI in mobile apps allows for interactive and personalized user experiences. This is made possible through AI-driven features, such as recommendation systems, chatbots, and voice assistants.

Competitive Edge: As more companies adopt AI in their mobile apps, you’ll see that it’s become a huge advantage. Businesses can gain insights from user data, then make improvements in app performance, and ultimately use that to stay ahead of the competition in a market evolving so quickly.

Scalability: Automation streamlines various aspects of app development, which reduces the need for big teams of developers as well as the development time. The amount which one developer can accomplish is amplified with AI tools and can also optimize resource usage.

Integration with IoT and Big Data: AI's ability to process and analyze vast amounts of data is particularly valuable when it comes to IoT and Big Data. AI-powered mobile apps can make sense of data that’s been collected from devices and sources which creates new opportunities for innovation and of course data-driven decision-making.

Using AI to Build My App

Getting started: AI frameworks like TensorFlow and PyTorch will give you the documentation to learn the syntax, functions, and usage of different components within the framework when integrating machine learning models into your mobile app. These frameworks have pre-trained models for common tasks like image recognition and sentiment analysis, which is important because again, it makes it easier to incorporate AI functionalities without expertise. 

For developers looking to create apps without in-depth coding knowledge, no-code and low-code platforms like Appgyver and Bubble are great choices. These are going to come with AI plugins and integrations, which will allow developers to drag and drop AI components into their apps. You can create an entire website and mobile app in a matter of hours, and plug-ins are at your fingertips for just about anything you could want your app to have. 

Also, using AIaaS platforms like AWS AI services or Google Cloud AI can further simplify the development process. Developers can integrate these APIs into their apps and leverage the AI without having to think too much about the underlying infrastructure.

In the middle: Now, during the design phase, developers can employ design tools such as Adobe Sensei and Figma's Smart Selection, which create interfaces that are both aesthetic and user-friendly. These tools use AI algorithms that suggest improvements to the layout, that generate design prototypes, and that adapt designs based on feedback from users. 

When testing the app, AI testing frameworks like Testim and Appvance AI Testing can automate test case creation and execution, which will help identify potential issues. For personalization and recommendation features, you can use AI-based recommendation engines like Amazon Personalize or Google Recommendations AI. 

At the end: Now, when you deploy and monitor the app, DevOps tools like Jenkins X or GitLab Auto DevOps will help automate the deployment process, monitor the app's performance, and detect anomalies you should be aware of. 

Traditional Methodology Comparison

If you compare everything we just outlined to traditional methods which require big budgets, teams of experienced developers, and months or even years until an app is complete, it's clear that code generators and low-code platforms alone change the landscape for app development forever. 

Businesses and developers can have cost-efficiency, faster development cycles, and access to a broader audience which were seemingly unattainable with traditional methods. Let’s say for instance a FinTech company wants to develop a mobile app as an extension of the services they offer on their website. In a world without AI, you'd have to hire experienced developers, UI/UX designers, and domain experts. The development timeline could again span months or even years which would delay market entry time and make scalability a bigger concern.

Now, if that same FinTech company wants to do this in today’s world, you could likely do this with one or two people in a matter of weeks. Though you certainly could bring in a project manager, frontend and backend developers, UI/UX Designer, AI Specialist/Data Scientist, and a QA Tester if you have the means or require the oversight. Nonetheless, the development landscape remains focused on keeping the process quick and painless.

The Takeaway

Finding ways to leverage AI in processes your business may have followed for years isn’t easy, but it’s necessary in 2023. App development is a great example of how AI is transforming the landscape, but it’s just barely scratching the surface in terms of the kind of change AI will bring to you and your business. To navigate this, companies must look to consult with AI architects who can identify opportunities for AI integration and develop tailored strategies for implementation.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.