At this point, it becomes quite clear that DevOps can bring significant value to virtually any organization that relies on technology to deliver products and services to its customers.
This is especially true in the scenario presented in this module, where legacy operations slowed the pace of innovation and increased the risks associated with software deployment and maintenance.

To better understand the positive impact of DevOps, let’s take a closer look at its goals and benefits.

What are the main goals and benefits of DevOps?

Adopting a DevOps culture can offer numerous benefits to organizations, including:

• Faster time to market:
  Implementing DevOps practices facilitates rapid release of software and updates, reducing the time needed to deliver them to users.
  This speed improves the organization’s responsiveness to changing customer needs and market trends.
• Adaptation to market dynamics and competition:
  DevOps promotes an agile and responsive development process, enabling organizations to quickly adapt to market changes and respond to competitive pressure.
  This adaptability ensures the company remains aligned with customer expectations.
• Maintaining operational stability and reliability:
  DevOps principles encourage collaboration between development and operations teams, leading to more stable and reliable operational environments.
  This collaboration helps identify and resolve issues earlier in the development cycle, reducing disruptions.
• Reducing Mean Time to Recovery (MTTR):
  DevOps practices rely on automation, continuous monitoring, and fast feedback loops, allowing problems to be detected and resolved more quickly.
  This significantly improves MTTR, minimizing the impact of outages and enabling a rapid return to optimal state.

Are DevOps benefits limited to traditional applications?

It is important to note that DevOps benefits go beyond the traditional application lifecycle.
Its scope includes, for example:

• Machine learning, applying operations to the deployment of machine learning models
• Infrastructure, with the Infrastructure as Code (IaC) approach for deploying underlying platform services
• Machine learning operations, often called MLOps, which focus on the transition from model development to production deployment.

By following MLOps practices, organizations can ensure their workloads are resilient and easily reproducible.
This includes monitoring, retraining, and redeploying models as needed, while maintaining stability after each deployment.

Infrastructure as Code (IaC) consists of managing and provisioning computing resources (compute, network, and storage) using programming methods rather than manually configuring physical hardware or using interactive administration tools.

IaC enables developers and operations teams to define and manage infrastructure components such as servers, network devices, and databases using code. This code is typically written in a high-level, human-readable scripting language. The scripts are then executed to automate the provisioning and configuration of the infrastructure, making it more efficient, consistent, and easily scalable.