9 advanced IT certifications for operations experts

Certified in the Governance of Enterprise IT (CGEIT), ISACA

Compliance and security are critical for every enterprise, as well as the individuals who can implement and maintain them.

One option to prove your compliance and security skills is the CGEIT certification. This advanced-level IT certification proves you are knowledgeable in enterprise IT governance, IT resources, benefits realization and risk optimization.

This certification is a two-part process. The first part is passing the exam. Second, within five years of the exam, candidates must submit at least five years of work experience in a role that supports enterprise IT governance to ISACA.

Certified Technical Specialist, The Open Group

To receive this certification, IT technicians must demonstrate their skills through written applications and peer reviews. In this process, applicants must showcase their core skills, technician-specific skills and experience.

Per The Open Group, recipients of this certification at the highest level are experts in system design and implementation, weighing factors such as security, interoperability and manageability. They also learn and apply new techniques and technologies, which they must demonstrate in the interview process.

Certifications are broken into three levels, with Level 3: Distinguished Technical Specialist as the highest tier. For a distinguished certification -- the Level 3 certification -- The Open Group recommends applicants have three out of the last five years of experience in their specialization and five years of work experience out of the last eight.

CompTIA Server+, CompTIA

The location of servers might change from on premises to cloud, and even back again, so this certification is for individuals such as sys admins, data center technicians, IT technicians and data center engineers, and proves they can work in any environment.

The exam covers topics such as high availability, cloud computing and scripting, and processes such as how to install, manage and troubleshoot servers, regardless of the environment. Other skills learned include:

• how to manage and maintain OS configuration, access control and virtualization;
• disaster recovery and backup; and
• how to troubleshoot various issues in various production resources, such as hardware, storage and security.

Linux Foundation Certified Engineer (LFCE), The Linux Foundation

The Linux OS is one of the most supported OSes, with hundreds of available distributions. However, the design of these Linux distros follows a modular design. The LFCE certification shows you have command of these elements.

Expect to be evaluated based on command-line tasks and your skill with on-the-job simulations. Passing the exam proves your mastery of different Linux topics, such as user and group management, service configuration, and system design and deployment, and that you can deploy and configure the Linux OS at an enterprise level.

As an advanced IT certification, the recommended Linux experience for applicants is three to five years.

Microsoft Certified: DevOps Engineer Expert, Microsoft

As DevOps engineers know, the DevOps methodology requires a commitment to the culture and processes. The expert DevOps engineer certification from Microsoft confirms knowledge of, and expertise with, DevOps.

Candidates should expect questions on:

• instrumentation;
• site reliability engineering (SRE);
• security and compliance;
• source control management;
• communication and collaboration; and
• CI/CD.

In addition to work experience, applicants for this certification must be either a Microsoft Certified: Azure Administrator Associate or a Microsoft Certified: Azure Developer Associate.

Professional Cloud Architect, Google Cloud

As cloud adoptions rise, more organizations will need individuals familiar and skilled with cloud architecture. In addition to the rest of your resume, this certification shows skill with Google Cloud.

Specifically, this advanced IT certification proves you can design, manage and provision cloud architecture; meet security and compliance requirements; analyze and optimize technical and business processes; and ensure reliability.

While there are no prerequisites for the certification, it is recommended that candidates have at least three years of industry experience and one year of design and project management practice with Google Cloud Platform.

Red Hat Certified Engineer, Red Hat

For IT sys admins, or IT pros who work in DevOps environments, becoming a Red Hat Certified Engineer shows they can manage multiple systems and automate, at least, part of their workload.

The practical exam requires applicants to use Red Hat Ansible Engine and Red Hat Enterprise Linux 8 to perform real-world tasks. This certification recognizes individuals can:

• perform sys admin tasks, such as create and configure file systems;
• understand core Ansible components, such as variables and playbooks;
• create simple shell scripts; and
• work with Ansible roles.

Applicants should already have experience as a sys admin.

Site Reliability Engineering: Measuring and Managing Reliability, Google Cloud

Site reliability engineering is no longer just a Google specialty. More enterprises are adopting SRE practices, which means more staff should know how to implement them.

This course and certification from Google Cloud teaches people about service-level indicators (SLIs), service-level objectives and service-level agreements; how to make systems reliable; and how to quantify the risks to -- and consequences of -- SLOs.

Certification holders can develop SLOs for their own organizations and use SLIs to measure reliability and error budgets.

Machine Learning Engineering for Production (MLOps) Specialization, DeepLearning.AI

At an advanced level, comprehension of machine learning isn't enough: You must also be able to use it in production environments.

This certification from DeepLearning.AI verifies you can complete each component necessary to run a machine learning model in production. For example, it validates you can:

• create a model baseline;
• gather, clean and validate data sets to build data pipelines;
• track data changes with data schemas; and
• use analytics to bottlenecks.

Before you begin this certification, you should already have some knowledge of AI and deep learning, intermediate Python skills and experience with a deep learning framework, such as PyTorch, Keras or TensorFlow.