As generative AI continues to grow, implementing and managing the right infrastructure becomes even more critical to ensure the secure and efficient development and deployment of AI-based solutions.
To meet these needs, NVIDIA has introduced two professional-level certifications that offer structured paths for infrastructure and operations practitioners to enhance and validate the skills needed to work effectively with advanced AI technologies.
The certification exams and recommended training to prepare for them are designed for network and system administrators, DevOps and MLOps engineers, and others who need to understand AI infrastructure and operations.
NVIDIA's certification program equips professionals with skills in areas such as AI infrastructure, deep learning and accelerated computing to enhance their career prospects and give them a competitive edge in these high-demand fields.
Developed in collaboration with industry experts, the program features relevant content that emphasizes practical application alongside theoretical knowledge.
The NVIDIA-Certified Professional: AI Infrastructure certification is designed for practitioners seeking to showcase advanced skills in deploying AI infrastructure. Candidates must demonstrate expertise in GPU and DPU installation, hardware validation and system optimization for both AI and HPC workloads. The exam also tests proficiency in managing physical layers, configuring MIG, deploying the NVIDIA BlueField operating system, and integrating NVIDIA's cloud-native stack with Docker and NVIDIA NGC.
To prepare for this professional-level certification, candidates are encouraged to attend the AI Infrastructure Professional Workshop. This hands-on training covers critical AI data center technologies, including compute platforms, GPU operations, networking, storage solutions and BlueField DPUs. The workshop is recommended for professionals aiming to elevate their AI infrastructure expertise.
The NVIDIA-Certified Professional: AI Operations certification is tailored for individuals seeking proficiency in managing and optimizing AI operations. The exam tests expertise in managing AI data centers, including the use of Kubernetes, Slurm, MIG, BCM, NGC containers, storage configuration and DPU services.
To prepare for this professional-level certification, candidates are encouraged to attend the AI Operations Professional Workshop, where they will gain hands-on experience in managing AI data centers, including compute platforms, networking, storage and GPU virtualization. The workshop also provides practical experience with NVIDIA AI software and solutions, including NGC containers and the NVIDIA AI Enterprise software suite, making it ideal for professionals looking to deepen their AI operations expertise.
Both of these professional-level certifications build upon the foundational knowledge covered in the NVIDIA-Certified Associate: AI Infrastructure and Operations certification.
Additional NVIDIA certifications include:
NVIDIA-Certified Associate: Generative AI LLMs - validates skills in using generative AI and large language models
NVIDIA-Certified Associate: Generative AI Multimodal - focuses on multimodal AI content creation
Saleh Hassan, an embedded software engineer at Two Six Technologies, successfully completed three NVIDIA certification exams at the NVIDIA AI Summit in Washington, D.C., earlier this year.
The knowledge I gained has definitely made me a better developer when it comes to integrating AI, said Hassan, who encourages others to pursue certifications as a key milestone for advancing their AI careers.
Saleh Hassan showing off one of his NVIDIA certifications. All NVIDIA certifications are part of a comprehensive learning path that offers foundational courses, advanced training and hands-on labs to thoroughly prepare candidates for real-world applications.
The certifications support individual career growth and organizations can use them to enhance workforce capabilities.
Explore the options on the NVIDIA Certification portal and sign up for NVIDIA's monthly newsletter to stay updated on the latest offerings.
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As generative AI continues to grow, implementing and managing the right infrastructure becomes even more critical to ensure the secure and efficient development and deployment of AI-based solutions.
