Instructor Qualifications For NVIDIA Training Workshops
Follow the links below to ensure you have the qualifications necessary for the workshop you are interested in.
NVIDIA DLI Workshops
Accelerated Computing
- Accelerating CUDA C++ Applications with Multiple GPUs
- Fundamentals of Accelerated Computing with Modern CUDA C++
- Fundamentals of Accelerated Computing with CUDA Python
- Scaling CUDA C++ Applications to Multiple Nodes
AI Fundamentals
Data Science
- Enhancing Data Science Outcomes with Efficient Workflows
- Fundamentals of Accelerated Data Science
- Building AI Based Cybersecurity Pipelines
Deep Learning
- Applications of AI for Anomaly Detection
- Applications of AI for Predictive Maintenance
- Building Conversational AI Applications
- Computer Vision for Industrial Inspection
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
- Model Parallelism: Building and Deploying Large Neural Networks
GenAI/LLM
- Adding New Knowledge to LLMs
- Building Agentic AI Applications with LLMs
- Building LLM Applications with Prompt Engineering
- Building Multimodal Pipelines With Large Language Models
- Building RAG Agents with LLM
- Deploying RAG Pipelines for Production at Scale
- Generative AI with Diffusion Models
- Rapid Application Development Using Large Language Models(LLMs)
Physical AI
- Bootstrapping Computer Vision Models with Synthetic Data
- NVIDIA Isaac for Accelerated Robotics
NVIDIA Academy Workshops
General Teaching Requirements
All candidates must demonstrate teaching experience, such as:
- Classroom or virtual teaching experience delivering technical content to network or system professionals.
- Significant presentation experience in instructor-led settings, including remote delivery via platforms like Teams or WebEx.
- Ability to facilitate hands-on labs and guide troubleshooting exercises in a virtual environment
Candidates must be able to effectively communicate complex technical concepts, adapt to varying learner skill levels, and foster an interactive, hands-on learning environment aligned with NVIDIA’s training standards.
Follow the links below to ensure you have the qualifications necessary for the workshop you are interested in.
- Cumulus Linux Administration
- Spectrum-X Networking Platform Administration
- AI Infrastructure
- AI Operations
Accelerating CUDA C++ Applications with Multiple GPUs
Instructor qualifications:
Candidates must demonstrate significant experience with multiple CUDA-accelerated applications in the past, either in a professional or meaningful academic scenario, and be able to explain their work with these applications. These applications should involve the use of multiple GPUs and concurrent streams.
- How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
- The specifics of optimization strategies that the applications use
- Specific CUDA-related technical challenges that arose while developing the applications
Candidates should have the following:
- Advanced CUDA C++ experience.
- Mastery of multiple techniques for performing copy/compute overlap in single and multiple GPU applications, and the ability to discuss them clearly in detail and at length.
- Experience with NSight Systems.
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Adding New Knowledge to LLMs
Instructor qualifications:
Candidates must demonstrate experience working on at least one LLM application involving finetuning, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
Candidates should have experience with the following:
- Differentiating RAG, finetuning, and alignment
- Strategies to drive creation of diverse synthetic data sets
- Parameter efficient finetuning
- Pruning techniques
- Distillation techniques
- LLM decoding strategies, such as top-k/p, beam search, etc.
- LLM output evaluation techniques, such as ROUGE/BLEU, semantic similarity, LLM-as-a-judge, etc.
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Applications of AI for Anomaly Detection
Instructor qualifications:
Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- A role as a major contributor to a project that used Deep Learning
- A role as a major contributor to a project that used other Machine Learning techniques
- A role as a major contributor to a project that required data science
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Candidates should also have the following:
- Professional Data Science Experience using Python
- A working understanding of NVIDIA RAPIDS
- Significant experience in machine and deep learning, specifically the use of XG Boost, AutoEncoder, and GAN models
- Exposure to the telecommunications industry and cybersecurity, specifically networking and the threat of network intrusion.
Applications of AI for Predictive Maintenance
Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Deep Learning for time-series data, work/research experience with variations of auto-encoder models, recurrent models (LSTMs) and GANs.
- Measures of model accuracy, preferably in the context of industrial applications.
- Familiarity with machine learning techniques. Having a thorough understanding of XGBoost algorithm is crucial to the success of course delivery.
- Minimum of one deep learning library. Keras and TensorFlow are preferred
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Candidates should also have the following:
- Familiarity with deep learning concepts (At minimum level, having knowledge of artificial neural networks)
- Python (and common python libraries used in DL, e.g., numpy, pandas, sklearn,...)
- Having knowledge of TensorFlow and keras
Bootstrapping Computer Vision Models with Synthetic Data
Instructor qualifications:
Candidates must demonstrate experience working on at least one agentic AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
Candidates should have experience with the following:
- Omniverse Replicator
- Automatic label annotating
- Domain randomization
- Convolutional neural networks
- Deep learning model training, including transfer learning
- Hyperparameter optimization techniques
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Building Agentic AI Applications with LLMs
Instructor qualifications:
Candidates must demonstrate experience working on at least one agentic AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
Candidates should have experience with the following:
- Modern LangChain, including LCEL, LangGraph, etc.
- Differentiating capabilities of various agentic tools such as LangGraph, CrewAI, Autogen, etc.
- Stateful LLM systems
- LLM tool calling
- Strategies to prevent derailing
- Agentic routing
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Building AI Based Cybersecurity Pipelines
Instructor qualifications:
Candidates must have professional experience in the domain of defensive cybersecurity and data analysis. Candidates should be able to discuss their work as it relates to topics such as:
- Methods and tooling used in service of defensive cybersecurity for data collection, preparation, analysis, storage etc.
- Approaches to defending and resolving common cybersecurity attacks such as DOS, phishing, hijacked accounts etc.
- Effective data analysis through the use of machine and deep learning models like XGBoost.
- The use of GPU-accelerated libraries for use in data analysis, particularly those found in NVIDIA RAPIDS Forest Inference library.
- Familiarity with the structure of applications using NVIDIA Morpheus (pipelines, stages, modules, messages, etc.).
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience - in person or remote
- Significant presentation experience
Building Conversational AI Applications
Instructor qualifications:
Candidates must demonstrate experience working on at least one conversational AI application using automatic speech recognition (ASR), natural language understanding (NLU), and/or text to speech (TTS), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- A professional role (Ex: Engineer, Data Scientist) on a conversational AI project that used an ASR model to transcribe spoken language and process it
- A completed conversational AI project for a virtual assistant application
- Academic coursework in conversational AI using neural networks
Candidates should have the following:
- Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
- Experience using TAO Toolkit and Riva
- Basic Linux command line experience
- Experience using Docker
- Experience using Helm Charts and Kubernetes
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience - in person or remote
- Significant presentation experience
Building LLM Applications with Prompt Engineering
Instructor qualifications:
Candidates must demonstrate experience working on at least one LLM application using a programmatic interface, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
Candidates should have experience with the following:
- Development in Python, including an understanding of Pydantic
- Modern LangChain, including LCEL, LangGraph, etc.
- LLM next token prediction decoding methods
- How LLM models are developed (pretraining, alignment, instruction-tuning, etc.)
- LLM prompting techniques (iterative, zero/one/few-shot, chain of thought, etc.)
- Agents using tools, such as ReAct
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Building Multimodal Pipelines With Large Language Models
Instructor qualifications:
Candidates must demonstrate experience working on at least one generative AI application incorporating inputs of multiple modalities, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
Candidates should have experience with the following:
- Details of implementing contrastive pretraining
- Techniques for combining embeddings of various modalities such as VLM projections
- Tools for chunking documents including text, titles, figures, charts, tables, etc.
- Graph RAG and associated technology such as knowledge bases, cypher queries, etc.
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Building RAG Agents with LLMs
Instructor qualifications:
Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Active open-source contribution or coordination efforts in the area
- Experience orchestrating dialog management and information retrieval systems
- Strong applied software engineering expertise, esp. surrounding microservices and inference server solutions
Candidates should have the following:
- Strong proficiency in Python, including functional programming and server deployment
- Expertise in large language models as inference endpoints, including industry use-cases.
- Strong experience with modern LangChain (including LCEL) and LangServe required; understanding of LangGraph, LlamaIndex, Langsmith, and NeMo Guardrails useful.
- Experience with microservice/server orchestration, including Docker and FastAPI.
- Experience with modern RAG, including some derivative formulations and pros/cons.
- Understanding of agentic behavior, tooling, and modular agent components.
- Intuition of evaluation metrics and performance expectations.
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Building Transformer Based Natural Language Processing Applications
Instructor qualifications:
Candidates must demonstrate experience working on at least one Natural Language Processing application using a Transformer-based architecture (such as BERT), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- A professional role (Ex: Engineer, Data Scientist) on an NLP project that used a Transformer-based architecture
- A completed NLP project that used a Transformer-based architecture
- Academic coursework in NLP Transformer-based networks
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience - in person or remote
- Significant presentation experience
Candidates should also have the following:
- Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
- Basic pandas and NeMo competency
- Experience using NVIDIA Triton Inference Server
Computer Vision for Industrial Inspection
Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Using Deep Learning techniques to tackle classification problems, preferably in the context of industrial applications.
- A professional role on a computer vision project that used Deep Learning techniques.
- Significant coursework in Deep Learning for computer vision that covers the various stages of the development workflow.
Candidate should have the following:
- Python (and common python libraries used in DL, e.g., numpy and pandas)
- Familiarity with end-to-end machine learning workflow
- Familiarity with manipulating data using pandas DataFrame
- Familiarity with deep learning concepts including knowledge of convolutional neural networks
- Familiarity of at least one deep learning framework (Keras and TensorFlow are preferred)
- Familiarity with metrics such as accuracy and inference performance
- Familiarity with command-line interface and basic linux commands
- Familiarity with transfer learning and fine-tuning models
- Knowledge of NVIDIA’s DALI, TAO Toolkit, TensorRT, and Triton Inference Server
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Deploying deep learning training workloads to multiple GPUs and preferably multi-node clusters
- Data Parallel approaches to distributed Deep Learning
- Profiling and optimizing the deep learning code
- Using NGC containers
- Experience in building neural networks with PyTorch
- Using PyTorch DDP to deploy distributed training
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Candidate should have the following:
- Good understanding of the literature discussing implications of training deep neural networks with large batches. In particular, a good understanding of the LARS/LARC algorithm.
- Understanding of the process used in training deep neural networks. In particular understanding of the Stochastic Gradient Descent and backpropagation algorithms.
Deploying RAG Pipelines for Production at Scale
Instructor qualifications:
Candidates must demonstrate significant experience in deployment of retrieval systems, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Active open-source contribution or coordination efforts in the area
- Experience orchestrating dialog management and information retrieval systems
- Strong applied software engineering expertise, esp. surrounding microservices and inference server solutions
Candidates should have experience with the following:
- Strong proficiency in Python, including functional programming and server deployment
- Expertise in large language models as inference endpoints, including industry use-cases.
- Experience with microservice/server orchestration, including Docker and FastAPI.
- Retrieval systems, including combination of embedders and rerankers
- Intuition of evaluation metrics and performance expectations.
- Container orchestration platforms, especially Kubernetes clusters
- Monitoring tools like Prometheus
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Enhancing Data Science Outcomes with Efficient Workflows
Instructor qualifications:
Candidates must demonstrate significant experience with Data Science in Python using distributed computing for large datasets and should be able to discuss about their previous work:
- Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
- The use of various data transformations applied on input data for model consumption
- The use of various Machine Learning algorithms in their work, explaining their decisions
- Extensive use of Python Data Science libraries like pandas, NumPy, scikit-learn, and xgboost
- Previous work with or on RAPIDS and Dask
- Recognition of the iterative nature of Data Science and appreciation of hardware acceleration for rapid experimentation
Candidates should have the following:
- Python and common Data Science libraries like pandas, NumPy, scikit-learn, and xgboost
- Proficiency with DataFrame manipulation
- Familiarity with distributed computing using Dask
- Familiarity with end-to-end machine learning workflow
- Proficiency with various Machine Learning models, specifically those of tree-based variant
- Proficiency with model performance metrics such as accuracy and inference performance
- Familiarity with model tuning and its benefits
- Knowledge of NVIDIA’s RAPIDS, NVTabular, and Triton Inference Server
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Fundamentals of Accelerated Computing with Modern CUDA C++
Instructor qualifications:
Candidates must demonstrate significant experience working with CUDA/GPU-accelerated applications in the past, either in a professional or meaningful academic scenario, and should be able to discuss about their previous work:
- How meaningful acceleration was achieved on a problem that could not be addressed as successfully in a CPU-only environment
- Details of the applied strategies that the applications use in relation to a GPU architecture
- Technical challenges encountered, tailored to CUDA-specifics and how they were addressed
Candidates should have the following:
- Basic understanding of computer architecture (memory hierarchies, computing cores, etc.)
- Foundational knowledge in parallel computing
- Awareness of race conditions and familiarity with methods to prevent them
- Understanding synchronization mechanisms between threads/processes
- Medium to advance knowledge and experience in modern C++ programming including understanding classes, functors, and lambda functions
- Knowledge and experience with the C++ Standard Template Library (STL), including extensive use of iterators
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Fundamentals of Accelerated Computing with CUDA Python
Instructor qualifications:
Please provide some evidence of having worked significantly with a CUDA-accelerated application in the past, either in a professional or meaningful academic scenario, and be prepared to talk about your work with others. You should be able to discuss:
- How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
- The specifics of optimization strategies that the applications use
- Specific CUDA-related technical challenges that arose while developing the applications
Candidates should also have the following:
- Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. Basic NumPy competency including familiarity ndarrays and ufuncs.
Fundamentals of Accelerated Data Science
Instructor qualifications:
Candidates must demonstrate significant experience with Data Science in Python and should be able to discuss about their previous work:
- Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
- The use of many DS/ML algorithms in their work, explaining their decisions
- Extensive use of Python DS libraries like Pandas, NumPy, scikit-learn, NetworkX
- Encouraged, previous work with Dask. Polars, and/or RAPIDS
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Fundamentals of Deep Learning
Instructor qualifications:
Candidates must demonstrate experience working on a computer vision task—image classification, object detection, etc.—using deep learning in either a professional or academic setting. Foundational knowledge of natural language processing (NLP), reinforcement learning (RL), and other neural network architectures such as RNNs / LSTMs and GANs is required. Qualifying experience includes:
- A professional role (Ex: Data Engineer, Data Scientist) architecting computer vision projects that use deep learning
- Academic coursework in computer vision, NLP, RL and neural network architectures
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience—either via in-person or distance setting
- Significant presentation experience
Candidates should also have the following:
- Familiarity with basic programming fundamentals such as functions and variables
- Basic Python competency
Generative AI with Diffusion Models
Instructor qualifications:
Candidates must demonstrate thorough up-to-date experience with deep learning, computer vision, and diffusion models. Ideal candidates should have background knowledge of surrounding material as well as active roles which expose them to the latest trends, innovations, and emerging intuitions. Qualifying experiences include:
- A professional role (Ex: Machine Learning Engineer, Data Scientist) architecting deep learning projects that generate images
- Active open-source contribution or coordination efforts in the area
- Academic coursework in using AI to generate images.
Candidates should have the following:
- Proficiency in Python and PyTorch
- Active intuitive understanding of CLIP and multimodal AEs/VAEs/GANs/Stable Diffusion
- Intuitive understanding of audio/video/image classification/captioning/transcription
- Foundation in statistics including the normal distribution and random sampling
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Model Parallelism: Building and Deploying Large Neural Networks
Instructor qualifications:
Candidates must demonstrate experience working on a model parallelism related task using deep learning in either a professional or academic setting. Foundational knowledge of optimization techniques such as activation checkpointing, mixed precision training, and gradient accumulation is required. Qualifying experience includes:
- A professional role (Ex: Data Engineer, Data Scientist) architecting deep learning projects that use distributed systems such as the cloud or multi-GPU machines.
- Academic coursework in large neural network architectures such as GPT-3.
Candidate should have the following:
- Classroom teaching experience—either via in-person or distance setting
- Significant presentation experience
Candidates must also demonstrate teaching experience, such as:
- An understanding of the Slurm, NVIDIA Triton and DeepSpeed technologies
- An understanding of the differences between Model and Data Parallelism
NVIDIA Isaac for Accelerated Robotics
Instructor qualifications:
Candidates must demonstrate experience working on at least one robotics simulation project, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
Candidates should have experience with the following:
- Isaac Sim
- OpenUSD
- URDF models
- ROS2
- Robotic navigation, i.e. SLAM
- Synthetic data generation
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Rapid Application Development using Large Language Models
Instructor qualifications:
Candidates must demonstrate thorough up-to-date experience with deep learning, large language models, and agent systems. Ideal candidates should have background knowledge of surrounding material as well as active roles which expose them to the latest trends, innovations, and emerging intuitions. Qualifying experiences include:
- Chat model/multimodal model architecture design experience
- Experience with the training loop and pipeline assumptions/intuitions
- Active open-source contribution or coordination efforts in the area
- Experience orchestrating dialog management and information retrieval systems
Candidates should have the following:
- Advanced proficiency with Python, sufficient for reading HuggingFace source code
- HuggingFace comfort, including serialization, model release, HF Transformers, etc
- Experience designing systems with LLM constituent components
- Familiarity with PyTorch, deep learning, generative AI, multimodal models, etc
- Understanding of experimentation/deployment with LLM systems, including hardware requirements, safety considerations, evaluation techniques, etc
- Intuitive understanding of audio/video/image classification/captioning/transcription
- Active intuitive understanding of CLIP and multimodal AEs/VAEs/GANs/Stable Diffusion
- LangChain experience, including intuitions and details of current developments
- Familiarity with RAG, including LlamaIndex, VDB services, retriever models, etc
- Comfort with NVIDIA value propositions surrounding LLMs, RAG, NeMo, etc
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience
- Significant presentation experience
Scaling CUDA C++ Applications to Multiple Nodes
Instructor qualifications:
Candidates must have professional or academic experience developing CUDA C++ applications in the SPMD paradigm (MPI or SHEMEM derivatives) on compute clusters, and should be able to discuss their work on these applications in detail. In particular, candidates should be able to discuss:
- The technologies used to scale their application to multiple nodes
- Details about the compute cluster used to deploy their applications, including details about intra and inter-node networking
- Inter-GPU and inter-node communication patterns required to successfully run the application
- The reasoning behind communication patterns employed in the application
Candidates should have the following:
- Instructors should have either experience, or the ability at least to discuss and describe, prototypical scientific applications such as a Jacobi solver or wave simulation.
Candidates must also demonstrate teaching experience, such as:
- Classroom teaching experience - in person or remote
- Significant presentation experience
NVIDIA Academy Workshops
Cumulus Linux Administration
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies, and real-time monitoring.
Qualifying experiences include:
- Professional experience (e.g., Network Engineer, System Administrator, Infrastructure Engineer, Solutions Architect, DevOps, Trainers) deploying, configuring, and managing Cumulus Linux-based network environments in production data centers.
Candidates must have:
- Proficiency in Linux administration (shell, config management, troubleshooting)
- Strong knowledge of Ethernet networking, switching, and routing
- NVIDIA networking hardware experience preferred
- Layer 2 and Layer 3 networking: VLANs, bridging, trunking, link aggregation (LAG/MLAG), SVIs, VRR, VRF, and BGP (including BGP unnumbered).
- Network virtualization using VXLAN and EVPN, including both symmetric and asymmetric routing models.
- Experience with network automation tools and workflows (e.g., Ansible, REST APIs, Zero Touch Provisioning).
- Monitoring, diagnostics, and troubleshooting across multiple network layers, including hardware resource monitoring and Open Telemetry
Preferred:
- NVIDIA Cumulus Linux or AI Networking certification
- Active involvement in open-source networking projects or community
Spectrum-X Networking Platform Administration
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies, and real-time monitoring.
Qualifying experience includes:
- Professional roles such as Network Engineer, DevOps, Technical Instructors or System Administrators working with Spectrum-X, Cumulus Linux, and AI data center networks.
- Cumulus Linux Certified Instructor.
- Experience with NVIDIA Air cluster simulation and deployment.
- Real-time monitoring and troubleshooting with NetQ, Cumulus Linux CLI, and telemetry tools (ASIC, OTLP, DTS).
Candidates must also have:
- Cumulus Linux: Hands-on experience with installation, configuration, upgrades, Layer 2/3 features, network virtualization (VXLAN/EVPN), automation, and troubleshooting.
- Networking: Strong knowledge of Ethernet, switching, routing, and data center networking automation.
- Linux: Proficiency in Linux administration and managing Linux-based network environments.
Preferred:
- NVIDIA Spectrum-X or AI Networking certification
- Active involvement in open-source networking projects or community
AI Infrastructure
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in deploying and managing AI data center infrastructure, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI infrastructure technologies and workflows.
Required Experience
- Professional roles such as Data Center Administrator, DevOps Engineer, System Administrator, or AI Infrastructure Engineer working with enterprise-scale AI environments.
- Direct, practical experience with:
- Deploying and managing AI compute platforms (GPUs, CPUs, DPUs)
- Building and maintaining InfiniBand and Ethernet networks for AI workloads
- Storage architecture and performance optimization for AI data centers
- Virtualization technologies (VMs, containers, GPU partitioning with vGPU/MIG)
- Installing and managing NVIDIA software (GPU drivers, DOCA, NGC containers, NVIDIA AI Enterprise Suite)
- Using management tools such as NVIDIA Base Command Manager (BCM) for AI cluster provisioning and operations
Technical Skills
- Networking: Strong knowledge of Ethernet and InfiniBand, switching, routing, and advanced data center networking automation.
- Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting).
- Storage: Understanding of file systems, storage protocols, and performance testing.
- Virtualization: Experience with VMs, containers, and GPU virtualization.
- AI Concepts: Familiarity with machine learning, deep learning, and common AI applications.
AI Operations
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in operating and managing AI data center environments, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI data center operations and workflows.
Required Experience
- Professional roles such as Data Center Administrator, DevOps Engineer, System Administrator, AI Infrastructure Engineer, or Data Scientist working with enterprise-scale AI environments.
- Direct, practical experience with:
- Operating and managing AI compute platforms (GPUs, CPUs, DPUs)
- Provisioning and managing AI workloads and virtualization in data centers
- Building and maintaining InfiniBand and Ethernet networks for AI workloads
- Storage architecture and performance optimization for AI data centers
- Virtualization technologies (VMs, containers, GPU partitioning)
- Installing and managing NVIDIA software (GPU drivers, DOCA, NGC containers, AI Enterprise Suite)
- Using management tools such as NVIDIA DCGM, UFM, and BlueField management utilities
Technical Skills
- Networking: Strong knowledge of Ethernet and InfiniBand, switching, routing, and data center networking automation.
- Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting).
- Storage: Understanding of file systems, storage protocols, and performance testing.
- Virtualization: Experience with VMs, containers, and GPU virtualization.
- AI Concepts: Familiarity with machine learning, deep learning, and common AI applications.
NVIDIA Academy Workshops
General Teaching Requirements
All candidates must demonstrate teaching experience, such as:
- Classroom or virtual teaching experience delivering technical content to network or system professionals.
- Significant presentation experience in instructor-led settings, including remote delivery via platforms like Teams or WebEx.
- Ability to facilitate hands-on labs and guide troubleshooting exercises in a virtual environment
Candidates must be able to effectively communicate complex technical concepts, adapt to varying learner skill levels, and foster an interactive, hands-on learning environment aligned with NVIDIA’s training standards.
Follow the links below to ensure you have the qualifications necessary for the workshop you are interested in.
Accelerating CUDA C++ Applications with Multiple GPUs
Instructor qualifications:
Candidates must demonstrate significant experience with multiple CUDA-accelerated applications in the past, either in a professional or meaningful academic scenario, and be able to explain their work with these applications. These applications should involve the use of multiple GPUs and concurrent streams.
- How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
- The specifics of optimization strategies that the applications use
- Specific CUDA-related technical challenges that arose while developing the applications
- Advanced CUDA C++ experience.
- Mastery of multiple techniques for performing copy/compute overlap in single and multiple GPU applications, and the ability to discuss them clearly in detail and at length.
- Experience with NSight Systems.
- Classroom teaching experience
- Significant presentation experience
Adding New Knowledge to LLMs
Instructor qualifications:
Candidates must demonstrate experience working on at least one LLM application involving finetuning, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
- Differentiating RAG, finetuning, and alignment
- Strategies to drive creation of diverse synthetic data sets
- Parameter efficient finetuning
- Pruning techniques
- Distillation techniques
- LLM decoding strategies, such as top-k/p, beam search, etc.
- LLM output evaluation techniques, such as ROUGE/BLEU, semantic similarity, LLM-as-a-judge, etc.
- Classroom teaching experience
- Significant presentation experience
Applications of AI for Anomaly Detection
Instructor qualifications:
Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- A role as a major contributor to a project that used Deep Learning
- A role as a major contributor to a project that used other Machine Learning techniques
- A role as a major contributor to a project that required data science
- Classroom teaching experience
- Significant presentation experience
- Professional Data Science Experience using Python
- A working understanding of NVIDIA RAPIDS
- Significant experience in machine and deep learning, specifically the use of XG Boost, AutoEncoder, and GAN models
- Exposure to the telecommunications industry and cybersecurity, specifically networking and the threat of network intrusion.
Applications of AI for Predictive Maintenance
Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Deep Learning for time-series data, work/research experience with variations of auto-encoder models, recurrent models (LSTMs) and GANs.
- Measures of model accuracy, preferably in the context of industrial applications.
- Familiarity with machine learning techniques. Having a thorough understanding of XGBoost algorithm is crucial to the success of course delivery.
- Minimum of one deep learning library. Keras and TensorFlow are preferred
- Classroom teaching experience
- Significant presentation experience
- Familiarity with deep learning concepts (At minimum level, having knowledge of artificial neural networks)
- Python (and common python libraries used in DL, e.g., numpy, pandas, sklearn,...)
- Having knowledge of TensorFlow and keras
Bootstrapping Computer Vision Models with Synthetic Data
Instructor qualifications:
Candidates must demonstrate experience working on at least one agentic AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
- Omniverse Replicator
- Automatic label annotating
- Domain randomization
- Convolutional neural networks
- Deep learning model training, including transfer learning
- Hyperparameter optimization techniques
- Classroom teaching experience
- Significant presentation experience
Building Agentic AI Applications with LLMs
Instructor qualifications:
Candidates must demonstrate experience working on at least one agentic AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
- Modern LangChain, including LCEL, LangGraph, etc.
- Differentiating capabilities of various agentic tools such as LangGraph, CrewAI, Autogen, etc.
- Stateful LLM systems
- LLM tool calling
- Strategies to prevent derailing
- Agentic routing
- Classroom teaching experience
- Significant presentation experience
Building AI Based Cybersecurity Pipelines
Instructor qualifications:
Candidates must have professional experience in the domain of defensive cybersecurity and data analysis. Candidates should be able to discuss their work as it relates to topics such as:
- Methods and tooling used in service of defensive cybersecurity for data collection, preparation, analysis, storage etc.
- Approaches to defending and resolving common cybersecurity attacks such as DOS, phishing, hijacked accounts etc.
- Effective data analysis through the use of machine and deep learning models like XGBoost.
- The use of GPU-accelerated libraries for use in data analysis, particularly those found in NVIDIA RAPIDS Forest Inference library.
- Familiarity with the structure of applications using NVIDIA Morpheus (pipelines, stages, modules, messages, etc.).
- Classroom teaching experience - in person or remote
- Significant presentation experience
Building Conversational AI Applications
Instructor qualifications:
Candidates must demonstrate experience working on at least one conversational AI application using automatic speech recognition (ASR), natural language understanding (NLU), and/or text to speech (TTS), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- A professional role (Ex: Engineer, Data Scientist) on a conversational AI project that used an ASR model to transcribe spoken language and process it
- A completed conversational AI project for a virtual assistant application
- Academic coursework in conversational AI using neural networks
- Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
- Experience using TAO Toolkit and Riva
- Basic Linux command line experience
- Experience using Docker
- Experience using Helm Charts and Kubernetes
- Classroom teaching experience - in person or remote
- Significant presentation experience
Building LLM Applications with Prompt Engineering
Instructor qualifications:
Candidates must demonstrate experience working on at least one LLM application using a programmatic interface, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
- Development in Python, including an understanding of Pydantic
- Modern LangChain, including LCEL, LangGraph, etc.
- LLM next token prediction decoding methods
- How LLM models are developed (pretraining, alignment, instruction-tuning, etc.)
- LLM prompting techniques (iterative, zero/one/few-shot, chain of thought, etc.)
- Agents using tools, such as ReAct
- Classroom teaching experience
- Significant presentation experience
Building Multimodal Pipelines With Large Language Models
Instructor qualifications:
Candidates must demonstrate experience working on at least one generative AI application incorporating inputs of multiple modalities, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
- Details of implementing contrastive pretraining
- Techniques for combining embeddings of various modalities such as VLM projections
- Tools for chunking documents including text, titles, figures, charts, tables, etc.
- Graph RAG and associated technology such as knowledge bases, cypher queries, etc.
- Classroom teaching experience
- Significant presentation experience
Building RAG Agents with LLMs
Instructor qualifications:
Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Active open-source contribution or coordination efforts in the area
- Experience orchestrating dialog management and information retrieval systems
- Strong applied software engineering expertise, esp. surrounding microservices and inference server solutions
- Strong proficiency in Python, including functional programming and server deployment
- Expertise in large language models as inference endpoints, including industry use-cases.
- Strong experience with modern LangChain (including LCEL) and LangServe required; understanding of LangGraph, LlamaIndex, Langsmith, and NeMo Guardrails useful.
- Experience with microservice/server orchestration, including Docker and FastAPI.
- Experience with modern RAG, including some derivative formulations and pros/cons.
- Understanding of agentic behavior, tooling, and modular agent components.
- Intuition of evaluation metrics and performance expectations.
- Classroom teaching experience
- Significant presentation experience
Building Transformer Based Natural Language Processing Applications
Instructor qualifications:
Candidates must demonstrate experience working on at least one Natural Language Processing application using a Transformer-based architecture (such as BERT), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- A professional role (Ex: Engineer, Data Scientist) on an NLP project that used a Transformer-based architecture
- A completed NLP project that used a Transformer-based architecture
- Academic coursework in NLP Transformer-based networks
- Classroom teaching experience - in person or remote
- Significant presentation experience
- Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
- Basic pandas and NeMo competency
- Experience using NVIDIA Triton Inference Server
Computer Vision for Industrial Inspection
Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Using Deep Learning techniques to tackle classification problems, preferably in the context of industrial applications.
- A professional role on a computer vision project that used Deep Learning techniques.
- Significant coursework in Deep Learning for computer vision that covers the various stages of the development workflow.
- Python (and common python libraries used in DL, e.g., numpy and pandas)
- Familiarity with end-to-end machine learning workflow
- Familiarity with manipulating data using pandas DataFrame
- Familiarity with deep learning concepts including knowledge of convolutional neural networks
- Familiarity of at least one deep learning framework (Keras and TensorFlow are preferred)
- Familiarity with metrics such as accuracy and inference performance
- Familiarity with command-line interface and basic linux commands
- Familiarity with transfer learning and fine-tuning models
- Knowledge of NVIDIA’s DALI, TAO Toolkit, TensorRT, and Triton Inference Server
- Classroom teaching experience
- Significant presentation experience
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Deploying deep learning training workloads to multiple GPUs and preferably multi-node clusters
- Data Parallel approaches to distributed Deep Learning
- Profiling and optimizing the deep learning code
- Using NGC containers
- Experience in building neural networks with PyTorch
- Using PyTorch DDP to deploy distributed training
- Classroom teaching experience
- Significant presentation experience
- Good understanding of the literature discussing implications of training deep neural networks with large batches. In particular, a good understanding of the LARS/LARC algorithm.
- Understanding of the process used in training deep neural networks. In particular understanding of the Stochastic Gradient Descent and backpropagation algorithms.
Deploying RAG Pipelines for Production at Scale
Instructor qualifications:
Candidates must demonstrate significant experience in deployment of retrieval systems, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:
- Active open-source contribution or coordination efforts in the area
- Experience orchestrating dialog management and information retrieval systems
- Strong applied software engineering expertise, esp. surrounding microservices and inference server solutions
- Strong proficiency in Python, including functional programming and server deployment
- Expertise in large language models as inference endpoints, including industry use-cases.
- Experience with microservice/server orchestration, including Docker and FastAPI.
- Retrieval systems, including combination of embedders and rerankers
- Intuition of evaluation metrics and performance expectations.
- Container orchestration platforms, especially Kubernetes clusters
- Monitoring tools like Prometheus
- Classroom teaching experience
- Significant presentation experience
Enhancing Data Science Outcomes with Efficient Workflows
Instructor qualifications:
Candidates must demonstrate significant experience with Data Science in Python using distributed computing for large datasets and should be able to discuss about their previous work:
- Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
- The use of various data transformations applied on input data for model consumption
- The use of various Machine Learning algorithms in their work, explaining their decisions
- Extensive use of Python Data Science libraries like pandas, NumPy, scikit-learn, and xgboost
- Previous work with or on RAPIDS and Dask
- Recognition of the iterative nature of Data Science and appreciation of hardware acceleration for rapid experimentation
- Python and common Data Science libraries like pandas, NumPy, scikit-learn, and xgboost
- Proficiency with DataFrame manipulation
- Familiarity with distributed computing using Dask
- Familiarity with end-to-end machine learning workflow
- Proficiency with various Machine Learning models, specifically those of tree-based variant
- Proficiency with model performance metrics such as accuracy and inference performance
- Familiarity with model tuning and its benefits
- Knowledge of NVIDIA’s RAPIDS, NVTabular, and Triton Inference Server
- Classroom teaching experience
- Significant presentation experience
Fundamentals of Accelerated Computing with Modern CUDA C++
Instructor qualifications:
Candidates must demonstrate significant experience working with CUDA/GPU-accelerated applications in the past, either in a professional or meaningful academic scenario, and should be able to discuss about their previous work:
- How meaningful acceleration was achieved on a problem that could not be addressed as successfully in a CPU-only environment
- Details of the applied strategies that the applications use in relation to a GPU architecture
- Technical challenges encountered, tailored to CUDA-specifics and how they were addressed
- Basic understanding of computer architecture (memory hierarchies, computing cores, etc.)
- Foundational knowledge in parallel computing
- Awareness of race conditions and familiarity with methods to prevent them
- Understanding synchronization mechanisms between threads/processes
- Medium to advance knowledge and experience in modern C++ programming including understanding classes, functors, and lambda functions
- Knowledge and experience with the C++ Standard Template Library (STL), including extensive use of iterators
- Classroom teaching experience
- Significant presentation experience
Fundamentals of Accelerated Computing with CUDA Python
Instructor qualifications:
Please provide some evidence of having worked significantly with a CUDA-accelerated application in the past, either in a professional or meaningful academic scenario, and be prepared to talk about your work with others. You should be able to discuss:
- How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
- The specifics of optimization strategies that the applications use
- Specific CUDA-related technical challenges that arose while developing the applications
- Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. Basic NumPy competency including familiarity ndarrays and ufuncs.
Fundamentals of Accelerated Data Science
Instructor qualifications:
Candidates must demonstrate significant experience with Data Science in Python and should be able to discuss about their previous work:
- Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
- The use of many DS/ML algorithms in their work, explaining their decisions
- Extensive use of Python DS libraries like Pandas, NumPy, scikit-learn, NetworkX
- Encouraged, previous work with Dask. Polars, and/or RAPIDS
- Classroom teaching experience
- Significant presentation experience
Fundamentals of Deep Learning
Instructor qualifications:
Candidates must demonstrate experience working on a computer vision task—image classification, object detection, etc.—using deep learning in either a professional or academic setting. Foundational knowledge of natural language processing (NLP), reinforcement learning (RL), and other neural network architectures such as RNNs / LSTMs and GANs is required. Qualifying experience includes:
- A professional role (Ex: Data Engineer, Data Scientist) architecting computer vision projects that use deep learning
- Academic coursework in computer vision, NLP, RL and neural network architectures
- Classroom teaching experience—either via in-person or distance setting
- Significant presentation experience
- Familiarity with basic programming fundamentals such as functions and variables
- Basic Python competency
Generative AI with Diffusion Models
Instructor qualifications:
Candidates must demonstrate thorough up-to-date experience with deep learning, computer vision, and diffusion models. Ideal candidates should have background knowledge of surrounding material as well as active roles which expose them to the latest trends, innovations, and emerging intuitions. Qualifying experiences include:
- A professional role (Ex: Machine Learning Engineer, Data Scientist) architecting deep learning projects that generate images
- Active open-source contribution or coordination efforts in the area
- Academic coursework in using AI to generate images.
- Proficiency in Python and PyTorch
- Active intuitive understanding of CLIP and multimodal AEs/VAEs/GANs/Stable Diffusion
- Intuitive understanding of audio/video/image classification/captioning/transcription
- Foundation in statistics including the normal distribution and random sampling
- Classroom teaching experience
- Significant presentation experience
Model Parallelism: Building and Deploying Large Neural Networks
Instructor qualifications:
Candidates must demonstrate experience working on a model parallelism related task using deep learning in either a professional or academic setting. Foundational knowledge of optimization techniques such as activation checkpointing, mixed precision training, and gradient accumulation is required. Qualifying experience includes:
- A professional role (Ex: Data Engineer, Data Scientist) architecting deep learning projects that use distributed systems such as the cloud or multi-GPU machines.
- Academic coursework in large neural network architectures such as GPT-3.
- Classroom teaching experience—either via in-person or distance setting
- Significant presentation experience
- An understanding of the Slurm, NVIDIA Triton and DeepSpeed technologies
- An understanding of the differences between Model and Data Parallelism
NVIDIA Isaac for Accelerated Robotics
Instructor qualifications:
Candidates must demonstrate experience working on at least one robotics simulation project, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:
- A professional role (e.g. engineer, data scientist)
- A completed project
- Academic coursework
- Isaac Sim
- OpenUSD
- URDF models
- ROS2
- Robotic navigation, i.e. SLAM
- Synthetic data generation
- Classroom teaching experience
- Significant presentation experience
Rapid Application Development using Large Language Models
Instructor qualifications:
Candidates must demonstrate thorough up-to-date experience with deep learning, large language models, and agent systems. Ideal candidates should have background knowledge of surrounding material as well as active roles which expose them to the latest trends, innovations, and emerging intuitions. Qualifying experiences include:
- Chat model/multimodal model architecture design experience
- Experience with the training loop and pipeline assumptions/intuitions
- Active open-source contribution or coordination efforts in the area
- Experience orchestrating dialog management and information retrieval systems
- Advanced proficiency with Python, sufficient for reading HuggingFace source code
- HuggingFace comfort, including serialization, model release, HF Transformers, etc
- Experience designing systems with LLM constituent components
- Familiarity with PyTorch, deep learning, generative AI, multimodal models, etc
- Understanding of experimentation/deployment with LLM systems, including hardware requirements, safety considerations, evaluation techniques, etc
- Intuitive understanding of audio/video/image classification/captioning/transcription
- Active intuitive understanding of CLIP and multimodal AEs/VAEs/GANs/Stable Diffusion
- LangChain experience, including intuitions and details of current developments
- Familiarity with RAG, including LlamaIndex, VDB services, retriever models, etc
- Comfort with NVIDIA value propositions surrounding LLMs, RAG, NeMo, etc
- Classroom teaching experience
- Significant presentation experience
Scaling CUDA C++ Applications to Multiple Nodes
Instructor qualifications:
Candidates must have professional or academic experience developing CUDA C++ applications in the SPMD paradigm (MPI or SHEMEM derivatives) on compute clusters, and should be able to discuss their work on these applications in detail. In particular, candidates should be able to discuss:
- The technologies used to scale their application to multiple nodes
- Details about the compute cluster used to deploy their applications, including details about intra and inter-node networking
- Inter-GPU and inter-node communication patterns required to successfully run the application
- The reasoning behind communication patterns employed in the application
- Instructors should have either experience, or the ability at least to discuss and describe, prototypical scientific applications such as a Jacobi solver or wave simulation.
- Classroom teaching experience - in person or remote
- Significant presentation experience
NVIDIA Academy Workshops
Cumulus Linux Administration
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies, and real-time monitoring.
Qualifying experiences include:
- Professional experience (e.g., Network Engineer, System Administrator, Infrastructure Engineer, Solutions Architect, DevOps, Trainers) deploying, configuring, and managing Cumulus Linux-based network environments in production data centers.
- Proficiency in Linux administration (shell, config management, troubleshooting)
- Strong knowledge of Ethernet networking, switching, and routing
- NVIDIA networking hardware experience preferred
- Layer 2 and Layer 3 networking: VLANs, bridging, trunking, link aggregation (LAG/MLAG), SVIs, VRR, VRF, and BGP (including BGP unnumbered).
- Network virtualization using VXLAN and EVPN, including both symmetric and asymmetric routing models.
- Experience with network automation tools and workflows (e.g., Ansible, REST APIs, Zero Touch Provisioning).
- Monitoring, diagnostics, and troubleshooting across multiple network layers, including hardware resource monitoring and Open Telemetry
- NVIDIA Cumulus Linux or AI Networking certification
- Active involvement in open-source networking projects or community
Spectrum-X Networking Platform Administration
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies, and real-time monitoring.
Qualifying experience includes:
- Professional roles such as Network Engineer, DevOps, Technical Instructors or System Administrators working with Spectrum-X, Cumulus Linux, and AI data center networks.
- Cumulus Linux Certified Instructor.
- Experience with NVIDIA Air cluster simulation and deployment.
- Real-time monitoring and troubleshooting with NetQ, Cumulus Linux CLI, and telemetry tools (ASIC, OTLP, DTS).
- Cumulus Linux: Hands-on experience with installation, configuration, upgrades, Layer 2/3 features, network virtualization (VXLAN/EVPN), automation, and troubleshooting.
- Networking: Strong knowledge of Ethernet, switching, routing, and data center networking automation.
- Linux: Proficiency in Linux administration and managing Linux-based network environments.
- NVIDIA Spectrum-X or AI Networking certification
- Active involvement in open-source networking projects or community
AI Infrastructure
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in deploying and managing AI data center infrastructure, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI infrastructure technologies and workflows.
- Professional roles such as Data Center Administrator, DevOps Engineer, System Administrator, or AI Infrastructure Engineer working with enterprise-scale AI environments.
- Direct, practical experience with:
- Deploying and managing AI compute platforms (GPUs, CPUs, DPUs)
- Building and maintaining InfiniBand and Ethernet networks for AI workloads
- Storage architecture and performance optimization for AI data centers
- Virtualization technologies (VMs, containers, GPU partitioning with vGPU/MIG)
- Installing and managing NVIDIA software (GPU drivers, DOCA, NGC containers, NVIDIA AI Enterprise Suite)
- Using management tools such as NVIDIA Base Command Manager (BCM) for AI cluster provisioning and operations
- Networking: Strong knowledge of Ethernet and InfiniBand, switching, routing, and advanced data center networking automation.
- Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting).
- Storage: Understanding of file systems, storage protocols, and performance testing.
- Virtualization: Experience with VMs, containers, and GPU virtualization.
- AI Concepts: Familiarity with machine learning, deep learning, and common AI applications.
AI Operations
Instructor prerequisites:
Candidates must demonstrate comprehensive, up-to-date expertise in operating and managing AI data center environments, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI data center operations and workflows.
- Professional roles such as Data Center Administrator, DevOps Engineer, System Administrator, AI Infrastructure Engineer, or Data Scientist working with enterprise-scale AI environments.
- Direct, practical experience with:
- Operating and managing AI compute platforms (GPUs, CPUs, DPUs)
- Provisioning and managing AI workloads and virtualization in data centers
- Building and maintaining InfiniBand and Ethernet networks for AI workloads
- Storage architecture and performance optimization for AI data centers
- Virtualization technologies (VMs, containers, GPU partitioning)
- Installing and managing NVIDIA software (GPU drivers, DOCA, NGC containers, AI Enterprise Suite)
- Using management tools such as NVIDIA DCGM, UFM, and BlueField management utilities
- Networking: Strong knowledge of Ethernet and InfiniBand, switching, routing, and data center networking automation.
- Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting).
- Storage: Understanding of file systems, storage protocols, and performance testing.
- Virtualization: Experience with VMs, containers, and GPU virtualization.
- AI Concepts: Familiarity with machine learning, deep learning, and common AI applications.