Areas of Interest:
AI Platforms Deployment
Content Creation / Rendering
Data Centre / Cloud
Edge Computing
Models / Libraries /Frameworks
Simulation / Modeling /Design
AR / VR
Converstional AI
Data Science
Generative AI
Networking / Communications
Computer Vision / Video Analytics
Cyber Security
Development and Optimisation
MLOPs
Robotics
AI Data
Neurons
TensorFlow 2
Data
Building a Neuron
Initiate Training
Evaluating the Model
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Welcome to the NVIDIA Developer Program
As a member, you can now access tools and training to help you succeed with NVIDIA technologies. Get started:
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Get free access to NVIDIA NIM™ API endpoints and downloadable containers for research, development and testing with optimized AI models.
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How to stand out as an NVIDIA Project Manager:
Operating systems principles, Linux OS and Programming experience in modern programming languages advantageous. Datacenter functional knowledge and experience with PCIe boards is a plus. Previous experience coordinating activities between HW, Firmware and SW organizations, is a plus. Knowledge of GPUs and NICs is a plus. Having experience with Agile tools in support of this role
To apply successfully for the role you’ve described, it sounds like a technical program/project management or system-level engineering role — likely within a data center hardware/software integration team — you'll need a blend of software fundamentals, systems knowledge, and cross-functional coordination skills. Here’s a breakdown of what you should have, learn, or brush up on to strengthen your application.
Foundational knowledge required
Learn and be comfortable with:
Processes and threads (scheduling, context switching, concurrency)
Memory management (paging, segmentation, virtual memory)
I/O systems (device drivers, buffering)
File systems and mounting
User/kernel mode transitions
Boot process and system calls
What to learn:
"Operating Systems: Three Easy Pieces" by Remzi H. Arpaci-Dusseau
Linux man pages and tutorials on how memory/IO management works
Hands-on experience required
Be able to:
Navigate the shell (bash, zsh)
Use system utilities (top, ps, lsof, dmesg, strace, etc.)
Write shell scripts
Understand Linux system boot, services, and package management
Compile and configure a kernel (advanced)
Analyze logs and manage performance tools (e.g., perf, iotop, htop)
What to learn:
“Linux Command Line and Shell Scripting Bible” – Richard Blum
Use a distro like Ubuntu Server or CentOS for hands-on experience
At least one language required
Languages to consider:
Python (automation, scripting, orchestration)
C/C++ (for firmware or OS-level interaction)
Go (popular in cloud-native systems)
Rust (growing in systems programming)
Bash (for Linux scripting)
What to learn:
Python: scripting, logging, subprocesses, APIs
C: memory management, structs, pointers (used in firmware/HW-SW interaction)
Helps you understand the environment
Understand the structure of a modern data center, including:
Servers, racks, PDUs
Networking layers (Top-of-Rack, Spine-Leaf)
Power, cooling, and environmental controls
Firmware management and updates
Storage (SAN/NAS), and compute platforms
What to learn:
Google: “Anatomy of a Datacenter”
Learn about Redfish or IPMI protocols for HW mgmt
Important if interacting with custom hardware
Know:
What PCIe is and how it works (link training, enumeration)
Types of cards: GPUs, NICs, custom accelerators (like AI chips)
Driver loading and hardware probing (lspci, modprobe, dmesg)
Debugging PCIe device issues
What to learn:
Read PCIe architecture basics from Intel or PCI-SIG
Learn about Linux PCIe subsystem
Big part of this role
You’ll need:
Strong communication and tracking skills
Understanding of development life cycles in hardware (firmware bring-up, driver integration, validation)
Handling dependency tracking and cross-team blockers
Escalating issues to the right layer: BIOS → firmware → driver → OS
What to practice:
Learn how embedded development cycles differ from software-only teams
Use tools like Confluence, JIRA, or Smartsheet to simulate cross-team tracking
Desirable if the role touches compute or networking performance
Understand:
GPU use cases: ML, graphics, video encoding, GPGPU
NICs: speeds (10/25/100/400G), features (RDMA, SR-IOV)
How GPUs and NICs appear on PCIe buses
How drivers (e.g., NVIDIA) are installed and managed
What to learn:
Look into nvidia-smi, CUDA stack
Study modern NIC vendors: Mellanox (NVIDIA), Broadcom, Intel
Important for managing the program/project
Know how to:
Use JIRA, Confluence, Trello, Asana or equivalents
Run Agile ceremonies: standups, sprint planning, retrospectives
Manage Kanban boards, burn-down charts
Prioritize and plan releases across multiple engineering orgs
What to learn:
Take a Scrum Master certification (CSM) or SAFe certification
Practice managing epics, stories, tasks in a sandbox JIRA project
Bachelor’s degree in Computer Science, Engineering, or equivalent experience
2–5+ years in systems/software/firmware/technical project management
Experience with Linux and one programming language
Demonstrated ability to manage cross-functional technical teams
Familiarity with datacenter hardware environments
Experience with PCIe devices, firmware updates, or embedded systems
Background working with GPUs/NICs/accelerator cards
Familiarity with Agile software development practices
Past work in hardware/software integration or silicon validation teams
Week
Focus
Resources
1–2
OS & Linux Internals
"OS: Three Easy Pieces", Ubuntu VM hands-on
3–4
Programming (Python/C)
Leetcode (easy-medium), GitHub projects
5
Data Center & PCIe
Read Intel whitepapers, test lspci, dmesg
6
Agile & Cross-Team Tools
Atlassian JIRA, Confluence tutorials
7
GPUs & NICs
NVIDIA documentation, nvidia-smi, NIC specs
8
Integration practice
Simulate a feature release across HW/FW/SW teams
Redfish and IPMI are out-of-band (OOB) management protocols.
They let administrators monitor, control, and manage server hardware remotely.
This includes power status, sensors, firmware, and boot settings.
These protocols work through a Baseboard Management Controller (BMC).
BMC is an embedded chip independent of the server's CPU/OS.
IPMI is older (developed in the 1990s); Redfish is its modern replacement.
IPMI stands for Intelligent Platform Management Interface.
Redfish is developed by the DMTF (Distributed Management Task Force).
IPMI communicates via UDP on port 623; Redfish uses HTTP/HTTPS.
Redfish is more secure, extensible, and developer-friendly than IPMI.
IPMI uses a command-response model, often over LAN.
It supports basic commands like power on/off/reset.
IPMI has serial-over-LAN (SOL) for accessing system consoles.
You can read sensor data (temps, voltages, fans).
It enables remote BIOS configuration and boot device selection.
It works even if the server’s OS is down.
Common tools: ipmitool, OpenIPMI.
Example: ipmitool -I lanplus -H <ip> -U admin -P pass chassis power status
Security concerns: weak authentication, unencrypted traffic in older versions.
Often used in legacy environments, though support is waning.
Redfish is a RESTful API built on HTTPS and JSON.
It's human-readable, machine-friendly, and easily scriptable.
It exposes server resources via URIs (like /redfish/v1/Systems/1).
It supports OAuth, HTTPS, and TLS for security.
Redfish is vendor-neutral and works across Dell, HPE, Supermicro, etc.
You can manage power, thermal data, fans, memory, BIOS, and RAID.
Redfish supports firmware updates over the network.
It integrates with modern DevOps and automation tools.
Redfish can be explored with a browser or curl.
Example: curl -k -u admin:pass https://<BMC-IP>/redfish/v1/
Both IPMI and Redfish expose health metrics from the BMC.
You can monitor CPU temp, fan speed, PSU status, voltage.
Redfish offers a JSON tree of sensors for structured access.
Redfish example: /redfish/v1/Chassis/1/Thermal/ shows fans and temps.
IPMI uses SDRs (Sensor Data Records) to represent sensor states.
IPMI command: ipmitool sensor list
Redfish allows real-time polling or event-based alerting (with event subscriptions).
Redfish supports push-based telemetry (using Redfish Eventing).
Redfish schemas are standardized and well-documented.
Sensor thresholds can be configured to trigger alarms.
IPMI v1.5 uses plaintext authentication — highly insecure.
IPMI v2.0 introduced lanplus for encryption, but still weak.
Redfish uses HTTPS/TLS for encrypted communication.
Redfish supports token-based authentication and RBAC.
IPMI is vulnerable to replay attacks and firmware exploits.
Many IPMI ports are exposed publicly — a huge risk.
Redfish promotes secure boot, firmware signing, and update validation.
Secure BMC config = disable IPMI, enforce Redfish over HTTPS only.
Redfish can be integrated with Active Directory or LDAP.
Firmware on the BMC should be regularly updated to patch vulnerabilities.
IPMI: ipmitool chassis power on/off/reset/status.
Redfish: PATCH /redfish/v1/Systems/1 with { "ResetType": "ForceOff" }.
Redfish enables graceful shutdown or force reset.
You can change the boot order remotely via Redfish.
Redfish supports setting boot options: PXE, USB, disk, etc.
IPMI uses chassis bootdev for similar functionality.
Redfish allows scheduled reboots and firmware update triggers.
You can script power cycling for batch operations with Redfish.
Redfish allows automated provisioning via boot settings + image deployment.
IPMI lacks modern orchestration compatibility.
Redfish supports firmware update upload via HTTPS POST.
URI: /redfish/v1/UpdateService
You can patch BIOS, BMC, and peripheral firmware.
Redfish supports multipart upload of binary payloads.
It enables staged updates, rollback, and validation.
Redfish logs all updates for audit tracking.
IPMI requires external tools or vendor software for firmware upgrades.
Redfish allows chaining updates across a fleet.
You can automate firmware compliance via Ansible/Redfish.
Redfish makes data center lifecycle management easier.
IPMI tools: ipmitool, OpenIPMI, FreeIPMI.
Redfish tools: Redfishtool, Python Redfish client, Postman, Ansible modules.
Redfish integrates with DCIM platforms like HPE OneView, Dell iDRAC, Red Hat Satellite.
Redfish schemas are publicly available and machine-parsable.
DMTF provides a Redfish emulator for learning.
GitHub: DMTF/Redfish-Tools repo contains tools and validators.
Redfish API supports versioning and schema discovery.
Python bindings for Redfish: python-redfish, Sushy.
Redfish can be used in Kubernetes clusters for bare-metal provisioning.
Redfish Explorer UI tools exist for training and testing.
Remote power control in racks without physical access.
Health checks and predictive maintenance (fan failures, temps).
Firmware compliance validation across servers.
BIOS settings tuning for virtualization or performance.
Automating server provisioning workflows.
Detecting failed components for ticketing automation.
Monitoring PSU redundancy in real time.
Configuring secure boot policies at scale.
Integrating server control into CI/CD pipelines.
Redfish supports composable infrastructure and rack-level orchestration.
Redfish is now the industry-preferred OOB protocol.
Major vendors (Dell, HPE, Lenovo, Supermicro) support Redfish natively.
IPMI is still around for legacy systems.
Redfish supports multi-node systems and chassis aggregation.
Redfish is growing to support NVMe-oF and SmartNICs.
IPMI will likely be deprecated in the next decade.
Redfish is key for modern server fleet automation.
Learn from: redfish.dmtf.org
Try Redfish in Postman or curl to practice real API calls.
Secure your BMCs, use Redfish over HTTPS, and disable old IPMI interfaces.
Essential AI knowledge: Exam Weight 38%
1.1 Describe the NVIDIA software stack used in an AI environment.
1.2 Compare and contrast training and inference architecture requirements and considerations.
1.3 Differentiate the concepts of AI, machine learning, and deep learning.
1.4 Explain the factors contributing to recent rapid improvements and adoption of AI.
1.5 Explain the key AI use cases and industries.
1.6 Explain the purpose and use case of various NVIDIA solutions.
1.7 Describe the software components related to the life cycle of AI development and deployment.
1.8 Compare and contrast GPU and CPU architectures.
Here's a summary covering the Essential AI Knowledge section of an NVIDIA AI certification or foundational course (Exam Weight 38%). Each line highlights a key concept or detail related to the listed subtopics:
NVIDIA offers a full AI software stack optimized for its hardware.
The stack starts with CUDA, NVIDIA’s parallel computing platform.
cuDNN is a GPU-accelerated library for deep neural networks.
TensorRT optimizes trained models for high-performance inference.
NVIDIA Triton Inference Server simplifies deploying models at scale.
NVIDIA DeepStream is used for real-time video analytics.
RAPIDS accelerates data science pipelines using GPUs.
TAO Toolkit allows transfer learning with NVIDIA-optimized models.
NGC (NVIDIA GPU Cloud) provides containers and pretrained models.
NVIDIA's AI stack is designed to maximize performance across the full AI lifecycle.
Training requires high compute, large memory, and fast communication.
Training workloads involve forward and backward passes (gradient updates).
GPUs like the A100 are optimized for training due to their tensor cores and VRAM.
Inference is about fast, cost-efficient prediction from trained models.
Inference architecture focuses on throughput and latency optimization.
Energy efficiency and cost per inference are key considerations.
TensorRT helps shrink model size and boost inference performance.
Edge inference may use Jetson devices for local, low-latency use cases.
Datacenter inference uses multi-GPU setups or inference servers.
Training is compute-intensive, inference is often latency-sensitive.
AI is the broad goal of machines mimicking human intelligence.
Machine Learning (ML) is a subset focused on learning patterns from data.
Deep Learning (DL) is a specialized ML approach using neural networks.
AI includes rule-based systems, planning, and perception.
ML includes supervised, unsupervised, and reinforcement learning.
DL relies on multi-layered (deep) neural networks like CNNs or RNNs.
Not all AI uses learning; ML is data-driven, AI may not be.
DL outperforms traditional ML on tasks like image recognition.
All deep learning is machine learning, but not all ML is deep.
AI → ML → DL (nested hierarchy of technologies).
Increased computing power from GPUs and TPUs.
Massive datasets from sensors, internet, and IoT.
Open-source libraries like TensorFlow, PyTorch, and Keras.
Breakthroughs in neural network architecture (e.g., transformers).
Improved algorithms and training techniques (e.g., Adam optimizer).
Cloud platforms enable scalable and accessible AI training.
Availability of pretrained models and transfer learning.
Growing demand across industries for automation and analytics.
AI-specific hardware accelerates research and deployment.
Investment from academia, startups, and Big Tech fuels innovation.
Healthcare: disease diagnosis, drug discovery, medical imaging.
Retail: customer analytics, recommendation engines, inventory prediction.
Finance: fraud detection, algorithmic trading, risk modeling.
Manufacturing: predictive maintenance, defect detection, automation.
Automotive: driver assistance systems, autonomous vehicles.
Smart Cities: traffic management, surveillance, public safety.
Media & Entertainment: content creation, video upscaling, personalization.
Energy: grid optimization, predictive outage detection.
Agriculture: crop monitoring, yield prediction, smart irrigation.
Education: personalized learning, automated grading, chatbots.
DGX Systems: purpose-built supercomputers for AI training.
Jetson: edge AI computing for robotics, drones, and IoT devices.
Triton Inference Server: manage and serve models at scale.
TAO Toolkit: simplifies training with transfer learning.
NVIDIA Clara: AI for healthcare and medical imaging.
NVIDIA Isaac: robotics development and simulation.
DeepStream SDK: AI-based video analytics.
NVIDIA Omniverse: digital twin and collaboration platform.
Riva: speech AI SDK for conversational applications.
Morpheus: cybersecurity framework using AI.
Data Collection: sensors, scraping, logs, APIs.
Data Labeling & Preparation: cleaning, tagging, augmenting.
Model Selection & Training: using frameworks like PyTorch or TensorFlow.
Model Optimization: quantization, pruning, TensorRT conversion.
Validation & Testing: performance metrics, confusion matrix.
Deployment: edge, cloud, or datacenter via Triton or containers.
Monitoring: drift detection, accuracy, system health.
Model Update: retraining on new data (continual learning).
Lifecycle tools include: TAO Toolkit, Triton, DeepStream, NGC.
DevOps for AI (MLOps) ensures reliability and reproducibility.
CPUs: few powerful cores, optimized for sequential tasks.
GPUs: many smaller cores, ideal for parallel processing.
CPUs excel at general-purpose computing and decision logic.
GPUs excel at large matrix operations and floating-point arithmetic.
CPUs have high clock speed but lower thread count.
GPUs offer massive parallelism, critical for training DL models.
GPU memory bandwidth is higher, enabling faster data transfer.
NVIDIA’s GPUs include specialized Tensor Cores for AI.
GPUs reduce training time from weeks to hours.
For AI, training workloads benefit most from GPU acceleration.
CUDA is the foundation for all GPU programming with NVIDIA.
AI solutions must be optimized at both software and hardware levels.
Deployment includes balancing latency, throughput, and power.
Model compression helps fit large models into edge devices.
GPUs are now essential for all stages of AI workflow.
Deep learning models grow in complexity—scaling matters.
Transfer learning helps when labeled data is limited.
NVIDIA's ecosystem supports end-to-end AI—from dev to deploy.
TensorRT bridges training frameworks with efficient deployment.
AI success depends on hardware, algorithms, and data.
Use DGX stations for deep learning at scale.
Leverage Jetson Nano or Orin for edge AI.
Use NGC for pre-built AI containers and workflows.
Rely on TAO Toolkit for low-code model customization.
Monitor models in production to maintain accuracy.
Edge AI enables real-time decision-making near data source.
GPUs continue to evolve for larger and more efficient AI models.
AI engineers need to understand hardware constraints.
NVIDIA’s stack supports both research and enterprise use.
Mastery of AI concepts and NVIDIA tools opens career pathways in the AI era.
https://www.nvidia.com/en-us/learn/certification/