Blog Posts

Step-by-step overview of NVIDIA's Agentic AI certification path, covering AI agents, multi-agent systems, planning, tool use, evaluation, governance, deployment, and preparation strategies for building production-ready Agentic AI applications.

Learn how modern Agentic AI systems use planning, tool calling, memory, evaluation, reflection, and workflow orchestration to solve complex real-world tasks. Explore the architecture, design patterns, and best practices behind production-grade AI agents.

Learn how Agentic AI workflows combine planning, reasoning, tool use, memory, reflection, and evaluation to solve complex tasks autonomously. Explore common workflow patterns, architectures, and best practices for building production-ready AI agents.

Learn how memory enables AI agents to retain context, recall past interactions, access knowledge, and execute complex tasks across sessions. Explore working, episodic, semantic, procedural, retrieval, and shared memory patterns used in modern agentic AI systems.

Learn how to evaluate Agentic AI systems using end-to-end and component-level evaluations. Discover practical techniques for error analysis, trace inspection, LLM-as-a-judge, objective and subjective metrics, and building reliable evaluation pipelines that drive continuous improvement in AI agents.

Learn how Error Analysis helps diagnose failures in Agentic AI systems by identifying bottlenecks, inspecting traces, and measuring component-level performance. Discover practical techniques for root cause analysis, observability, and continuous improvement of AI agents in production.

Learn how to systematically diagnose, measure, and improve failures in Agentic AI systems using error analysis. Discover how traces, component-level evaluations, root cause analysis, and observability help identify bottlenecks and drive continuous improvement in AI agent performance.

Discover how Agentic AI systems leverage tool calling to interact with APIs, databases, search engines, and enterprise applications. Learn how tool use transforms large language models from conversational assistants into autonomous agents capable of retrieving information, executing actions, and orchestrating real-world workflows.

Learn how Agentic AI systems generate, execute, and refine code to solve complex problems, perform calculations, automate workflows, and interact with external systems. Explore execution loops, self-correction, sandboxing, and the role of code execution in building powerful autonomous AI agents.

Learn how the Model Context Protocol (MCP) standardizes access to tools, resources, and external systems for AI applications. Discover how MCP enables interoperability between AI agents, data sources, and enterprise services, reducing integration complexity and accelerating the development of Agentic AI systems.

Learn how to optimize Agentic AI systems for latency, cost, and scalability without sacrificing output quality. Explore benchmarking techniques, bottleneck analysis, parallel execution, model selection strategies, and practical approaches for improving the performance of production AI agents.

Learn how multiple AI agents collaborate to solve complex tasks through specialization, coordination, and delegation. Explore multi-agent architectures, communication patterns, manager-worker systems, and best practices for building scalable Agentic AI applications.

Learn how Model Fusion combines information from multiple modalities and machine learning models to improve prediction accuracy and robustness. Explore early fusion, intermediate fusion, and late fusion techniques used in modern multimodal AI systems such as vision-language models, autonomous vehicles, and conversational AI applications.

Learn how to deploy AI agents reliably in production using containerization, orchestration, observability, evaluation pipelines, guardrails, retries, scaling strategies, and resilient architectures. Explore best practices for running agentic systems across cloud environments while maintaining performance, reliability, security, and cost efficiency.

Learn how to deploy Agentic AI systems to production using containerization, Kubernetes, inference services, observability, evaluation pipelines, guardrails, memory systems, and scalable orchestration. Explore best practices for reliability, fault tolerance, security, monitoring, and cost optimization when operating AI agents at scale.

📙 Index of AI-AgenticAI posts

A comprehensive learning path for deep learning, covering foundational concepts, optimization techniques, project structuring, convolutional neural networks, and sequence models. This guide provides a structured approach to mastering deep learning through the Deep Learning Specialization DLS.

Explore the hypothesis and intuition behind neural networks, including their structure, activation functions, and how they process inputs to produce outputs.

Understand how forward propagation works in neural networks. Learn how inputs move through layers, how weights and biases transform data, and how activation functions generate predictions in deep learning models.

Learn how to represent neural networks in a vectorized form, transforming scalar equations into efficient matrix operations for scalable and optimized computations.

A simple example of applying neural networks is predicting logical operations like AND and OR. By choosing appropriate weights and bias, a single logistic neuron can simulate these gates. This illustrates the power of neural networks to represent complex functions by stacking simple units.

In the previous section, we saw how to implement basic logical gates (AND, OR, NOR) using single neurons. However, some functions like XOR and XNOR cannot be represented by a single neuron. In this post, we will see how adding a hidden layer allows us to model the XNOR function.

Learn how to extend binary classification to multiclass classification using neural networks, where the output layer consists of multiple units representing different classes, and the final prediction is made by selecting the class with the highest output value.

The cost function for neural networks generalizes the logistic regression cost to multiple output units and includes regularization over all weights in the network. This post breaks down the cost function, explaining the double and triple summations, and provides intuition for how it works.

Backpropagation is the algorithm used to minimize the neural network cost function. It computes the gradients of the cost function with respect to the parameters, allowing us to perform gradient descent and update our model.

Gradient checking is a technique to verify the correctness of your backpropagation implementation. Random initialization is crucial for breaking symmetry and allowing the network to learn effectively.

In this post, we will put together all the pieces we've learned about neural networks to understand how to train a neural network effectively. We will cover the cost function, backpropagation, gradient checking, and random initialization, along with key intuitions for each step.

A concise cheat sheet covering core concepts, dimensions, activation functions, forward propagation, cost function, backpropagation, gradient checking, random initialization, training pipeline, and key intuition for neural networks.

📙 Index of AI-DeepLearning posts

Step-by-step overview of NVIDIA certifications for AI and LLM developers, including exam details, learning resources, and preparation guidance, along with core AI infrastructure fundamentals.

A clear introduction to generative AI, explaining how modern AI models create text, images, and other content, and how technologies like transformers, large language models, and deep learning power today's generative systems.

Step-by-step overview of AI model development, including generative AI, large language models, training and inference workflows, GPU computing, and practical learning resources.

A practical guide to selecting the right AI and LLM models based on use case, latency, cost, accuracy, infrastructure, and deployment requirements.

Comprehensive overview of transformer models, including their architecture, key components, and their role in powering large language models and generative AI applications.

Comprehensive guide to Retrieval-Augmented Generation, covering architecture, embeddings, vector databases, document indexing, retrieval strategies, and best practices for building production-ready RAG systems.

A practical guide to Large Language Models (LLMs) and foundation models, covering architectures, training concepts, fine-tuning, inference, embeddings, RAG, and real-world AI application development.

Practical examples of how large language models are integrated into real production systems, from support automation and knowledge retrieval to developer tooling, code generation, and intelligent assistants.

Learn how large language models are deployed in real-world production environments, including system architecture, retrieval augmented generation (RAG), prompt engineering, evaluation, monitoring, and scaling AI-powered applications.

Understand the differences between Ethical AI, Responsible AI, and Trustworthy AI, including their principles, governance models, operational practices, and role in building safe and reliable AI systems.

Learn how Generative Adversarial Networks (GANs) work, including generators, discriminators, adversarial training, minimax optimization, image synthesis, and modern generative AI applications.

Learn how U-Net works, including encoder-decoder architectures, skip connections, image segmentation, denoising, diffusion models, and modern generative AI applications. Discover why U-Net remains one of the most influential neural network architectures in computer vision.

Learn how OpenAI's CLIP model bridges vision and language by mapping images and text into a shared embedding space. Explore CLIP encodings, similarity search, zero-shot classification, and how CLIP powers modern text-to-image generation systems such as Stable Diffusion.

Learn how Diffusion Models generate realistic images by progressively adding and removing noise. Explore forward and reverse diffusion processes, U-Net architectures, denoising techniques, latent diffusion, and the foundations behind modern generative AI systems such as Stable Diffusion.

Explore the economic impact of Generative AI across industries, including productivity gains, automation, workforce transformation, and the creation of new digital economies powered by large language models and AI systems.

Practice questions and explanations for the NVIDIA Certified Associate Generative AI (NCA-GENL) certification exam, covering LLMs, transformers, embeddings, vector databases, prompt engineering, AI infrastructure, responsible AI, and generative AI fundamentals.

📙 Index of AI-GenAI posts

Comprehensive guide to NVIDIA AI infrastructure covering GPU architecture, accelerated computing, training vs inference workloads, data center networking, storage design, virtualization, and operational best practices.

Comprehensive overview of modern AI infrastructure covering CPU, GPU, and DPU architectures, accelerated computing models, cluster scaling, high-speed networking (InfiniBand and RoCE), storage integration, and power and cooling considerations for AI data centers.

Overview of NVIDIA's AI programming model, including core libraries (CUDA, NCCL, cuDNN), training vs inference workloads, and compute scaling models (data parallelism and model parallelism) for AI infrastructure.

Learn how pinned memory (page-locked memory) improves CPU-to-GPU data transfer performance in CUDA, deep learning, and high-performance AI workloads using direct memory access (DMA).

Comprehensive overview of the RAPIDS ecosystem covering GPU accelerated DataFrames, machine learning, graph analytics, CUDA execution, distributed computing with Dask and NCCL, TensorRT integration, and large-scale AI data processing pipelines on NVIDIA GPUs.

How NVIDIA's Data Center GPU Manager works — the nv-hostengine daemon, key DCGM metric field IDs, XID error codes and diagnostic levels, the DCGM Exporter DaemonSet and Prometheus integration on Kubernetes, PromQL queries and alert rules for GPU clusters, DCGM-driven KEDA autoscaling, and MIG instance monitoring.

How NVIDIA Base Command Manager (BCM) operates an entire GPU cluster — bare metal provisioning and node imaging, the head node vs compute node architecture, Slurm and Kubernetes workload manager integration, user and group management, the cmsh CLI and REST API, and how BCM, DCGM, and SMI fit together at different layers of the stack.

Slurm from the ground up — the controller/node-daemon architecture, partitions and QOS, job submission with sbatch/srun/salloc, GPU allocation with GRES, multi-node MPI jobs, running containers under Slurm with enroot and pyxis, multifactor job priority, preemption, job arrays and dependencies, node health and cgroup enforcement, topology-aware scheduling, the slurmrestd API, Slurm vs Kubernetes, and a set of interview questions with answers.

Comprehensive overview of NVIDIA TensorRT covering ONNX model optimization, CUDA kernel fusion, FP16 and INT8 inference, TensorRT-LLM, GPU memory optimization, Triton Inference Server integration, and production-scale AI inference pipelines on NVIDIA GPUs.

Comprehensive overview of NVIDIA NCCL covering GPU-to-GPU communication, AllReduce operations, distributed AI training, CUDA integration, tensor synchronization, multi-node scaling, InfiniBand networking, and high performance communication for large-scale AI and HPC workloads.

Comprehensive overview of ONNX covering portable neural network model formats, cross-framework interoperability, ONNX Runtime, TensorRT integration, GPU accelerated inference, model optimization, and production AI deployment across heterogeneous hardware platforms.

Comprehensive overview of LangChain covering AI agents, Retrieval-Augmented Generation (RAG), prompt orchestration, tool calling, memory management, vector databases, multi-step LLM workflows, and production GenAI application development.

Comprehensive overview of NVIDIA NeMo covering large language model training, distributed GPU scaling, Megatron-LM integration, Retrieval-Augmented Generation (RAG), NeMo Retriever, TensorRT-LLM optimization, and enterprise AI deployment pipelines for production-scale generative AI systems.

Comprehensive overview of NVIDIA Megatron-LM covering distributed transformer training, tensor and pipeline parallelism, NCCL communication, CUDA optimization, mixed precision training, trillion-parameter scaling, and large-scale GPU accelerated language model infrastructure.

NVIDIA Triton Inference Server and vLLM compared — PagedAttention and continuous batching mechanics, TensorRT-LLM vs vLLM vs Triton tradeoff table, when to use each for production LLM serving, and Kubernetes deployment patterns for both.

Comprehensive overview of NVIDIA Riva covering real-time speech AI, Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Text-to-Speech (TTS), multilingual conversational AI, custom model deployment, GPU acceleration, Kubernetes deployment, and production-grade voice AI architectures.

Comprehensive overview of the NVIDIA NGC Catalog covering GPU optimized containers, CUDA and TensorRT environments, NeMo and Triton deployments, pretrained AI models, Kubernetes integration, NVIDIA NIM microservices, and enterprise-scale AI infrastructure for accelerated computing workloads.

Networking fundamentals for AI-centric data centers — the four network planes, DMA and RDMA mechanics, InfiniBand vs RoCE vs Ethernet with real numbers, the GPU interconnect hierarchy from PCIe through NVLink/NVSwitch to InfiniBand, BlueField DPUs, and how the GPU and Network Operators automate all of it on Kubernetes.

Storage architectures for AI infrastructure — the hot/warm/cold tiering model with real throughput numbers, GPUDirect Storage's direct path from NVMe to GPU memory, NVMe-oF, checkpoint math for large models, erasure coding for durability, and cloud vs on-prem storage tradeoffs.

Comprehensive overview of monitoring and operations for AI infrastructure, covering GPU monitoring tools (DCGM, BCM), infrastructure monitoring (Prometheus, Grafana), cluster orchestration (Kubernetes, Slurm), power and cooling monitoring, high availability, failure scenarios, security monitoring, GPU utilization optimization, capacity planning, multi-GPU scaling strategies, lifecycle management, logging systems, and alerting best practices.

📙 Index of AI-Infrastructure posts

A structured learning path through machine learning fundamentals, covering supervised learning (regression and classification), neural networks, model evaluation and system design, unsupervised learning, recommenders, and reinforcement learning — based on the Coursera Machine Learning course and specialization.

A complete self-study roadmap built entirely from Stanford University's publicly available AI courses. Learn mathematics, machine learning, deep learning, reinforcement learning, large language models, AI systems, RAG, agentic AI, and production deployment through a structured path from foundations to real-world AI engineering.

Beginner-friendly introduction to machine learning, covering key concepts, model types, supervised and unsupervised learning, and essential algorithms such as linear regression, logistic regression, decision trees, and clustering.

Learn linear regression in machine learning, including single-variable and multivariate models, hypothesis function, cost function (MSE), gradient descent optimization, feature scaling, assumptions, and real-world implementation examples.

Learn how neural networks evaluate a hypothesis using forward propagation. Understand how inputs pass through layers, weights, and activation functions to produce predictions in machine learning models.

Understanding the bias-variance tradeoff in machine learning, including the concepts of bias and variance, underfitting and overfitting, and strategies to balance model complexity for better generalization.

Learn how cost function regularization helps prevent overfitting in machine learning models by adding a penalty term to the cost function, controlling model complexity, and improving generalization performance.

Understand polynomial regression with practical examples.

Understand the Normal Equation in linear regression, its closed-form solution, mathematical formula, advantages, limitations, and how it compares to gradient descent for model optimization.

Complete guide to logistic regression for binary classification, including the sigmoid function, hypothesis model, cost function, decision boundary, gradient descent, and practical machine learning implementation.

Complete guide to logistic regression for binary classification, including the sigmoid function, hypothesis model, cost function, decision boundary, gradient descent, and practical machine learning implementation.

Learn how Support Vector Machines (SVM) build powerful classification models by finding the optimal separating hyperplane that maximizes the margin between classes. Discover how the margin, regularization parameter C, and kernel functions help SVM handle both linear and non-linear data while improving generalization performance.

Learn how XGBoost works, including gradient boosting, decision trees, residual learning, regularization, and why XGBoost is one of the most powerful machine learning algorithms for structured and tabular data.

Learn how dimensionality reduction simplifies high-dimensional data while preserving important patterns. Explore techniques like PCA and understand how reducing features improves model performance, visualization, and computational efficiency.

Learn how Principal Component Analysis (PCA) reduces the dimensionality of datasets while preserving important information. Understand the intuition, mathematics, and practical uses of PCA in machine learning and data science.

Learn how t-SNE works for dimensionality reduction and data visualization, including high-dimensional embeddings, neighborhood preservation, probability distributions, KL divergence, and clustering visualization.

K-Means is a powerful unsupervised learning algorithm for clustering data into coherent subsets. It iteratively assigns points to the nearest centroid and updates centroids to minimize distortion, making it widely used in practice.

Learn how anomaly detection models identify unusual data points using statistical methods such as Gaussian distributions. Understand how to detect fraud, system failures, and rare events in real-world datasets.

Learn how anomaly detection works using the Gaussian (normal) distribution. Understand how to model data probabilistically, estimate parameters, compute likelihoods, and identify outliers using threshold-based decision making in machine learning systems.

Learn anomaly detection using multivariate Gaussian distribution to identify unusual patterns and correlated outliers in datasets. Understand covariance matrices, parameter estimation, probability density functions, and threshold-based anomaly detection techniques used in machine learning systems.

Comprehensive guide to recommender systems, covering collaborative filtering, content-based filtering, and hybrid approaches, with practical implementation examples and best practices for building effective recommendation engines.

Learn how collaborative filtering powers modern recommender systems by simultaneously learning user preferences and item features from rating data. Understand the optimization objective, matrix factorization approach, and how gradient-based methods enable scalable recommendations.

Learn how classical Photo OCR pipelines detect and read text in images using sliding window text detection, character segmentation, character recognition classifiers, and artificial data synthesis — plus how modern computer vision (CNNs, YOLO, Vision Transformers) has superseded this approach.

Explore techniques for scaling machine learning algorithms to large datasets, including stochastic gradient descent and mini-batch gradient descent. Learn how to efficiently train linear models, logistic regression, and neural networks on millions of examples.

Understand how Stochastic Gradient Descent works and why it is widely used in large-scale machine learning. Learn how SGD updates model parameters using one training example at a time to improve computational efficiency and scalability.

Learn how the MapReduce framework enables distributed computation for large-scale machine learning. Understand how it helps parallelize gradient computation and process massive datasets efficiently across multiple machines.

📙 Index of AI-Machine-Learning posts

Geometric intuition for linear algebra in machine learning — orthogonality and independence, eigenvectors, Singular Value Decomposition (SVD), neural networks as layered transformations, and linear regression as projection.

Linear algebra crash course for ML — scalars, vectors, matrices, transpose, inverse, determinant, dot product, and matrix transformations explained with ML context. Includes the deeplearning.ai math learning path.

Complete MATLAB crash course covering fundamentals, data types, matrices, operators, control flow, functions, and object-oriented programming — everything you need to go from zero to productive in MATLAB.

Complete guide to plotting in MATLAB including line plots, subplots, matrix visualization, styling options, legends, axis control, and exporting figures.

📙 Index of AI-Math posts

AWS Introduction, Global Infra, EC2, VPC, Storage, DB, Security, Monitoring & Migration

Introduction to AWS Global Infrastructure, Route 53, CloudFront, Scalability, Disaster Recovery and Infrastructure Management

Deploy & manage infrastructure using AWS Beanstalk & Cloudformation

Overview of available Compute Services in AWS and how to use them

Overview of other AWS Services like Serverless, Lambda, API Gateway, Step Function, ECS, Fargate

Overview of available streaming services in AWS & when to use them

Overview of available AWS Storage Services: S3, EBS, EFS, Storage Gateway, FSx, Glacier

Overview of available AWS DB Services, their types, use cases, pros and cons.

Overview of AWS Networking & Content Delivery Services with VPC, Subnet, Security Group, NACL, VPN, Direct Connect, Transit Gateway

How to secure AWS resources and manage user access effectively using IAM, KMS, and other security tools.

Design to enable everyone build secure and reliable application with AWS Well Architected Framework

Overview of Monitoring and Observability services in AWS including CloudWatch, CloudTrail, Config, and X-Ray.

Use AWS Code Commit, Code Build, Code Deploy & Code Pipeline to automate code build, test & deploy on AWS

AWS Pricing Model, Free Tier, Billing & Cost Management Dashboard, AWS Pricing Calculator, Cost Allocation Tag, Cost Explorer, Budgets & Alerts, AWS Support Plan

Cheat Sheet for AWS Solutions Architect Associate

Connect with EC2 Instance, Configure AWS CLI, Dry Run, Decode Error Message, MFA with CLI

Summary of AWS services organized by category, along with brief descriptions of each service.

📙 Index of AWS posts

Summary of Azure services organized by category, along with brief descriptions of each service.

Introduction to Azure Global Infrastructure, CDN and Caching

Overview of other Azure Services like Serverless, Functions, Logic Apps

Overview of available Compute Services in Azure and how to use them

Overview of Azure Services like Serverless, Functions, Logic Apps

Overview of available streaming services in Azure: Event Grid, EventHubs, Service Bus

Overview of available Azure Storage Services: Blob, File, Disk, Table, Queue

Overview of available Azure DB Services: SQL, NoSQL, In-Memory, and NewSQL databases

Overview of Azure Networking & Content Delivery Services with: Traffic Manager, Virtual Network (VNet), Subnets, Network Security Groups, VPN Gateway, ExpressRoute, Front Door, Load Balancers, etc.

How to secure Azure resources and manage user access effectively using Acrive Directory, Conditional Access, MFA, and more.

How to secure Azure resources and manage user access effectively using Resource Manager, RBAC, and Policies

Overview of Monitoring and Observability services in Azure including Azure Monitor, Application Insights, Log Analytics, Alerts and more.

Use Azure DevOps and GitHub Actions to build, test, and deploy applications on Azure

Azure Pricing, Cost Management + Billing, Azure Advisor, Spending limit, Support Plans, SLA

📙 Index of Azure posts

A working engineer's map of Kubernetes — the reconciliation model underneath the objects, the scheduling and networking internals that bite at scale, and how GPU workloads actually get placed and run.

The complete journey of a container image — OCI manifest, content-addressable layers, registry pull flow, containerd snapshots, OverlayFS rootfs assembly, pod sandbox creation, and how a container process finally starts inside a pod.

What a container actually is from the OS and Kubernetes perspectives — Linux namespaces, cgroups, OverlayFS image layers, the OCI spec, the container runtime stack from CRI to runc, and container security primitives.

What a Kubernetes pod actually is from the OS and Kubernetes perspectives — Linux namespaces, cgroups, the pause container, shared networking, pod lifecycle, init containers, and what happens between kubectl apply and your process running.

Deep dive into the Kubernetes API Server — authentication, authorization, RBAC, admission controllers, schema validation, the watch cache, optimistic concurrency, and API Priority & Fairness explained with diagrams.

How Kubernetes allocates CPU and memory — requests vs limits, QoS classes, ResourceQuota, LimitRange, node allocatable capacity, GPU resources, and best practices for production workloads.

etcd internals for Kubernetes engineers — Raft consensus, leader election, MVCC and resourceVersion, snapshots, log compaction, the watch API, quorum loss behavior, and etcdctl operations for backup and defrag.

Inside the Kubernetes Scheduler — scheduling queue, filtering, scoring, preemption, binding, topology spread constraints, the scheduler plugin framework, and where Kueue fits for batch and AI workloads.

What the kubelet actually does on every node — the sync loop and pod sources, the Container Runtime Interface, the Pod Lifecycle Event Generator, node heartbeats and leases, static pods, cgroup enforcement, the eviction manager, probe execution, and the Device Manager that hands out GPUs.

How Kubernetes controllers and informers work — reconciliation loops, shared informers, local caches, work queues with exponential backoff, owner references, generation tracking, finalizers, and the operator pattern.

How Kubernetes networking works from the ground up — the flat Pod network model, CNI plugins, kube-proxy and iptables, Services (ClusterIP, NodePort, LoadBalancer, Headless), CoreDNS service discovery, Ingress, NetworkPolicies, and why overlay networks are replaced by InfiniBand for GPU training.

How Kubernetes persistent storage works — Volumes vs PersistentVolumes, PersistentVolumeClaims, StorageClass dynamic provisioning, access modes, reclaim policies, the CSI driver model, StatefulSet stable storage, and storage requirements for GPU training checkpoints.

Helm from the ground up — charts, releases, repositories, values, and Go templates. Essential commands, values overrides, chart structure, Helm hooks, Helm vs Kustomize, and real examples using GPU Operator, NIM, and Kueue.

The three pillars of observability on Kubernetes — metrics with Prometheus and PromQL, visualization with Grafana, structured logging with Loki and Fluent Bit, distributed tracing with OpenTelemetry and Tempo, the OTel Collector pipeline, and GPU-specific observability with DCGM on DGX clusters.

How Kubernetes schedules GPUs — Device Plugin gRPC protocol, nvidia-container-toolkit, GPU Operator ClusterPolicy, MIG profiles on H100, time-slicing, DCGM monitoring, GPU health tainting, and GPU sharing strategies for AI workloads.

Why a DGX cluster trains 10× faster than a regular GPU cluster — the full network stack explained: RDMA, GPUDirect RDMA, InfiniBand, SR-IOV, Multus CNI, and how the NVIDIA Network Operator automates all of it on Kubernetes.

How Kubernetes DRA replaces Device Plugins for GPU scheduling — ResourceClaim, DeviceClass, ResourceSlice, CEL selectors, topology-aware allocation, the NVIDIA GPU DRA driver, and how DRA and Device Plugins coexist during migration.

Kubernetes at hyperscale — official SLIs and SLOs, pod startup latency breakdown, watch storms, LIST scalability, API Priority & Fairness, etcd bottlenecks, scheduler throughput, horizontal API Server scaling, and benchmarking with ClusterLoader2 and KWOK.

There is no single technique to keep GPUs busy. A layer-by-layer map of AI inference optimization — from quantization and inference engines through KV cache, continuous batching, GPU sharing, scheduling, and cache-aware routing up to parallelism, autoscaling, and the networking underneath.

Deep dive into Kueue — the CNCF project that adds job queuing, resource quotas, gang scheduling, preemption, and fair sharing to Kubernetes. Covers ResourceFlavors, ClusterQueues, LocalQueues, Cohorts, and integration with PyTorchJob and batch workloads.

How Kubernetes orchestrates distributed AI training across multiple DGX nodes — Kubeflow Training Operator, PyTorchJob, gang scheduling, NCCL, AllReduce, intra-node NVLink vs inter-node InfiniBand, and fault-tolerant checkpointing.

How the kubelet Topology Manager co-locates GPUs, CPUs, memory, and NICs on the same NUMA node — the difference between 1 μs and 100 ns RDMA latency on DGX. Covers NUMA basics, CPU Manager, Memory Manager, hint collection, and the four Topology Manager policies.

What NVIDIA NIM is and how it works — NIM profiles, NGC model cache, the NIM Operator, NIMService CRD, OpenAI-compatible API, GPU-aware deployment on Kubernetes, and how NIM compares to raw Triton and vLLM for production inference on DGX Cloud.

How to autoscale GPU workloads on Kubernetes — DCGM metrics pipeline to HPA, KEDA ScaledObjects with Prometheus triggers, Cluster Autoscaler for GPU node groups, scale-down protection for training jobs, and KEDA + Kueue integration for queue-depth-driven scaling.

Why fine-tuning exists, the memory math that makes full fine-tuning prohibitive, LoRA's low-rank decomposition trick, QLoRA on quantized base models, instruction tuning vs RLHF vs DPO, and how to run fine-tuning jobs on a DGX Kubernetes cluster with NeMo and PyTorchJob.

How standard self-attention creates an O(N²) memory bottleneck, the IO-aware tiling algorithm that Flash Attention uses to stay in SRAM, Flash Attention 2 and 3 improvements, Grouped Query Attention and its KV cache impact, PagedAttention, and how these optimizations flow through TensorRT-LLM and NIM on H100.

Foundational concepts of Kubernetes and the cloud native ecosystem, covering container orchestration, architecture, observability, and core Kubernetes components.

A 60-question practice exam for the Kubernetes and Cloud Native Associate (KCNA) certification, weighted to the official exam domains — Kubernetes Fundamentals, Container Orchestration, Cloud Native Application Delivery, and Cloud Native Architecture.

A second 60-question practice exam for the Kubernetes and Cloud Native Associate (KCNA) certification, covering the same official exam domains with a fresh question set for additional practice.

A 60-question practice exam for the Kubernetes and Cloud Native Security Associate (KCSA) certification, covering the 4Cs security model, cluster component security, Kubernetes threat modeling, platform security, and compliance frameworks.

A second 60-question practice exam for the Kubernetes and Cloud Native Security Associate (KCSA) certification, covering the same official security domains with a fresh question set for additional practice.

📙 Index of kubernetes posts

Discover the certification roadmap, key skills, hands-on labs, and tips to ace Terraform exams and become proficient in cloud infrastructure as code.

A beginner-friendly guide to building and managing cloud infrastructure as code. Learn installation, configuration, providers, and resource management.

Master Terraform configurations: Learn how to read, generate, and modify Terraform files efficiently. Practical tips and best practices for scalable and maintainable IaC setups.

Learn how to create and use reusable TF modules for scalable, maintainable, and shareable infrastructure configurations. Best practices and versioning tips included.

Learn how to implement, manage, and maintain TF state using backends. Best practices for safe, collaborative, and scalable infrastructure management.

The complete Terraform workflow — Write, Plan, Apply, and Destroy — with what each step does internally, essential commands, the production CI/CD pattern using plan files, importing existing infrastructure, and verbose logging.

Understand core Infrastructure as Code (IaC) concepts and the role of Terraform in automating cloud infrastructure. Declarative vs imperative, idempotency, IaC tool comparison, and why Terraform's provider model wins at multi-cloud scale.

Discover TF Cloud features, workflows, and the Sentinel policy-as-code framework. Learn how to automate, secure, and collaborate effectively on cloud infrastructure.

Quick reference for commonly used TF string functions. Learn how to manipulate strings efficiently in your Terraform configurations with this handy cheatsheet.

📙 Index of Terraform posts