# AI Infrastructure Learning Center Understand how to build, maintain, and improve the infrastructure behind AI and high-performance workloads. Our expert technical guides, architectural deep dives, and data pipeline frameworks have everything you need to know. - [AI Checkpoints: How They Work and More](/learn/ai-checkpoints) — AI & ML Fundamentals / AI basics AI checkpoints preserve model progress for faster recovery, fine-tuning, deployment, and reproducibility. Learn how high-performance storage accelerates checkpoint I/O, eliminates bottlenecks, and keeps GPUs productive. - [AI FAQ: The Most Frequently Asked Questions About AI](/learn/frequently-asked-questions-about-ai) — AI & ML Fundamentals / AI basics Get clear, practical answers to the most common AI questions—from how AI learns and generates content to its real-world applications, risks, benefits, environmental impact, and future. - [AI Foundation Models: How They Work and More](/learn/ai-foundation-models) — AI & ML Fundamentals / AI basics Explore how AI foundation models learn, adapt, and power generative applications—and why high-performance data infrastructure is essential to eliminate I/O bottlenecks, maximize GPU utilization, and accelerate training at scale. - [AI Pipeline: What It Is & Why It's Important](/learn/ai-pipeline) — AI & ML Fundamentals / AI basics AI pipelines automate the path from data ingestion and preprocessing to model training, evaluation, and prediction. Learn how high-performance hybrid cloud infrastructure removes data bottlenecks and accelerates AI at scale. - [AI Storage TCO & Token Economics: How Storage Determines AI Profitability](/learn/ai-storage-tco-token-economics-how-storage-determines-ai-profitability) — AI Infrastructure & Inference / AI storage Storage—not compute alone—determines AI profitability. Learn how eliminating data bottlenecks boosts GPU utilization, lowers cost per token, and turns AI infrastructure from a cost center into a profit engine. - [AI Storage for Model Training](/learn/ai-storage-for-training) — AI Infrastructure & Inference / AI storage AI training demands billions of random reads and terabyte-scale checkpoint writes at once. Purpose-built, parallel storage eliminates data bottlenecks, keeps GPUs productive, and maximizes infrastructure ROI. - [AI Storage vs. Legacy Storage: A Migration Guide for the AI Era](/learn/ai-storage-vs-legacy-storage) — AI Infrastructure & Inference / AI storage Why legacy NAS, SAN, and Object storage fail under petabyte-scale GPU workloads — and how to evaluate parallel file systems for seamless migration. - [AI Storage: Its Architecture, Benefits, & Uses](/learn/what-is-ai-storage) — AI Infrastructure & Inference / AI storage AI workloads demand storage that keeps data flowing at GPU speed. Learn how scalable, high-performance AI storage eliminates bottlenecks, accelerates insights, and improves infrastructure efficiency from edge to cloud. - [AI: A Complete Guide in Simple Terms](/learn/what-is-ai) — AI & ML Fundamentals / AI basics Understand how AI works, from models and pipelines to enterprise applications—and learn why high-performance, scalable data infrastructure is essential to eliminate bottlenecks, accelerate GPUs, and maximize AI ROI. - [AWS: Everything You Need to Know About the Amazon Cloud Computing Platform](/learn/what-is-aws) — Cloud & Data Management / Cloud platforms Explore how AWS delivers scalable compute, storage, databases, networking, security, and AI—and learn how WEKA accelerates data-intensive cloud workloads with low-latency performance, elastic scaling, and seamless data portability. - [BeeGFS Parallel File System Explained](/learn/beegfs-parallel-file-system) — Storage Architecture / Parallel file systems BeeGFS scales throughput for traditional HPC, but its metadata bottlenecks and missing enterprise capabilities can constrain modern AI pipelines. See how a modern parallel file system keeps GPUs fed while protecting critical data. - [Big Data & Machine Learning (How Do They Relate?)](/learn/big-data-machine-learning) — AI & ML Fundamentals / Machine learning Big data fuels machine learning, but only high-performance infrastructure can turn massive datasets into timely insight. Learn how cloud-scale storage and compute eliminate bottlenecks, accelerate AI, and drive smarter decisions. - [Big Data Architecture (Best Practices, Tips & Tools)](/learn/big-data-architecture) — HPC / Analytics Learn how to design big data architecture that unifies ingestion, processing, analytics, and governance—then apply proven practices and high-performance infrastructure to eliminate bottlenecks, scale AI workloads, and accelerate insights. - [Big Data Integration (Overcoming the Challenges)](/learn/big-data-integration) — Cloud & Data Management / Data pipelines Unify fragmented data sources into a high-performance foundation for faster analytics and smarter decisions. Learn how to overcome ingestion, availability, governance, security, and scalability challenges with modern cloud infrastructure. - [Block Storage vs. Object Storage: When to Use Each](/learn/block-storage-vs-object-storage) — Storage Architecture / Storage types Compare block storage’s low-latency performance with object storage’s cost-efficient scalability—and learn which architecture best fits databases, backup and DR, IoT, and modern high-performance workloads. - [CPU vs GPU | Best Use Cases For Each](/learn/cpu-vs-gpu) — GPUs & AI Hardware / GPU basics Understand where CPUs excel at flexible, sequential workloads and GPUs dominate massively parallel computing—then learn how the right architecture can accelerate AI, machine learning, analytics, and other data-intensive applications. - [Cloud Bursting | A Complete Explanation](/learn/cloud-bursting) — Cloud & Data Management / Cloud operations Learn how cloud bursting extends private infrastructure into the public cloud on demand, helping enterprises absorb peak workloads, avoid costly overprovisioning, and scale high-performance applications efficiently. - [Cloud Services: Everything You Need to Know](/learn/cloud-services) — Cloud & Data Management / Cloud concepts Understand how cloud services, deployment models, and SaaS, IaaS, PaaS, FaaS, and GPUaaS work—then learn how WEKA removes data bottlenecks to accelerate AI and other demanding workloads across hybrid and multicloud environments. - [Cloud Storage vs Local Storage: Pros and Cons of Each](/learn/cloud-storage-vs-local-storage) — Cloud & Data Management / Cloud storage Compare cloud storage’s elastic scale and cost efficiency with local storage’s performance, control, and security—and learn how a hybrid strategy can optimize AI workloads, resilience, and ROI. - [Cloud Storage: A Complete Guide in Simple Terms](/learn/what-is-cloud-storage) — Cloud & Data Management / Cloud storage Understand how cloud storage works, where file, block, and object architectures fit, and how to balance scalability, security, cost, and performance for modern AI and enterprise workloads. - [Clustered File System](/learn/clustered-file-system) — Storage Architecture / File systems Clustered file systems unify storage across multiple servers, enabling simultaneous data access, higher performance, and resilient operations. Learn how they simplify management, scale capacity, balance workloads, and reduce infrastructure overhead. - [Complete Guide to Genomics Data Storage](/learn/genomics-data-storage) — Cloud & Data Management / Industry Genomics workloads demand secure, scalable storage that can process massive datasets without bottlenecks. Learn how parallel access, hybrid cloud architecture, and resilient data management accelerate sequencing and discovery. - [Computer Vision AI (What Is It & How Does It Work?)](/learn/computer-vision-ai) — AI & ML Fundamentals / AI basics Discover how computer vision AI turns images and video into actionable insights—and why high-performance data infrastructure is essential to accelerate training, maximize GPU performance, and scale real-world applications. - [DataOps (Principles & Best Practices)](/learn/dataops) — Cloud & Data Management / Data pipelines Learn how DataOps unifies people, processes, and automation to deliver trusted data faster—and discover best practices for building scalable, self-service pipelines that accelerate analytics and AI across edge, core, and cloud. - [Deep Learning vs. Machine Learning: Differences Explained](/learn/deep-learning-vs-machine-learning) — AI & ML Fundamentals / Machine learning Understand how machine learning and deep learning differ, where each delivers value, and why high-throughput, low-latency data infrastructure is critical to accelerating AI workloads at scale. - [Distributed File Systems (DFS)](/learn/distributed-file-system) — Storage Architecture / File systems Distributed file systems unify data across locations, delivering transparent access, scalability, resilience, and high performance. Learn how modern DFS architectures eliminate data bottlenecks and accelerate AI, HPC, and cloud workloads. - [Edge Computing vs Cloud Computing: What’s the Difference?](/learn/edge-computing-vs-cloud-computing) — Cloud & Data Management / Cloud concepts Edge computing processes data closer to where it’s created for lower latency and faster decisions, while centralized cloud delivers scale for demanding workloads. Learn how combining both can accelerate AI, analytics, and IoT infrastructure. - [Everything You Need to Know About Cloud Computing and Big Data](/learn/cloud-computing-big-data) — Cloud & Data Management / Cloud concepts Discover how cloud computing solves big data’s toughest scale, processing, and storage challenges—and how WEKA delivers the high-performance, secure infrastructure needed to accelerate analytics across hybrid and multicloud environments. - [GCP: Everything You Need to Know About Google Cloud Platform](/learn/what-is-google-cloud-platform) — Cloud & Data Management / Cloud platforms Explore GCP’s core services, pricing, architecture, security, and competitive position—then see how WEKA accelerates AI and modern workloads and scales storage without downtime. - [GPU Acceleration for High-Performance Computing](/learn/gpu-acceleration) — GPUs & AI Hardware / GPU data path Learn how GPU acceleration offloads parallel workloads from CPUs to speed AI, machine learning, life sciences, and edge computing—and how WEKA’s high-performance data platform keeps GPUs fed for maximum efficiency. - [GPUDirect RDMA: How It Works and More](/learn/what-is-gpudirect-rdma) — GPUs & AI Hardware / NVIDIA platform GPUDirect RDMA moves data directly between GPU memory and RDMA-capable devices, bypassing CPUs and host memory to cut latency, increase bandwidth, and accelerate distributed AI and HPC workloads. - [GPUDirect Storage: How it Works and More](/learn/what-is-gpudirect-storage) — GPUs & AI Hardware / GPU data path Discover how NVIDIA GPUDirect Storage bypasses the CPU to move data directly between storage and GPU memory—reducing latency, increasing throughput, and helping WEKA accelerate data-intensive AI and HPC workloads. - [Global Namespaces](/learn/global-namespaces) — Storage Architecture / File systems Global namespaces unify distributed file systems into a single, location-independent view, simplifying storage management, accelerating access to unstructured data, and enabling seamless scale without disrupting users or existing environments. - [HPC & GPUs Explained (How They Work Together)](/learn/gpu-and-hpc-explained) — HPC / HPC basics Discover how HPC clusters pair GPUs’ massive parallel processing with high-speed storage and networking to accelerate data-intensive AI, analytics, life sciences, and genomic workloads—without letting infrastructure bottlenecks limit performance. - [HPC Applications & Real-World Examples](/learn/hpc-applications) — HPC / HPC basics See how HPC accelerates AI, financial analytics, manufacturing, genomics, and energy—and how WEKA’s high-performance data platform eliminates storage bottlenecks to maximize GPU efficiency, scalability, and ROI. - [HPC Architecture Explained](/learn/hpc-architecture) — HPC / HPC basics Explore how compute, networking, and high-speed storage work together in HPC architecture to accelerate AI and data-intensive workloads—and learn what it takes to eliminate bottlenecks, scale efficiently, and maximize infrastructure ROI. - [HPC Storage Explained](/learn/hpc-storage-explained) — HPC / HPC basics Discover how modern HPC storage combines parallel file systems, NVMe, and object storage to eliminate data bottlenecks, keep CPUs and GPUs productive, and deliver low-latency performance at exabyte scale. - [HPDA (What Is It & Why Is It Important?)](/learn/hpda) — HPC / Analytics High-performance data analytics unites HPC, big data, and advanced analytics to turn massive datasets into real-time insight. Learn how HPDA architecture accelerates AI, life sciences, finance, and other data-intensive workloads. - [How AI Inference Actually Works: Prefill, Decode, and Infrastructure](/learn/how-ai-inference-works) — AI Infrastructure & Inference / Inference mechanics AI inference splits into compute-heavy prefill and memory-intensive decode, with KV cache pressure creating costly bottlenecks. Learn how purpose-built infrastructure breaks the memory wall to deliver scalable, low-latency performance. - [Inference Optimization: Practical Techniques for Faster, Cost-Effective AI](/learn/inference-optimization) — AI Infrastructure & Inference / Inference mechanics Optimize AI inference from GPU memory to storage with practical techniques that boost throughput, reduce latency, improve utilization, and lower cost per token—without relying on a single silver bullet. - [Introduction to HPC: What Are HPC & HPC Clusters?](/learn/what-are-hpc-and-hpc-clusters) — HPC / HPC basics Discover how HPC clusters unite compute, networking, orchestration, and high-performance storage to solve complex problems faster—and why eliminating data bottlenecks is critical to maximizing infrastructure efficiency and ROI. - [Isilon vs. Netapp [Comparing 21 Technical Capabilities]](/learn/isilon-vs-netapp) — Storage Architecture / Competitive Compare Isilon and NetApp across 21 critical storage capabilities—from deployment and cloud extensibility to security and scaling—to see where legacy platforms meet modern data demands and where they fall short. - [Large Language Model (LLM): Everything You Need to Know](/learn/what-is-llm) — AI & ML Fundamentals / Language models Explore how LLMs work, from transformer architectures and inference to benchmarks, use cases, and emerging trends—and learn how a high-performance data platform eliminates I/O bottlenecks to accelerate AI at scale. - [Life Science Data Management [Everything You Need to Know]](/learn/life-science-data-management) — Cloud & Data Management / Industry Learn how a FAIR data strategy, strong governance, and high-performance storage help life sciences teams secure sensitive data, eliminate silos, accelerate AI and analytics, and scale research from genomics to drug discovery. - [Lustre File System Explained](/learn/lustre-file-system-explained) — Storage Architecture / Parallel file systems Explore how Lustre’s parallel architecture accelerates large-file HPC workloads—and why its metadata bottlenecks and operational complexity make modern, distributed platforms like WEKA better suited for AI at scale. - [MLOps & Machine Learning Pipeline Explained](/learn/mlops-and-machine-learning-pipeline-explained) — AI & ML Fundamentals / MLOps Learn how MLOps extends DevOps with data pipelines, model training, CI/CD, and continuous training—helping teams deploy reproducible ML services, detect drift and data skew, and sustain performance in production. - [Machine Learning vs. Neural Networks (Differences Explained)](/learn/machine-learning-vs-neural-networks) — AI & ML Fundamentals / Machine learning Understand how machine learning, neural networks, and deep learning differ—and why high-performance data infrastructure is essential to train complex models faster, scale AI workloads efficiently, and unlock greater value from data. - [Microsoft Azure: Everything You Need to Know](/learn/what-is-azure) — Cloud & Data Management / Cloud platforms Explore Azure’s core services, infrastructure, and hybrid cloud capabilities—and learn how WEKA eliminates data bottlenecks to accelerate AI and high-performance workloads without sacrificing cloud economics. - [Modern Analytics: Strategies for Data-Intensive Businesses](/learn/modern-analytics) — Cloud & Data Management / Data pipelines Turn massive structured and unstructured datasets into actionable insights with AI-powered analytics—and learn how high-performance cloud storage eliminates bottlenecks, scales demanding workloads, and accelerates business decisions. - [Multi Cloud vs Hybrid Cloud: Understanding the Differences](/learn/multi-cloud-vs-hybrid-cloud) — Cloud & Data Management / Cloud concepts Hybrid cloud combines private and public infrastructure, while multi cloud spans multiple providers. Learn how each model impacts scalability, security, resilience, cost, and high-performance data operations. - [NAS vs SAN vs DAS Advantages & Disadvantages](/learn/nas-vs-san-vs-das-storage-comparison) — Storage Architecture / Storage types Compare NAS, SAN, and DAS across performance, scalability, complexity, and cost to choose the right storage architecture—and avoid the data bottlenecks that constrain demanding enterprise and AI workloads. - [NVIDIA DGX BasePOD: How it Works and More](/learn/nvidia-dgx-basepod) — GPUs & AI Hardware / NVIDIA platform Discover how NVIDIA DGX BasePOD simplifies scalable enterprise AI infrastructure—and how the NVIDIA-certified WEKA Data Platform eliminates storage bottlenecks to deliver linear performance, faster insights, and greater ROI. - [NVIDIA DGX H100: How it Works and More](/learn/nvidia-dgx-h100) — GPUs & AI Hardware / NVIDIA platform Explore how NVIDIA DGX H100 accelerates demanding AI and HPC workloads—and how WEKA’s high-performance data platform eliminates storage bottlenecks to maximize GPU utilization, scalability, and infrastructure ROI. - [NVIDIA DGX SuperPOD: How it Works and More](/learn/nvidia-dgx-superpod) — GPUs & AI Hardware / NVIDIA platform Explore how NVIDIA DGX SuperPOD scales AI and HPC with GPU-accelerated compute, high-speed networking, and optimized storage—and how certified WEKApod infrastructure eliminates data bottlenecks to keep GPUs fully utilized. - [NVIDIA InfiniBand Networking Platform: How it Works and More](/learn/nvidia-infiniband) — GPUs & AI Hardware / NVIDIA platform Explore how NVIDIA InfiniBand delivers ultra-low-latency, high-bandwidth connectivity for AI and HPC—and how WEKA NeuralMesh keeps GPUs fed with data to maximize infrastructure performance, scale, and ROI. - [NVIDIA NIM: How it Works and More](/learn/nvidia-nim) — GPUs & AI Hardware / NVIDIA platform Explore how NVIDIA NIM packages optimized AI models into portable microservices for faster inference across cloud and on-premises environments—and how WEKA eliminates data bottlenecks to maximize GPU performance and AI infrastructure ROI. - [NVIDIA NeMo: How It Works and More](/learn/nvidia-nemo) — GPUs & AI Hardware / NVIDIA platform Explore how NVIDIA NeMo builds, customizes, and deploys enterprise generative AI—and how WEKA eliminates data bottlenecks to accelerate model training, inference, and RAG at scale. - [NVIDIA Spectrum-X Ethernet Platform: How it Works and More](/learn/nvidia-spectrum-x) — GPUs & AI Hardware / Networking Discover how NVIDIA Spectrum-X combines AI-optimized Ethernet, adaptive routing, and BlueField-3 SuperNICs to eliminate network bottlenecks, keep GPUs productive, and scale training and inference efficiently. - [Network File System (NFS): Advantages and Disadvantages](/learn/what-is-network-file-system) — Storage Architecture / File systems NFS makes remote files easy to share, but its chatty protocol, limited scalability, and metadata overhead can bottleneck modern AI workloads. Learn how high-performance storage delivers NFS simplicity while saturating 100 Gb/s networks. - [Object Storage (What Is It Used For & How Does It Work?)](/learn/object-storage) — Storage Architecture / Storage types Object storage uses flat architecture, rich metadata, and global identifiers to scale unstructured data across cloud environments. Learn how it compares with file and block storage—and where it accelerates analytics, AI, media, IoT, and backup workloads. - [Prefill and Decode: A Technical Guide to the Two Phases of Inference](/learn/prefill-and-decode) — AI Infrastructure & Inference / Inference mechanics Prefill demands compute; decode demands memory bandwidth. Learn how parallel file systems, disaggregated serving, and chunked prefill eliminate contention to reduce latency, increase throughput, and maximize GPU efficiency. - [Retrieval Augmented Generation: Everything You Need to Know About RAG in AI](/learn/retrieval-augmented-generation) — AI & ML Fundamentals / Language models Learn how RAG grounds generative AI in current, trusted data—and how WEKA’s high-performance, scalable infrastructure eliminates retrieval bottlenecks to deliver faster, more accurate responses. - [SLM vs LLM: The Key Differences](/learn/slm-vs-llm) — AI & ML Fundamentals / Language models Compare SLMs and LLMs across performance, cost, latency, privacy, and scale—and learn how high-performance data infrastructure keeps inference pipelines efficient, GPUs utilized, and cost per token under control. - [Scale-Out Storage | Modern Data Storage Explained](/learn/scale-out-storage) — Storage Architecture / Architecture Scale-out storage expands capacity and performance by adding nodes as demand grows—reducing infrastructure sprawl, improving hardware utilization, and giving data-intensive AI workloads a more flexible, cost-efficient foundation. - [Software-Defined Storage: How It Works & Its Benefits](/learn/software-defined-storage) — Storage Architecture / Storage types Software-defined storage decouples data services from hardware to unify diverse infrastructure, automate management, and scale on demand—helping enterprises accelerate workloads, improve flexibility, and control storage costs. - [Synthetic Data in AI: How It Works and More](/learn/synthetic-data) — AI & ML Fundamentals / Data & analytics for AI Learn how synthetic data expands scarce or sensitive datasets, improves AI model development, and unlocks rare edge cases—and how high-performance infrastructure eliminates I/O bottlenecks across generation, validation, and training. - [The AI Memory Wall: Why Inference Is Running Out of Memory (and What to Do About It)](/learn/ai-memory-wall) — AI Infrastructure & Inference / Inference mechanics As model weights and KV cache overwhelm scarce, costly HBM, inference performance and economics suffer. Learn how intelligent tiering and software-defined memory extend GPU capacity, reduce redundant prefill, and sustain real-time AI at scale. - [Time Series Database (How It Works & Use Cases)](/learn/time-series-database) — Cloud & Data Management / Data platforms Learn how time series databases organize timestamped data for fast ingestion, analysis, and retrieval—and why scalable, high-performance infrastructure is essential for demanding financial, IoT, healthcare, and AI workloads. - [Understanding Cloud Workloads: Everything You Need to Know](/learn/cloud-workloads) — Cloud & Data Management / Cloud operations Explore cloud workload types, use cases, and infrastructure requirements—from AI and genomics to real-time analytics—and learn how WEKA accelerates data access, GPU performance, and scalable cloud operations. - [VAST Data vs Netapp [Comparing Technical Capabilities]](/learn/vast-data-vs-netapp) — Storage Architecture / Competitive Compare VAST Data and NetApp across deployment models, mixed workloads, cloud mobility, security, and scaling—then see where each platform meets, or falls short of, modern high-performance data infrastructure demands. - [Vector Database: Everything You Need to Know](/learn/vector-database) — AI & ML Fundamentals / AI basics Learn how vector databases power fast, context-aware AI through high-dimensional similarity search—and how WEKA eliminates storage bottlenecks to accelerate RAG pipelines, scale seamlessly, and improve infrastructure efficiency. - [What Are Big Data & Predictive Analytics? How Do They Relate?](/learn/big-data-predictive-analytics) — Cloud & Data Management / Data pipelines Big data infrastructure turns massive, complex datasets into usable information; predictive analytics converts that information into forecasts. Learn how WEKA eliminates data bottlenecks to accelerate AI and analytics at scale. - [What Is Cloud Automation & What Are the Benefits?](/learn/cloud-automation) — Cloud & Data Management / Cloud concepts Cloud automation eliminates repetitive manual work to simplify operations at scale, strengthen governance and security, and accelerate deployment—helping enterprises maximize the performance and ROI of cloud-native infrastructure. - [What Is Cloud Portability and Interoperability in Cloud Computing? Does It Matter?](/learn/cloud-portability-and-interoperability) — Cloud & Data Management / Cloud concepts Cloud portability and interoperability help enterprises move data and applications across environments, connect multi-cloud systems, and avoid vendor lock-in—unlocking greater flexibility, efficiency, and control. - [What Is Context Memory? The New Infrastructure Layer Powering AI Inference](/learn/what-is-context-memory-primary) — AI Infrastructure & Inference / Inference mechanics Agentic AI has outgrown GPU memory. Context memory adds a high-speed, persistent infrastructure tier that keeps KV cache accessible at scale—accelerating inference, increasing GPU utilization, and lowering cost per token. - [What Is High Availability in Cloud Computing & How Do I Achieve It?](/learn/high-availability-in-cloud-computing) — Cloud & Data Management / Cloud operations High availability keeps cloud applications running through component failures. Learn how redundancy, failover, orchestration, load balancing, and high-performance data storage minimize downtime and sustain performance at scale. - [What Is Hybrid Cloud Storage? What Are the Benefits?](/learn/benefits-of-hybrid-cloud-storage) — Cloud & Data Management / Hybrid cloud Hybrid cloud storage unifies on-premises, private, and public cloud resources to place data and workloads where they deliver the best performance, security, scalability, and cost efficiency. - [What Is KV Cache? The Hidden Engine Behind Every AI Response](/learn/what-is-kv-cache) — AI Infrastructure & Inference / Inference mechanics KV cache powers fast, context-aware AI—but GPU memory limits can turn it into an inference bottleneck. Learn how tiered memory and cache optimization unlock scalable, real-time performance while maximizing GPU efficiency. - [What Is Machine Learning & Why Does It Matter?](/learn/machine-learning) — AI & ML Fundamentals / Machine learning Learn how machine learning turns data into predictions and decisions—and why high-performance, cloud-native data infrastructure is essential to accelerate AI workloads, maximize GPU efficiency, and scale from edge to exabytes. - [What Is the Post-Production Workflow & How Do I Optimize It?](/learn/post-production-workflow) — Cloud & Data Management / Industry Post-production turns raw footage into a finished release—but massive files and complex collaboration can stall progress. Learn how a high-performance cloud data platform accelerates editing, VFX, rendering, versioning, and archival workflows. - [What is Amazon S3?](/learn/what-is-amazon-s3) — Cloud & Data Management / Cloud platforms Amazon S3 delivers scalable, durable object storage for applications, backups, archives, and hybrid clouds. Explore its S3 API, security controls, storage classes, and role in modern data infrastructure. - [What is Cloud Agnostic & Why Does It Matter?](/learn/cloud-agnostic) — Cloud & Data Management / Cloud concepts Cloud-agnostic architecture gives enterprises the flexibility to move workloads across providers, avoid vendor lock-in, and optimize high-performance data infrastructure. Learn the benefits, tradeoffs, and strategies for building it. - [What is Cloud Data Migration? Everything You Need To Know](/learn/cloud-data-migration) — Cloud & Data Management / Cloud operations Cloud data migration unlocks scalable, resilient infrastructure—but success demands the right strategy. Learn how to move data and applications efficiently, preserve integrity, control costs, and accelerate high-performance workloads. - [What is Cloud Tiering And What Are the Benefits?](/learn/cloud-tiering) — Cloud & Data Management / Cloud operations Cloud tiering automatically places data on the right storage tier based on access needs, balancing high-performance availability with lower costs, stronger resilience, and efficient data lifecycle management. - [What is Data Reduction & What Are the Benefits?](/learn/data-reduction) — Storage Architecture / Data types Learn how data reduction techniques shrink storage footprints without sacrificing critical insights—lowering costs, accelerating AI and machine learning workloads, and improving performance across cloud and on-premises infrastructure. - [What is Multi-Cloud Storage? Everything You Need To Know](/learn/multi-cloud-storage) — Cloud & Data Management / Multi-cloud Multi-cloud storage gives enterprises the flexibility to optimize cost, resilience, security, and compliance across providers. Learn how to overcome integration complexity and accelerate AI and other data-intensive workloads with a unified data platform. - [What is Unstructured Data? Why Does It Matter?](/learn/unstructured-data) — Storage Architecture / Data types Unstructured data powers modern AI and analytics—but only when it is accessible, secure, and scalable. Learn how data lakes, governance, and high-performance storage turn complex information into actionable insights. - [What is a GPU? Optimizing AI Infrastructure and Performance](/learn/what-is-a-gpu) — GPUs & AI Hardware / GPU basics A GPU, meaning graphics processing unit, accelerates rendering images and videos on a device by design. Originally developed for rendering graphics in video games and computer-aided design (CAD) applications, GPUs have evolved to handle a wide range of parallel processing tasks. - [Why Use a GPUs for Machine Learning? A Complete Explanation](/learn/gpus-for-machine-learning) — GPUs & AI Hardware / GPU basics Discover why GPUs’ parallel processing power makes them essential for machine learning—and how a high-performance data platform keeps them fed, accelerates model training, and reduces infrastructure costs.