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What is an AI Stack? | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Data visualization for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Tutorial: Multilingual LLM agent Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Overview Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection Cole Stryker artificial intelligence AI lifecycle enterprise AI gen AI LLMs GPT IBM’s generative AI tech stack IBM Privacy Statement infrastructure AI models AI accelerators GPUs large language models storage systems deep learning HDFS cloud storage latency Go to episode data lakes data warehouses Data scientists data ingestion data management unstructured Apache Kafka Apache Spark supervised learning labeled open source GDPR CCPA fine-tuned deep learning PyTorch natural language processing machine learning algorithm linear regression decision trees neural networks transformers gradient descent Feature engineering fine-tuning APIs automating APIs visualizations automation chatbot observability AI workflows governance GDPR EU AI Act bias explainability infrastructure machine learning Guide 5 Steps to Build Resilient AI-Ready Infrastructure Modern AI infrastructure demands more than performance—it requires built-in resilience to withstand cyber threats, system failures, and operational disruptions. This guide outlines five essential steps for infrastructure leaders to establish strong data ownership, classify critical workloads, implement resilient protection, detect anomalies using AI, and automate recovery workflows to ensure continuous, reliable operations in an increasingly complex environment. Explore the 5 Steps Product Do more at the core with IBM Z Unlock more business value by bringing AI to mission-critical workloads on the IBM z17™. Explore the new IBM Z17 Report Discover how to integrate AI with your mainframe IBM Z® is a family of modern infrastructure powered by the IBM Telum® processor that runs enterprise operating systems and IBM Z software delivering greater AI accuracy, productivity and agility. Read the playbook Report IBM z17 makes more possible Process up to 24 trillion operations per second. Read the data sheet Report A full stack AI solution The IBM z17™ integrates advanced AI into hybrid cloud, optimizing performance, security and decision-making where critical data resides. Read the Celent analyst paper Report Unlock the value of IBM Storage FlashSystem resilience Learn how you can safeguard your organization's data against cyberthreats and ensure swift recovery during critical events. Read the report Report The 2024 Gartner Report Every IT Infrastructure Leader Should Read See how top data integration platforms are enabling resilient, scalable, and hybrid-ready infrastructures for tomorrow’s enterprise. Read the report Explore watsonx.ai Explore AI infrastructure Explore AI services Explore IBM Storage Fusion Explore AI Infrastructure solutions

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