Deep Learning
Explore deep learning fundamentals, computer vision, TensorFlow, Keras, transfer learning, and advanced topics like TPUs in this comprehensive introduction to neural networks.
Accelerators Made Easy - Improving MLOps at Loblaw Digital Using Vertex AI
Leverage Vertex AI for efficient matrix multiplication in MLOps. Build scalable recommendation engines using Google Cloud Platform's tools for improved product relevance.
Testing the Wham Bam Carbon Fiber Plate - The Ultimate 3D Printer Bed?
Comprehensive review of Wham Bam carbon fiber 3D printer bed, examining its performance with various filaments, durability, and comparison to alternatives. Insights for 3D printing enthusiasts seeking optimal print surfaces.
On-Demand Systems and Scaled Training Using the JobSet API
Explore JobSet API for efficient orchestration of complex workflows, enabling scaled ML training and on-demand HPC systems. Learn deployment techniques and see results from large-scale experiments.
Scaling LLMs on Google Cloud - Synergy Between Ray, TPU, and GKE
Discover how to optimize LLM deployments using Ray, TPUs, and GKE on Google Cloud, focusing on performance optimization, memory management, and large-scale orchestration techniques.
Cross-Architecture Dynamic Migration Solution for Heterogeneous AI Acceleration Systems
Discover a unified solution for seamless application migration across diverse AI accelerators, enabling cross-architecture compilation and dynamic runtime adaptation without code modifications.
Fine-tuning Flan-T5 LLM with HuggingFace Accelerate - Tutorial
Learn how to fine-tune Flan-T5 LLM using HuggingFace Accelerate for multi-GPU environments, with step-by-step guidance on preprocessing and implementation for specific downstream tasks.
Fine-tuning Vision Language Models with PaLiGemma-3B - A Practical Guide
Master fine-tuning techniques for Vision Language Models using PyTorch and JAX FLAX, with practical examples for PaliGemma-3B implementation on free compute platforms like Google Colab and Vertex AI.
Google Gemini - Technical Report on Model Architecture, Dataset, and Training
Explore Google's Gemini AI model family: architecture, datasets, and performance across text, image, video, and speech benchmarks. Learn how it advances multimodal AI and sets new records.
Machine Learning with JAX - From Hero to HeroPro+
Advanced JAX tutorial covering PyTrees, parallelism, multi-device training, and advanced techniques for efficient machine learning model implementation and training.
All About AI Accelerators - GPU, TPU, Dataflow, Near-Memory, Optical, Neuromorphic & More
Comprehensive exploration of AI accelerator technologies, from GPUs to emerging innovations like neuromorphic computing, discussing their principles, advantages, and future potential in advancing artificial intelligence.
PyTorch NLP Model Training and Fine-Tuning on Colab TPU Multi-GPU with Accelerate
Learn to train and fine-tune PyTorch NLP models on Colab TPU using Hugging Face's Accelerate library for efficient multi-GPU/TPU implementation with minimal code changes.
Making the Most Out of Hardware Accelerators in Kubernetes Clusters
Optimize GPU and TPU usage in Kubernetes clusters for MLOps. Learn efficient hardware accelerator management, scaling, and time-sharing techniques to enhance machine learning workloads.
Navigating the Processing Unit Landscape in Kubernetes for AI Use Cases
Explore specialized processing units for AI/ML workloads in Kubernetes. Learn about GPUs and TPUs, their strengths, and effective implementation strategies for parallel processing in AI applications.
Google I/O 2024 - Key AI Advancements and Their Implications
Explore Google I/O 2024's AI innovations: Gemini upgrades, Veo video generation, Trillium TPUs, enhanced search, and Gemini Nano for Android, revolutionizing user interactions across devices and services.
Solving a Complex Game with AI and All the Google Cloud Power
Explore AI-powered game solving using Google Cloud, focusing on reinforcement learning to tackle the complex 8-bit RPG "The Abbey of Crime" and overcome its challenging solution space.
Intro - CNCF SIG-Runtime
Explore cloud-native runtime technologies, projects, and specialized architectures with CNCF SIG-Runtime. Learn about orchestration, scheduling, and emerging trends in Edge, IoT, AI/ML, and more.
Optimizing Storage Solutions for AI and ML Workloads in Google Cloud
Discover advanced storage solutions optimized for AI/ML workloads, featuring Anywhere Cache, HyperDisk ML, and Cloud Storage FUSE innovations that enhance data accessibility and training efficiency.
AI-Optimized Infrastructure and Workload Management in Google Cloud
Discover how Google Cloud optimizes AI infrastructure through advanced storage solutions, accelerators, and managed services like Parallel Store and Hyperdisk ML for efficient large-scale AI operations.
Machine Learning Software Ecosystems: Frameworks, Compilers, and Models - Part 1
Explore the landscape of ML frameworks, compilers, and models while understanding performance considerations for various hardware platforms and edge computing requirements.
Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement
Optimize ML data preprocessing costs with Pecan's dynamic worker scheduling and transformation reordering, reducing preprocessing expenses by 87% and total training costs by up to 60%.