Accelerate Model Training with a High-Performance Distributed AI/ML Stack for the Cloud
Learn to accelerate AI/ML model training using Ray and Open Data Hub for efficient resource management and distributed computing in cloud environments, enhancing scalability and performance.
Training Large Language Models on Kubernetes
Explore challenges and best practices for training Large Language Models on Kubernetes, including optimization techniques, resource management, and benchmarks comparing K8s to bare metal environments.
Building Dynamic Machine Learning Pipelines with KubeDirector
Learn to build dynamic ML pipelines using KubeDirector on Kubernetes. Explore a taxi ride prediction example, from model training to deployment, and discover how KubeDirector's "Connections" feature enables always up-to-date ML models.
End-to-End Deep Learning Project: Kidney Disease Classification with MLflow, DVC, and Deployment
Comprehensive deep learning project on kidney disease classification, covering data ingestion, model preparation, training, evaluation, MLflow integration, DVC pipeline, and AWS deployment.
Build Your First PyTorch Model - Image Classification Tutorial
Learn to build an image-classifying PyTorch model for playing cards in this hands-on tutorial. Covers datasets, model creation, training, and result analysis.
How to Eliminate I/O Bottleneck and Continuously Feed GPU While Training in the Cloud
Optimize I/O for GPU-intensive machine learning training in the cloud. Learn a new architecture to maintain data throughput and implement it in Kubernetes for PyTorch workloads.
Building Your Own AI Platform and Tools Using ChatGPT
Explore creating custom AI platforms using TensorFlow, covering data collection, cleaning, training, and building a CLI-based system tailored to your organization's environment.
MegaScale - Scaling Large Language Model Training to More Than 10,000 GPUs
Innovative system for training large language models on 10,000+ GPUs, addressing efficiency and stability challenges through full-stack optimization and advanced diagnostic tools.
Coding a ChatGPT-Like Transformer From Scratch in PyTorch
Step-by-step tutorial on building a ChatGPT-like Transformer from scratch in PyTorch, covering data preparation, position encoding, attention mechanisms, and model training for natural language processing.
Training and Evaluating LLaMA2 Models with Argo Workflows and Hera
Comprehensive guide to train and evaluate LLaMA2 models using Argo Workflows and Hera SDK. Covers setup, data preprocessing, model training, and performance assessment with cloud-native tools.
Teeth Detection Using YOLOv8 and Python
Learn to detect and label teeth using YOLOv8 and Python. Master dataset preparation, model training, and making predictions with bounding boxes in this hands-on tutorial.
YOLOv9 Tutorial - Train Model on Custom Dataset
Learn to set up, train, and deploy YOLOv9 for custom object detection, including model evaluation, inference, and practical applications like self-service checkout systems.
Bloomberg's Journey to a Multi-Cluster Workflow Orchestration Platform
Explore Bloomberg's evolution in cloud-native workflow orchestration, addressing challenges of rapid growth and discussing solutions for efficient multi-cluster resource deployment and management.
Flood Detection with YOLO V8 Segmentation - One-Class Image Segmentation
Learn to detect floods using Yolo V8 image segmentation. Covers dataset preparation, model training, and testing on new images to generate segmentation masks highlighting flooded areas.
Dog Segmentation with YOLOv8 for Images and Videos
Learn to segment dogs in images and videos using YOLOv8 and Python. Train AI, isolate dogs from backgrounds, and merge with custom scenes. Step-by-step guide with code included.
Reproducing GPT-2 (124M) from Scratch - Implementation and Optimization
Comprehensive tutorial on reproducing GPT-2 (124M) from scratch, covering implementation, optimization, training setup, and results analysis. Includes practical coding and performance tuning tips.
AI Development Assistance for Android Studio - Using Gemini AI Models
Explore AI-assisted developer tools for Android, featuring insights on Gemini models, code transformation, and productivity enhancements in Android Studio from Google's AIDA team.
Object Detection Using Detection Transformer (DETR) for Bone Fracture Dataset
Learn to build an advanced object detection system using DETR, covering data preparation, model implementation, training, and inference with PyTorch, Transformers, and PyTorch Lightning.
Direct Preference Optimization - Fine-Tuning LLMs Without Reinforcement Learning
Explore Direct Preference Optimization (DPO), an efficient method for training Large Language Models without reinforcement learning. Learn about the Bradley-Terry model, KL divergence, and loss function.
Hugging Face Accelerate: Making Device-Agnostic ML Training and Inference Easy at Scale
Learn to simplify ML model training and inference across devices using Hugging Face Accelerate. Explore its low-level interface, hardware compatibility, and scalable deployment features.
THC - Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
Accelerates distributed deep learning with Tensor Homomorphic Compression, enabling direct aggregation of compressed values and compatibility with in-network aggregation for faster training of vision and language models.