Vertex AI - Pipelines for Your MLOps Workflows
Explore Vertex AI Pipelines for streamlined MLOps workflows, covering experiment pipelining, infrastructure management, and practical examples for efficient machine learning development and deployment.
Machine Learning Using Kubeflow and Kubernetes
Explore machine learning workflows on Kubernetes using Kubeflow, covering deployment, training, inference, and advanced features like hyperparameter tuning and automated pipelines.
Taking Models to the Next Level with Azure Machine Learning
Explore Azure Machine Learning for custom model training, from pre-built cognitive services to AutoML and hyperparameter tuning, with practical examples for text analysis and sentiment analysis.
Hyperparameter Tuning on the Cloud - Optimizing Machine Learning Models with SEER and Rubberband
Master hyperparameter tuning optimization in cloud environments through SEER algorithm and Rubberband system, focusing on maximizing model accuracy while minimizing costs and time constraints.
Large Language Models to Enhance Bayesian Optimization
Discover how Large Language Models enhance Bayesian optimization through LLAMBO, improving hyperparameter tuning and black-box function optimization with natural language integration.
Stochastic Tabu Search and Improvements - Application for Physician Scheduling
Explore stochastic tabu search for physician scheduling, including hyperparameter tuning and learning algorithms to enhance efficiency in radiotherapy centers.
Scaling Deep Learning Model Training
Learn techniques for scaling deep learning model training across distributed systems, reducing training time from days to hours while maintaining cost efficiency.
Hyperparameter Tuning with a Focus on Weights and Biases Sweeps
Explore hyperparameter tuning strategies and visualizations for deep learning models, focusing on Weights & Biases Sweeps. Accelerate progress in semantic segmentation and language understanding projects.
Building and Deploying Machine Learning Models with Amazon SageMaker
Learn to build, train, and deploy ML models using Amazon SageMaker, covering data preprocessing, model selection, hyperparameter tuning, and evaluation.
Fine-tuning LLMs - Every Step Explained for Memorization Tasks
Explore LLM fine-tuning techniques, from dataset generation to hyperparameter optimization. Learn to overcome challenges like the reversal curse and optimize model performance for specific tasks.
Full Fine-Tuning vs LoRA and QLoRA - Comparison and Best Practices
Explore full fine-tuning vs (Q)LoRA techniques, comparing VRAM usage, training time, and quality. Learn parameter selection, tips for QLoRA, and step-by-step guidance for TinyLlama and Mistral 7B fine-tuning.
SkyML: Infra-less Machine Learning on Any Cloud
Explore SkyML for simplified, high-performance machine learning across clouds. Leverage best-of-breed accelerators, spot instances, and automatic resource management for cost-effective ML applications.
Machine Learning for Predicting Additive Manufacturing Part Quality - Tutorial on Support Vector Regression
Learn to predict additive manufacturing part quality using support vector regression. Explore data analysis, hyperparameter tuning, and cross-validation techniques for machine learning in 3D printing applications.
LoRA Fine-tuning Explained - Choosing Parameters and Optimizations
Explore LoRA fine-tuning techniques, from parameter selection to optimization strategies, enhancing your ability to customize language models effectively.
Knowing the What But Not the Where in Bayesian Optimization
Explore Bayesian optimization techniques for finding optimal inputs when outputs are known. Enhance efficiency in hyperparameter tuning for deep reinforcement and supervised learning.
Neural Architecture Search - Part I
Explore neural architecture search techniques, focusing on automated design of efficient deep learning models for various applications.
Neural Architecture Search - Part I (EfficientML.ai) - Lecture 7
Explore neural architecture search techniques for optimizing deep learning models, covering fundamentals, methods, and applications in AI efficiency.
Neural Architecture Search Part I - Lecture 7
Explore neural architecture search techniques for optimizing deep learning models, covering fundamental concepts and cutting-edge approaches in AI efficiency.
Neural Architecture Search Part I - Lecture 7
Explore neural architecture search techniques for optimizing deep learning models, focusing on automated design and efficiency in AI systems.
Neural Architecture Search Part II - Lecture 8
Explore advanced neural architecture search techniques, including evolutionary algorithms and reinforcement learning, to optimize deep learning models for efficiency and performance.
How to Create, Optimize, and Implement Quantum ML Algorithms
Explore quantum ML algorithm creation, optimization, and implementation. Master Qiskit circuits, pulse schedules, and transpiler passes. Learn hyperparameter tuning and platform integration techniques.