Boosting Data Science Routines with DataArt's AutoML Platform
Discover how DataArt's DS/ML Platform automates data science workflows through feature engineering, model selection, and deployment - suitable for both expert and citizen data scientists.
Declarative Machine Learning Systems - Ludwig and Predibase
Explore declarative machine learning systems, focusing on Ludwig framework and Predibase enterprise solution for flexible, simplified ML infrastructure.
How to Get Started with Computer Vision AutoML in Azure
Leverage Azure Computer Vision to build and deploy advanced image processing models without extensive coding, jumpstarting your journey into AI-powered visual analysis.
Introduction to Vertex AI - Build Series Episode 9
Explore Vertex AI on Google Cloud: Learn about machine learning options, AutoML, custom models, and see demos on dataset creation, model building, and classification.
Getting Started with Google Cloud ML APIs
Explore Google Cloud ML APIs for startups. Learn to select, use pre-trained models, and customize AI solutions. Discover natural language, vision, speech, and video intelligence capabilities.
Solving LLM Data Problems with Vector Databases - Part 2
Explore vector databases' role in LLM apps, focusing on solving data issues and democratizing access to this technology for efficient billion-scale vector search.
Deep Learning New Frontiers
Explore deep learning limitations and new frontiers, including expressivity, generalization, adversarial attacks, uncertainty, and AutoML. Gain insights into cutting-edge developments in neural networks.
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.
AutoMoE - Neural Architecture Search for Efficient Sparsely Activated Transformers
Discover how Neural Architecture Search optimizes Transformer models through sparse activation, achieving 4x FLOP reduction while maintaining performance in machine translation tasks.
AutoGluon 1.0 - Shattering SOTA with Zeroshot-HPO and Dynamic Stacking
Discover how AutoGluon 1.0 revolutionizes machine learning with Zeroshot-HPO and Dynamic Stacking, achieving superior performance in automated model training and ensemble stability.
Multi-objective Differentiable Neural Architecture Search
Explore advanced techniques in neural architecture optimization across multiple objectives and hardware devices, focusing on Pareto front profiling and hypernetwork-based solutions.
Vanilla Bayesian Optimization Performs Great in High Dimensions
Discover how scaling Gaussian process lengthscale prior with dimensionality enables vanilla Bayesian optimization to excel in high-dimensional optimization problems, outperforming complex alternatives.
Mixture-of-Supernets - Improving Weight-Sharing Supernet Training with Architecture-Routed MoE
Explore cutting-edge research on Mixture-of-Supernets architecture that enhances neural architecture search efficiency and improves BERT and MT model performance through innovative weight-sharing techniques.
Searching for Neural Architectures from Fundamental Operations - An Introduction to einspace
Explore neural architecture search through einspace, a novel approach using fundamental operations to discover diverse and competitive network architectures for machine learning tasks.
The AI Scientist - Towards Fully Automated Open-Ended Scientific Discovery
Explore groundbreaking research on autonomous AI systems capable of conducting end-to-end scientific discovery, from generating ideas to writing papers and performing peer reviews in machine learning.
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.
Using MLflow and Databricks to Deploy ML Models in Production
Explore MLflow and Databricks for production ML model deployment. Learn end-to-end management, model tracking, and deployment strategies, plus Databricks' Feature Store and AutoML.
AutoML Towards Deep Learning: Optimizing Neural Architectures and Hyperparameters
Explore advances in AutoML for Deep Learning, focusing on joint optimization, efficiency, and robustness. Learn about the future of automated neural architecture and hyperparameter optimization.
Building Smarter Solutions With No Expertise in Machine Learning
Discover how to create intelligent solutions without ML expertise. Learn practical approaches to leverage AI technologies for smarter applications.
AutoML in Vertex AI for MLOps
Explore AutoML in Vertex AI for MLOps, streamlining model training, comparison, and deployment on Google Cloud's unified platform.