Automated Machine Learning Tuning with FLAML
Explore automated machine learning and tuning using FLAML, a fast Python library. Learn to automate ML tasks, perform general tuning, and conduct zero-shot AutoML efficiently.
Improving Productivity for Building ML Workloads Using Amazon SageMaker Autopilot
Automate ML model training and tuning for customer churn prediction using Amazon SageMaker Autopilot, with deployment and iteration in SageMaker Studio.
Machine Learning Simplified - From Ideation to Deployment in Minutes with Automated ML
Discover how to train high-quality ML models using Azure Machine Learning's automated ML, requiring only a dataset and minimal configuration parameters.
Automated Machine Learning on Azure - Comparing Azure ML Service and Azure Databricks
Explore AutoML on Azure Machine Learning Service and Azure Databricks to quickly build and deploy ML models, reducing time and expertise required for preprocessing and model creation.
Challenges in Hyperparameter Optimization for Reinforcement Learning - Understanding AutoRL Through AutoML
Explore the complexities of hyperparameter optimization in Reinforcement Learning, examining AutoML tools and methods to better understand and tune RL algorithms for improved performance.
AutoXGB - Automated ML with XGBoost + Optuna + FastAPI - Kaggle Demo
Explore AutoXGB, a Python library for automated machine learning using XGBoost, Optuna, and FastAPI. Learn its parameters and application in a Kaggle competition.
Test Drive - A Very First Look at H2O Hybrid Cloud
Explore H2O AI Hybrid Cloud's features: AI App Store, MLOps pipeline, AI engines, and collaboration tools. Gain insights into automated machine learning for complex business problem-solving.
Automated and Explainable Deep Learning for Clinical Language Understanding
Explore advanced deep learning techniques for clinical NLP, including automated model training, explainable AI, and scalable architectures to extract critical healthcare insights from unstructured medical notes.
Hyperparameter Tuning Using Kubeflow
Explore automated machine learning with Katib, a Kubernetes-native platform for hyperparameter tuning and neural architecture search, enhancing model performance efficiently.
Automated Quantitative Trait Locus Analysis - CGSI 2023
Explore automated quantitative trait locus analysis using machine learning. Learn about genetic architecture, feature engineering, and Pareto optimization for biodata mining and complex trait analysis.
Automated Machine Learning and Explainability
Explore Oracle AutoML's innovative approach to simplifying machine learning, integrating explainability techniques for enhanced user understanding and trust in complex ML pipelines and models.
AI Customer Use Cases and Learnings
Discover best practices and real-world insights from AI Platform Customer Engineering, exploring machine learning applications in wildlife protection and healthcare automation.
Overview of Automated ML June 2019
Explore automated machine learning for accelerating AI development, with demos on Azure ML Service and Databricks, focusing on Python implementation and recent Azure technologies.
Auto-Sklearn - Automated Machine Learning in Python
Discover Auto-sklearn, an open-source AutoML tool that simplifies machine learning pipeline development. Learn its functionality, benefits, and how to implement it as a scikit-learn replacement.
Automated Machine Learning With Keras
Explore automated machine learning with Keras Tuner and AutoKeras, enabling efficient development of high-performance deep learning models while requiring less expertise and effort.
Azure Machine Learning 101
Explore Azure ML's no-code approach to machine learning, from data preparation to model deployment, suitable for both beginners and experienced data scientists.
Data for Good - Machine Learning to Improve Water Access in Developing Nations
Leveraging machine learning to analyze global water point data, providing insights for governments to improve water access in developing nations through an accessible web-based tool.
Hidden Differences Between Azure ML Products Revealed
Explore Azure ML products, their differences, and use cases. Learn to create end-to-end ML workflows, integrate with SQL Server, and manage models in production using various Azure tools.
MLOps - Automated Machine Learning Made Easy
Explore AI and ML optimization techniques to enhance business outcomes through data understanding and model improvement.
Deep Learning New Frontiers
Explore deep learning limitations and new frontiers, including expressivity, generalization, failure modes, uncertainty, adversarial attacks, and emerging areas like graph learning 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.