How Ford Uses AI to Develop Better AI
Explore Ford's AI development lifecycle, focusing on data extraction, annotation, and augmentation for training. Learn about iteration's role in enhancing AI systems at Ford Motor Company.
An Inside Look at NASA's SpaceML Worldview Search
Explore NASA's SpaceML Worldview Search with insights from Machine Learning Lead Anirudh Koul, covering innovative applications in space exploration and data analysis.
Data-Centric AI in Comcast's Voice and Conversational Interfaces
Explore Comcast's data-centric approach to AI voice and conversational interfaces, powering innovative technology solutions.
How Cleanlab Uses AI to Correct Errors in Any Dataset
Discover AI-powered tools for cleansing datasets, enhancing model training accuracy. Explore Cleanlab's innovative approach to data-centric AI and its practical applications.
Can Data-Centric AI Help Us Develop Better Drugs?
Explore how machine learning and high-quality data can revolutionize drug development, predicting outcomes and designing safer, more effective therapies faster and at lower costs.
Why AI Needs New Data Benchmarks and Quality Metrics
Explore DataPerf, a benchmark suite for evaluating ML data quality and optimization algorithms. Learn about the need for data-centric AI benchmarks to advance research and improve real-world applications.
What Landing AI Learned Through Democratizing AI
Explore Landing AI's journey in democratizing AI, focusing on manufacturing challenges, data-centric techniques, and the evolution of Landing Lens for faster iteration and improved performance.
Why Detecting Distributional Shift is So Hard and So Important
Explore challenges and opportunities in detecting data distributional shift, a crucial aspect of AI robustness and reliability in real-world applications.
Why Fixing Your Data Can Fix Your Model in Machine Learning
Explore how addressing data issues can improve machine learning models, focusing on cost-effective solutions and identifying crucial data problems to optimize resources.
Democratizing Machine Learning: MLCommons' Role in Public Datasets and Benchmarks
Explore MLCommons' mission to transform machine learning through benchmarks, best practices, and data. Learn about public datasets' importance and the organization's tools for dataset creation.
Improving Data and Modeling for Computer Vision Applications
Explore advancements in computer vision for data-centric image classification. Learn about visual prompts, parameter-efficient models, and adapting representations for improved performance and efficiency.
How Insurance Competes in the AI Age
Explore AI's impact on insurance: operational effectiveness, new business models, and partnerships. Learn about data-centric approaches for risk capacity matching and policy ingestion.
How Wayfair Uses AI to Build Better E-Commerce Experiences
Explore Wayfair's ML strategies for enhancing e-commerce, from customer understanding to catalog enrichment. Learn about data-centric approaches and SME-DS collaboration for improved product discovery.
Automatically Fix Data Issues and Label Errors in Most ML Datasets - Cleanlab
Discover how to automatically detect and fix data issues in machine learning datasets using Cleanlab's open-source framework and no-code web interface for improved model accuracy.
Bringing Structure to Unstructured Data with an AI-First System Design
Explore AI-powered data systems for unstructured content, addressing challenges and sharing key lessons for end-to-end applications in enterprise environments.
Data-Centric Development is Key to Production-Level AI
Explore data-centric AI development approaches for adapting foundation models to complex use cases, focusing on techniques for pre-training, instruction-tuning, and task-specific fine-tuning.
Alfred: Building Training Data with Foundation Models and Weak Supervision - Snorkel AI
Streamline data labeling with Alfred, combining foundation models and weak supervision to create high-quality training datasets for academic research and machine learning projects.
Task Me Anything: Revolutionizing Multimodal Model Benchmarking
Explore Task Me Anything platform for efficient multimodal model evaluation. Learn programmatic task generation and active learning techniques to create custom benchmarks for data science projects.
Active Learning Loop on OpenShift
Implement an active learning pipeline on OpenShift using open-source tools for dynamic data labeling and improved model accuracy in machine learning operations.
Accelerating Enterprise Adoption of AI for Business Impact
Accelerate enterprise AI adoption for business impact. Learn market trends, challenges, and strategies to bridge AI with decision-makers using a universal semantic layer.
Foundational Models in Enterprise AI - Challenges and Opportunities
Explore the future of AI with foundation models, their enterprise applications, and challenges. Learn about Snorkel AI's approach to optimizing these models for cost, quality, and control.