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Andrew Ng’s Tips for the Data-Centric AI Future
Snorkel AI
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Programmatic Labeling: Better AI Faster and Cheaper
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Deep Dive on Data-Centric AI with Snorkel AI CEO Alex Ratner
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AstraZeneca’s Data-Centric Approach to AI in Pharma
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Harnessing the Power of Data-Centric AI Development
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Knowledge Distillation Demystified: Techniques and Applications
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Model Distillation: From Large Models to Efficient Enterprise Solutions
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Boosting Perception Model Training with Synthetic Data
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Weak Supervision Modeling Deep-Dive with Fred Sala
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LIGER: A Fusion of Foundation Models and Weak Supervision
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Why Clean Data is Capital One’s Top AI Asset
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What is AI-Ready Data? Key Takeaways from the 2025 Gartner Data & Analytics Summit
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Inside the Mind of Machine Learning Expert Fred Sala
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Quantum Black AI's Game-Changing Data Quality Strategy
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How Data-Centric Design Supports Resilient AI
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Data-Centric Development is Key to Production-Level AI
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Stanford University's Mayee Chen on LIGER: Fusing Model Embeddings & Weak Supervision
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How to Leverage Expertise In AI Development
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Tackling Advanced Classification Using Snorkel Flow
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Weak Supervision and Next-Gen AI-Based Threat Detection
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How PonderNet Knows What to Compute (And When to Give Up)
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Can SQL Boost Data Organization for ML?
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Case Study: How Snorkel AI Scales Language Model Tuning
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How to Use AI for Better Online Education
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Why GenAI Needs Careful Training Data Management
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How Ford Uses AI To Develop Better AI
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How Quickly Can We Train Effective ML Models?
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The Challenges and Opportunities of Continual Learning
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How to Improve Data and Modeling for Computer Vision Apps
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MLOps: Towards DevOps for Data-Centric AI
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What Is Data-Centric AI In Practice?
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The Art of Data Development for LLMs
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How Comcast Powers Its AI Voice Tech with Data
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How To Select Data for Data-Centric AI
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How Snowflake Builds Quality ML Pipelines
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How Comcast Powers AI Speech Recognition with Weak Supervision
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Learning with Imperfect Labels and Visual Data
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How to Build Powerful Extraction Models with Snorkel Flow
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Justin Gottschlich "Machine Programming and the Future of Data-Driven Software Development"
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Netflix and BNY Mellon Discuss the Future of Data-Centric AI
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How Google is Scaling NLP to the Next 1,000 Languages
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Why Detecting Distributional Shift is So Hard (And So Important)
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Why AI Needs New Data Benchmarks and Quality Metrics
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Imen Grida Ben Yahia "Data-Centric Facets for Network Ops"
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Responsible Data-Centric AI for Healthcare and Medicine
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An Inside Look at NASA's SpaceML Worldview Search
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The Essential Design Principles Driving AI Engineering
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How Hugging Face Helps Non-Technical Users Build ML
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How Insurance Competes in the AI Age
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Expert Panel: Mastering Speech and Search AI with NLP
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Adopting Data-Centric AI Across The Enterprise
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How To Operationalize Knowledge for Data-Centric AI
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Best Practices and Strategies for Explainable AI
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Reclaim Predictive Data With Knowledge Graphs
How Humans and AI Collaborate at JPM Chase
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Practical Paths to Data-Centric AI from Google
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Why Observing Your ML Makes It Better
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Talking Weak Supervision with Leading AI Researchers
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What Capital One Monitors to Better Manage AI Training Data
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How Georgetown Helps Data Scientists and SMEs Collaborate on ML
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Build Better LLMs Through Data Slicing
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How MLCommons Is Democratizing Machine Learning
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Extracting Insights From Climate Change Research with NLP
The Art of Building AI Systems: Rapid Training Data Creation
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Scaling Past Rule-Based Systems at the Petabyte Level
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Maximize ML Model Performance with Two Lines of Code
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Can Data-Centric AI Help Us Develop Better Drugs?
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How Grammarly Masters Communication Challenges with AI
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How AI Spreads Knowledge Throughout The Smithsonian Institution
