Operationalizing Data-Centric AI - Practical Tools to Quickly Improve ML Datasets
Discover practical tools and algorithmic strategies to automatically identify and address dataset issues, improving ML model performance across various data types.
Iterative Development Workflows for Building AI Applications
Explore iterative development workflows for AI applications using Snorkel framework. Learn guided error analysis, quality improvement techniques, and collaboration with domain experts.
Deploying Efficient Data-Centric AI 100x Faster
Accelerate AI application development and deployment 100x using dedicated platforms, data labeling, pre-built models, and iterative design. Explore region classification and deep-trained object detection approaches.
Deploying Efficient Data-Centric AI 100x Faster
Accelerate AI application development and deployment 100x using dedicated platforms, data labeling, pre-built models, and AI-automation. Explore region classification and deep-trained object detection approaches.
It's All About the Data - Continuously Improve ML Models the Data-Centric Way
Explore data-centric AI practices to enhance ML model performance. Debug input data, analyze datasets, and identify post-deployment issues using open-source whylogs for continuous improvement.
Data-Centric AI - Technological Key of the Future
Explore the pivotal role of data in shaping AI's future, from Model-Centric vs Data-Centric approaches to insights from Vietnam's first Data-Centric AI Competition. Uncover how data defines the AI landscape beyond code.
Leveraging Data to Unlock AI's True Potential in Enterprise Projects
Unlock AI's potential through data development. Learn to fine-tune LLMs for domain-specific tasks, leverage expert knowledge, and accelerate high-quality dataset creation using Snorkel Flow.
How Data Can Close the Foundation Model Performance Gap
Explore data-centric development's role in bridging the gap between AI's power and its practical value for companies, focusing on Foundation Models.
Data-Centric AI Approach in Pharmaceutical Industry - AstraZeneca Case Study
Explore data-centric AI in pharma: personalized doctor education, patient treatment optimization, and real-world data utilization for predictive modeling at AstraZeneca.
Building Machine Learning Systems for the Era of Data-Centric AI
Explore the evolution of ML systems beyond model training, focusing on data quality, trustworthiness, and regulatory compliance through systematic data debugging and cleaning approaches.
Data as the Programming Interface - The Future of AI Development
Explore how data-centric approaches revolutionize AI development, focusing on specialized evaluation, fine-grained analysis, and scalable solutions for capturing human feedback in enterprise systems.
Rethinking ML Development - A Data-Centric Approach
Explore a data-centric approach to ML development, focusing on iteration, quality tools, and problem-solving strategies for more effective AI model creation and implementation.
Operationalizing Organizational Knowledge with Data-Centric AI
Explore data-centric AI with Alex Ratner: operationalizing knowledge, managing training data, and leveraging foundational models for real-world applications in machine learning and responsible AI.
Getting High-Quality Data for Your Computer Vision Models - Building Computer Vision Models
Explore the importance of high-quality data in computer vision, focusing on data collection, annotation, and bias mitigation. Learn strategies for building representative datasets for AI models.
Explainable Data-Centric AI
Explore the next generation of AI systems through Data-Centric AI, focusing on data selection, engineering, and curation for robust, fair, and responsible AI with sustainable performance.
Practical Paths to Data-Centric AI
Explore data-centric AI with Google experts, covering data quality, feature selection, and large language model deployment. Learn about synthetic data, weak supervision, and data governance.
The Challenges and Opportunities of Continual Learning in Real-Time Machine Learning
Explore challenges and opportunities in continual learning, covering stages, training types, key issues, and solutions for feature monitoring and evaluation in machine learning ecosystems.
How Humans and AI Collaborate at JPMorgan Chase
Explore human-in-the-loop machine learning applications at JPMorgan Chase, focusing on practical datasets and methodologies in computer vision and natural language processing.
GenAI and Datacomp: Creating the Largest Public Multimodal Dataset in Academia
Explore academia's role in GenAI, focusing on large-scale dataset management. Learn about AI-driven data cleaning, data-centric AI, legal challenges, and synthetic dataset potential in this insightful talk.
Monitoring Unstructured Data - Lightning Session
Explore ML observability for unstructured data with Arize. Learn about embeddings, monitoring challenges, and solutions through expert insights and a product demo.
How Top Healthcare Companies Boost AI Development with Data-Centric Approaches
Discover how leading healthcare companies accelerate NLP application development using data-centric AI workflows, reducing development time from months to days.