Human Activity Recognition - Learning with Less Labels and Privacy Preservation
Learn advanced techniques for human activity recognition with limited labeled data and privacy preservation, including pseudoleveling, contrastive learning, and zero-shot approaches.
Turn Data Chaos into AI Strategy with Programmatic AI Data Development
Discover how programmatic data development is revolutionizing enterprise AI specialization in this 27-minute talk by Elena Boiarskaia, Head of Applied Machine Learning at Snorkel AI. Learn about the transformation from disconnected, manual data tasks to…
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.
Managing Human in the Loop Systems Without Burning Out Your Engineers
Explore strategies for managing human-in-the-loop systems efficiently, focusing on Pinterest's approach to labeling, training, and measuring shopping content quality with minimal engineering effort.
Know Your Data: Protect and Prevent Data Loss with Microsoft Information Protection
Discover Microsoft Information Protection's comprehensive solution for data security, compliance, and loss prevention across cloud, on-premise, and endpoint environments.
Leadership and AI
Explore the critical role of leadership in AI adoption, emphasizing the importance of understanding AI models, their applications, and potential pitfalls to drive organizational success and avoid historical missteps.
Prepping the Bar - Insights from Cleaning and Labeling a Cocktail Dataset
Explore data cleaning and labeling techniques using a cocktail dataset, gaining insights for effective data preparation in business analytics projects.
How Georgetown Helps Data Scientists and SMEs Collaborate on Machine Learning
Explore Georgetown's approach to enhancing ML project workflows and fostering collaboration between data scientists and subject matter experts using Snorkel.
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.
Medical Imaging with Azure Machine Learning
Explore Azure Machine Learning for medical imaging applications, covering data management, model development, deployment, and explainability in healthcare scenarios.
Build NLP Model with Azure ML AutoML NLP Capability
Learn to build NLP models using Azure ML AutoML for tasks like classification and named entity recognition. Explore multilingual support, data labeling, and distributed training for efficient model development.
What's New in Azure Machine Learning - Hybrid with Azure Arc and More
Explore Azure Machine Learning's latest features: hybrid with Azure Arc, output datasets, labeling, and interactive data preparation. Learn about custom roles, RBAC, and responsible AI development.
Jeremy Howard on Platform.ai and Fast.ai - Full Stack Deep Learning - March 2019
Explore machine learning innovations with Jeremy Howard, covering data labeling, deep learning successes, and insights from Fast.ai and Platform.ai for effective AI development.
Data Management - Full Stack Deep Learning - March 2019
Explore data management essentials: labeling, storage, versioning, and processing. Learn about data flywheels, annotation techniques, and efficient workflows for deep learning projects.
Lukas Biewald on Founding Weights & Biases and FigureEight - Full Stack Deep Learning - March 2019
Insights from AI industry pioneer on founding data labeling and ML tools companies, exploring challenges, breakthroughs, and the future of machine learning applications.
Teaching Machines Through Human Explanations
Explore an explanation-based learning framework for NLP that improves label efficiency and reliability, reducing reliance on extensive datasets and machine learning expertise.
ChatGPT Unleashed - Best Practices for NLP Applications
Explore ChatGPT's capabilities, best practices for NLP applications, and hands-on demos. Learn about language models, their history, and practical use cases in this comprehensive webinar.
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.
Self-Driving from Vision Only - Tesla's Self-Driving Progress by Andrej Karpathy - Talk Analysis
Analysis of Tesla's vision-only self-driving progress, discussing data labeling, edge-case sampling, neural network training, and the advantages of camera-only systems over multi-sensor approaches.
Making MLOps Uncool Again
Learn to build an automated MLOps workflow using Git, GitHub, and open-source tools, covering the entire ML model lifecycle from data labeling to prediction monitoring.