Challenges and Opportunities for Data Miners in the Era of Generative AI
Explore how generative AI reshapes data mining, using material science and chip design as examples. Learn to harness AI's power for solving complex real-world problems and advancing research methodologies.
Machine Learning for Clinical Management: From the Lab to the Hospital
Explore machine learning applications in clinical management, transitioning from research to real-world hospital implementation. Gain insights into healthcare data analytics for improved decision-making and resource optimization.
Frontiers of Foundation Models for Time Series
Explore foundation models for time series data, addressing challenges in science applications and discussing future research directions in deep learning for time series analysis.
The Structure around Real World Problems: Decisions, Knowledge, and Meaning
Explore AI's role in real-world problem-solving, focusing on decisions, knowledge, and meaning. Gain insights from Boeing's AI Chief Technologist on robust systems and autonomous flight.
Lessons Learned While Running ML Models in Harsh Environments
Explore challenges and solutions for deploying ML models in critical financial environments. Gain insights on handling data, scale, ethics, security, and compliance issues in fraud detection systems.
Generative AI in E-Commerce
Explore the impact of generative AI on e-commerce with IEEE Fellow Haixun Wang. Gain insights into semantic search, NLP, and data engineering applications in online retail platforms.
Managing Machine Learning Projects - From Design to Deployment
Explore ML project management strategies, from setup to production integration. Learn to navigate challenges, assess models, and align with business processes for successful implementation.
MLOps for Ad Platforms - Lessons from Ad Tech - Coffee Session 130
Explore MLOps in ad platforms with Andrew Yates. Learn about designing ML components, monitoring systems, and optimizing billion-dollar content marketplaces for long-term revenue and user experience.
Real-time Machine Learning - MLOps Coffee Sessions
Explore real-time machine learning with Chip Huyen, covering learning strategies, MLOps challenges, and streaming complexities. Gain insights on when to implement real-time solutions and avoid common company mistakes.
Real-Time Machine Learning: Features and Inference - MLOps Podcast #135
Explore real-time ML features and inference with LinkedIn experts. Gain insights on implementation, challenges, and best practices for moving from batch to interactive near real-time machine learning.
Systems Engineering in Machine Learning - Navigating Low-Level Challenges
Explore systems engineering in ML with Andrew Dye, discussing low-level work, blending disciplines, and ML infrastructure challenges. Gain insights on distributed training, managing scale, and bridging gaps between firmware and MLOps.
Machine Learning Operations - What is it and Why Do We Need It?
Explore Machine Learning Operations (MLOps) with expert Niklas Kühl, discussing its definition, importance, and practical applications in industrial ML projects for effective automation and operationalization.
Let's Talk About Raw Documents - Extracting Structured Data for ML Pipelines
Explore raw document processing for ML pipelines with Unstructured.io's open-source tools. Learn to extract structured data, build custom preprocessing APIs, and integrate with popular ML frameworks.
Machine Learning in Production: A Data Scientist's Perspective from Ubisoft
Explore ML in production at Ubisoft with Jean-Michel Daignan. Gain insights on platform development, SDK tools, generative AI, and balancing value delivery with rapid development in the gaming industry.
How a Manager Became a Believer in DevOps for Machine Learning
Explore insights on leading data teams, improving as a software engineer, and migrating from monolith to microservices in healthcare. Learn from Keith Trnka's experience in NLP, machine learning, and telemedicine.
The Birth and Growth of Spark: An Open Source Success Story
Explore a insightful conversation with Matei Zaharia, creator of Apache Spark, on open-source success, MLflow development, and the future of AI applications combining LLMs with other systems.
Why is MLOps Hard in an Enterprise? - Challenges and Solutions
Explore MLOps challenges in enterprise settings, focusing on data ecosystem complexity, cross-team collaboration, and standardization issues. Learn strategies for model reusability and effective implementation.
Clean Code for Data Scientists - MLOps Coffee Sessions
Explore clean coding practices for data scientists, Shopify's real-time serving platform Merlin, and insights on LLMs in production with Matt Sharp, Data Developer at Shopify.
Dataframes Are All You Need: MLOps on Easy Mode
Explore the power of dataframes in MLOps with Jay Chia. Learn how this flexible abstraction simplifies complex data processing, analytics, and machine learning pipelines using Daft as an example.
The Emerging Toolkit for Reliable, High-quality LLM Applications
Explore emerging techniques in LLMOps for building, tuning, and maintaining high-quality LLM applications. Learn about testing tools, programming frameworks, and methods to control LLM outputs for desired goals.
Using Vector Databases: Practical Advice for Production - LLMs in Prod Conference
Explore vector databases for LLMs in production. Learn design patterns, context retrieval, feature injection, and common pitfalls. Gain practical insights for efficient deployment and management.