Unleashing the Full Potential of Your Recommender System in Production
Improve recommender systems through production monitoring, data insights, and drift detection to enhance performance and drive better business outcomes.
Trade-off Between Optimality and Explainability in Machine Learning Models
Explore the trade-off between model explainability and accuracy in AI/ML, focusing on counterfactual explainability and optimization challenges in high-risk applications.
Element AI's Knowledge Scout Platform for COVID-19 Research
Découvrez la plateforme Knowledge Scout d'Element AI, conçue pour accélérer la recherche sur le COVID-19 en analysant efficacement des milliers d'articles scientifiques.
Beyond Standard Deep Learning Models for Time Series and Sequences
Explore advanced techniques for temporal data analysis, including confident predictions and adaptive computational time models for improved sequence-to-sequence processing.
Engineering and Production Techniques for Managing Feature Drift in AI Models
Explore engineering techniques for managing feature drift in large-scale AI models, drawing insights from real-world case studies and best practices.
Bringing Structure Into an Unstructured World - Applying State-of-the-Art Machine Learning at Digits
Explore how Digits applies transformer models to extract structured information from unstructured financial data and deploys ML experiments in production using TensorFlow.
A Cookbook for Deep Continuous-Time Predictive Models
Explore deep continuous-time predictive models for irregularly-sampled time series, from feedforward approaches to latent-variable stochastic differential equations.
AI and Machine Learning in Healthcare During the Coronavirus Pandemic in China
Explore machine learning applications in healthcare during China's coronavirus pandemic, focusing on practical implementations and outcomes.
A Tale of A Thousand Pipelines: Building Scalable Deep Learning Pipelines for Time-Series Datasets
Discover scalable deep learning pipelines for massive time-series datasets, reducing costs by 80% while training hundreds of models efficiently.
Revolutionizing Data Analysis: Combining LLM, RAG, and Domain Knowledge
Leverage NLP and RAG systems to extract insights from structured and unstructured data, generate actionable insights, and empower data-driven analysis without SQL.
Building Trustworthy AI for Environmental Science
Explore trustworthy AI methods for environmental science, focusing on weather, climate, and coastal oceanography predictions, with insights on hail and tornado forecasting.
Moving Beyond the First Portrait of Our Milky Way's Black Hole by Leveraging Underlying Structure
Explore computational imaging techniques used to capture black hole images and leverage data structure to extract evolving details of the Milky Way's supermassive black hole.
AI for Scientists: Accelerating Discovery through Knowledge, Data and Learning
Explore AI-driven scientific discovery, integrating machine learning with expert knowledge to automate data analysis and accelerate research across diverse domains.
Adventures in Not Writing Tests - Using Hypothesis for Code Validation
Explore Hypothesis library for creating robust test inputs, focusing on edge cases. Learn to use Ghostwriting to generate tests from type hints, saving time and validating code.
Forecasting the Future with EarthPT
Explore EarthPT, an open-source Earth Observation pretrained transformer. Learn about its capabilities in predicting surface reflectances, vegetation indices, and potential applications in land use classification and crop yield forecasting.
Kafka Monitoring: What Matters - Key Metrics and Performance Indicators
Explore key metrics for monitoring Kafka at scale, focusing on lag performance. Learn to interpret indicators, create dashboards, and set up proactive alerts for effective troubleshooting.
Analyzing Big Data in Azure Cloud Using Spark and Databricks
Explore Azure Databricks' key features and Spark scenarios through demos, gaining insights on best practices for big data analysis in the Azure cloud.
Gathering Requirements for Your Star Schema
Collaboratively design dimensional models with business users for effective star schema requirements gathering and data visualization.
A Data Engineer, Scientist and Analyst Walk Into a Bar
Explore data professional roles, essential skills, and real-world collaboration in data projects through personal journeys and industry insights.
Star Schema ALL the Things! But Why? - Practical Design Decisions in Data Modeling
Explore practical star schema design, from source to model to report. Learn best practices, test methodologies, and assess performance impacts in data modeling.
The Untruthful Art - Five Ways of Misrepresenting Data
Discover five common data misrepresentation techniques and learn to communicate complex information clearly and accurately in today's information-driven world.