All Episodes
The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations — 182 episodes · page 1 of 2
How Data Teams Build Trust With Synthetic Data
How Data Teams Measure AI ROI Without Guesswork
How Data Teams Handle Model Uncertainty
How Data Teams Stop Guessing With Bayesian Optimization
How Data Teams Scale Explainability Without Slowing Down
How Data Teams Stop Guessing With Bayesian Optimization
How Data Teams Stop Guessing With Bayesian Optimization
How Retailers Win With Dynamic Pricing
How Banks Use Real-Time Data To Stop Fraud Before It Happens
How Data Teams Ship Models Without Breaking Trust
How Data Teams Stop Guessing With Bayesian Optimization
The Hidden Cost of Data Lineage in Enterprise AI
Why Data Teams Struggle With Real-Time Decisioning
The Hidden Cost of Model Drift in Production
How Data Teams Turn Insights Into Action
How Retailers Predict Demand Without Guesswork
Why Your AI Models Are Silently Failing Customers
How Data Teams Build Trust With Synthetic Data
Why Data Labels Are Your Most Expensive Asset
How Data Teams Master Causal Inference for Better Decisions
How Data Teams Prevent AI Hallucinations
How Banks Use Data for Real-Time Fraud Detection
How Data Teams Manage Model Risk Without Killing Innovation
How Data Teams Master Feature Store Governance
Why Your AI Models Fail in Production
Why Your AI Models Are Confidently Wrong
How Data Teams Handle Model Decay in Production
How Data Teams Use Counterfactual Explanations
Why Your Data Models Fail in Production
How Data Teams Master Causal Inference for Better Decisions
How Data Teams Master Active Learning for Smarter AI
Why Your Data Models Fail at Scale
How Data Teams Measure AI Impact Beyond Accuracy
How Data Teams Build Causal AI Models
How Data Teams Build Model Cards for Transparency
How Data Teams Build Explainable AI Systems
How Data Teams Measure ROI Beyond Accuracy
How Data Teams Handle Concept Drift
How Data Teams Use Synthetic Data to Train AI
Why Your AI Models Are Failing in Production
How Data Teams Measure Model Impact Beyond Accuracy
How Data Teams Use Data Lineage to Trace Model Decisions
How Data Teams Use Feature Engineering to Build Better Models
How Data Teams Use Survival Analysis to Predict Churn
How Data Scientists Use Knowledge Distillation to Compress Models
How Data Teams Use Shapley Values to Explain Model Predictions
How Data Teams Use Data Contracts to Prevent Pipeline Chaos
How Data Teams Use Embedding Drift to Catch Model Decay
How Data Teams Use Embeddings to Capture Meaning
How Data Scientists Use Probabilistic Programming for Robust Forecasting
How Data Teams Use Incremental Learning for Streaming Data
How Data Scientists Use Feature Selection to Cut Noise
How Data Scientists Use Counterfactual Explanations
How Data Teams Use Model Monitoring to Catch Silent Failures
How Data Scientists Use Transfer Learning to Save Time
How Data Teams Use Feature Stores to Keep Models Honest
How Data Scientists Use Active Learning to Cut Labeling Costs
How Data Scientists Use Conformal Prediction for Reliable AI
How Data Scientists Use Multi-Armed Bandits to Balance Exploration and Exploitation
How Data Scientists Use Manifold Learning to Untangle High-Dimensional Data
How Data Scientists Use Synthetic Data to Bridge Privacy and Utility
How Data Scientists Use Causal Forests for Personalized Treatment Effects
How Data Scientists Use Quantile Regression for Uncertainty
How Data Scientists Use Gradient Boosting for Smarter Predictions
How Data Scientists Use Calibration to Fix Model Probabilities
How Data Scientists Use Data Lineage to Debug Pipelines
How Data Scientists Use Knowledge Graphs to Power Recommendation Engines
How Data Scientists Use Shapley Values for Fairer Models
How Data Scientists Use Anomaly Detection in Manufacturing
How Data Scientists Use Embeddings for Search
How Data Scientists Reduce Model Bias with Fairness Metrics
Why Your Recommendation Model Needs Online Learning
Data Drift: How Models Silently Decay and What Teams Do About It
How Data Scientists Use Counterfactual Reasoning on Campaigns
How Data Scientists Use Survival Analysis for Customer Lifetime Value
How Data Contracts Are Fixing Broken Data Pipelines
How Data Scientists Use Causal Inference for Marketing Attribution
How Data Scientists Use Feature Stores for Consistent ML Pipelines
How Bayesian A/B Testing Speeds Up Experimentation
How Graph Neural Networks Are Accelerating Drug Discovery
How Data Scientists Build Natural Language Interfaces for Databases
How Data Scientists Use Transformers for Time Series Forecasting
How Data Scientists Generate Synthetic Data That Works
How Data Scientists Use Spectral Clustering for Community Detection
Federated Learning Boosts Hospital AI Without Sharing Data
How Data Scientists Use SHAP Values to Explain Model Predictions
How Data Scientists Predict Warehouse Fires With Sensor Data
How Data Scientists Use Location Data for Retail Analytics
How Data Scientists Deploy Model Monitoring at Scale
How Data Scientists Build Churn Models That Actually Predict
Data Scientists Are Building Cloud Cost Forecasts With Time Series
How Data Scientists Use Model Distillation for Deployment
How Data Scientists Use Embeddings for Anomaly Detection
How Data Scientists Use Synthetic Control for Causal Impact
How Data Scientists Use Counterfactual Explanations
How Data Scientists Use Reinforcement Learning for Dynamic Pricing
How Data Scientists Use AutoML for Production Pipelines
How Data Scientists Use Reservoir Computing for Time Series
How Data Scientists Use Differential Privacy in Practice
How Data Scientists Use Bayesian A-B Testing for Smarter Decisions