📐 ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

📐 ML Fundamentals

Mastering Software Engineering for Data Scientists // Catherine Nelson // MLOps podcast #245 clip

MLOps.community Beginner 1y ago

📐 ML Fundamentals

Quality Metrics and Tools in Michelangelo 2.0 // MLOps Podcast #239 clip 2

MLOps.community Advanced 1y ago

📐 ML Fundamentals

All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // Podcast #245

MLOps.community Beginner 1y ago

📐 ML Fundamentals

Unifying Systems with Uber’s Michelangelo Platform // MLOps Podcast #239 clip

MLOps.community Advanced 1y ago

📐 ML Fundamentals

Uber's Michelangelo: Strategic AI Overhaul and Impact // MLOps podcast #239

MLOps.community Advanced 1y ago

📐 ML Fundamentals

Rethinking Retraining: Cost-Effective AI Solutions // Patrick Beukema // MLOps podcast #225 clip

MLOps.community Advanced 1y ago

📐 ML Fundamentals

AI Quality in Mo's Eyes // Mohamed Elgendy // MLOps Podcast #229 clip

MLOps.community Intermediate 1y ago

📐 ML Fundamentals

What is AI Quality? // Mohamed Elgendy // MLOps Podcast #229

MLOps.community Intermediate 1y ago

📐 ML Fundamentals

Beyond AGI, Can AI Help Save the Planet? // Patrick Beukema // MLOps Podcast #225

MLOps.community Advanced 1y ago

📐 ML Fundamentals

AI Safety and Reliability // Petar Tsankov // MLOps podcast #218 clip

MLOps.community Beginner 1y ago

📐 ML Fundamentals

The State of Production Machine Learning in 2024 // Alejandro Saucedo // AI in Production

MLOps.community Intermediate 1y ago

📐 ML Fundamentals

Language Processing, Recommendations, System Integration // Aayush Mugdal // MLOps Podcast #211 clip

MLOps.community Advanced 2y ago

📐 ML Fundamentals

The Myth of AI Breakthroughs // Jonathan Frankle // MLOps Podcast #205

MLOps.community Advanced 2y ago

📐 ML Fundamentals

Practical Approach to ML and AI System Design // Nathan Ryan Frank // MLOps Podcast #199 clip

MLOps.community Beginner 2y ago