Bayesian vs. Frequentist Thinking in Marketing Mix Modeling

Bayesian vs. Frequentist Thinking in Marketing Mix Modeling

In this episode, we unpack how Bayesian and Frequentist statistical approaches tackle marketing performance analysis, focusing on Marketing Mix Modeling (MMM). You’ll learn the key differences in interpretation, how Bayesian methods enable sequential updates and uncertainty modeling, and why they’re gaining traction in modern marketing analytics. Ideal for marketers, data scientists, and anyone curious about the “why” behind the math.

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Avsnitt(25)

The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are

The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are

MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is the one you build yourself, on your data, for your task.

30 Juli 20min

Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop

Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop

A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes everything about where AI can run.

16 Juli 20min

The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other

The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other

90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective tissue before the ecosystem fragments.

2 Juli 21min

KV Cache Compression: The Memory Wall Nobody Talks About

KV Cache Compression: The Memory Wall Nobody Talks About

Your GPU is not compute-bound. It is memory-bound. The KV cache is eating half your inference budget, and two ICLR 2026 breakthroughs KVTC and TurboQuant are about to change the math entirely.

18 Juni 21min

Context Rot: Why Million-Token Windows Quietly Fail

Context Rot: Why Million-Token Windows Quietly Fail

Models advertise million-token windows but accuracy degrades well before the limit. Three recent studies, the mechanisms behind the rot, and a practitioner playbook for what to do Monday.

4 Juni 21min

LLMOps: Operating Large Language Models in Production

LLMOps: Operating Large Language Models in Production

Building an AI model is one thing: keeping a large language model running reliably in the real world is another. In this episode, we discuss LLMOps, the emerging set of practices and tools for deployi...

26 Maj 28min

TinyML & Edge AI: Machine Learning on Devices

TinyML & Edge AI: Machine Learning on Devices

In this episode, we explore how AI is moving from the cloud to tiny devices. TinyML is the field of optimizing models and algorithms to run on microcontrollers, smartphones, and other edge devices wit...

12 Maj 25min

AI Hardware: GPUs, TPUs and Beyond

AI Hardware: GPUs, TPUs and Beyond

This episode is all about the specialized hardware that makes modern AI possible. We explain how GPUs became the workhorses of deep learning by offering massive parallelism for matrix math, and how co...

28 Apr 25min

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