The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Sam Charrington

team@twimlai.com

For verified host and producer emails, sign up to view.

Booking Overview

A go-to AI show for PR and communications leaders covering practical, enterprise-focused advances in ML/AI—less hype, more technical substance and real deployment lessons. Great for positioning AI researchers, platform builders, and production-minded experts, since episodes typically translate cutting-edge work into business, engineering, and operator takeaways.

Metrics

Episodes: 786

Frequency: Irregular

Rating: 4.7/5.0

Estimated listeners: 10k-100k

Gender skew: Male

Location: USA

YouTube: 29.9k subscribers

Instagram: 801 followers

Contact Information

team@twimlai.com

For verified host and producer emails, sign up to view.

Host

Sam Charrington - Sam Charrington is an industry analyst, speaker, commentator, and thought leader. He hosts the TWIML AI Podcast, sharing top ideas from the ML/AI world with a broad audience spanning researchers, d...

Booking Intelligence

Booking Requirements

high
Typical Credentials:  
Technical leaders with credible ML/AI depth—e.g., university faculty or senior academic researchers, co-founders/CEOs of ML platforms, or heads of engineering/education at prominent AI infrastructure companies. Guests should be able to explain systems-level or production-level concepts (deployment, evaluation, observability, inference, enterprise data/graphs) in a way that resonates with both researchers and engineering/IT leaders.
Required Achievements:  
Founded or leads an ML/AI company/platform, Professor/researcher at a top institution, Significant technical publications, benchmarks, or widely cited research/engineering work, Demonstrated real-world deployments or production operations of ML/LLM systems

Recent Guest Discussions

Jure Leskovec - Relational Foundation Models, Relational Deep Learning Over Enterprise Graphs, Enterprise Data Representations, AI For Science (ai Virtual Cell), Benchmarking, Explainability, Deployment And Integration

Scott Clark - Llm/agent Observability And Eval Failures, Telemetry/monitoring/analytics, Trace Clustering Via Vector Fingerprints, Adaptive Online Approaches, Instrumentation And Genai Conventions

Philip Kiely - Inference Engineering, Inference Vs Model Serving, Performance/efficiency Techniques (batching, Quantization, Speculation, KV Cache Reuse), Runtime Landscape, Research-to-production Timelines

Recent Topics

Machine Learning, Artificial Intelligence, Llm, Inference, Evaluation

Episodes

Here's the recent few episodes on
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
:

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

May 21, 2026

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational de...

How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

May 07, 2026

In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Maslow’s hierarchy of observability: telemetry for logging, monitoring for known signals, and post-production or online analytics to surface unknown unknowns. We dig into examples of real-world failures Scott’s team has seen in production systems, such as “lazy” tool-use hallucinations that standard ...

How to Engineer AI Inference Systems with Philip Kiely - #766

April 30, 2026

In this episode, Philip Kiely, head of AI education at Baseten, joins us to unpack the fast-evolving discipline of inference engineering. We explore why inference has become the stickiest and most critical workload in AI, how it blends GPU programming, applied research, and large-scale distributed systems, and where the line sits between inference and model serving. Philip shares how research-to-production can move in hours, not months, and why understanding “the knobs” of inference—batching,...

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