AI Engineering Podcast

Summary
In this episode I sit down with Hugo Shi, co-founder and CTO of Saturn Cloud, to map the strategic realities of sourcing and operating GPUs across clouds. Hugo breaks down today’s provider landscape—from hyperscalers to full-service GPU clouds, bare metal/concierge providers, and emerging GPU aggregators—and how to choose among them based on security posture, managed services, and cost. We explore practical layers of capability (compute, orchestration with Kubernetes/Slurm, storage, networking, and managed services), the trade-offs of portability on “Kubernetes-native” stacks, and the persistent challenge of data gravity. We also discuss current supply dynamics, the growing availability of on-demand capacity as newer chips roll out, and how AMD’s ecosystem is maturing as real competition to NVIDIA. Hugo shares patterns for separating training and inference across providers, why traditional ML is far from dead, and how usage varies wildly across domains like biotech. We close with predictions on consolidation, full‑stack experiences from GPU clouds, financial-style GPU marketplaces, and much-needed advances in reliability for long-running GPU jobs.

Announcements
Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.
Your host is Tobias Macey and today I'm interviewing Hugo Shi about the strategic realities of sourcing GPUs in the cloud for your training and inference workloads

Interview
Introduction
How did you get involved in machine learning?
Can you start by giving a summary of your understanding of the current market for "cloud" GPUs?
How would you characterize the customer base for the "neocloud" providers?
How is the access to the GPU compute typically mediated?
The predominant cloud providers (AWS, GCP, Azure) have gained market share by offering numerous differentiated services and ease-of-use features. What are the types of services that you might expect from a GPU provider?
The "cloud-native" ecosystem was developed with the promise of enabling workload portability, but the realities are often more complicated. What are some of the difficulties that teams encounter when trying to adapt their workloads to these different cloud providers?
What are the toolchains/frameworks/architectures that you are seeing as most effective at adapting to these different compute environments?
One of the major themes in the 2010s that worked against multi-cloud strategies was the idea of "data gravity". What are the strategies that teams are using to mitigate that tax on their workloads?
That is a more substantial impact when dealing with training workloads than for inference compute. How are you seeing teams think about the balance of cost savings vs. operational complexity for those different workloads?
What are the most interesting, innovative, or unexpected ways that you have seen teams capitalize on GPU capacity across these new providers?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on enabling teams to execute workloads on these neoclouds?
When is a "neocloud" or "GPU cloud" provider the wrong choice?
What are your predictions for the future evolutions of GPU-as-a-service as hardware availability improves and model architectures become more efficient?

Contact Info
LinkedIn

Parting Question
From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements
Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links
Saturn Cloud
Pandas
NumPy
MatLab
AWS
GCP
Azure
Oracle Cloud
RunPod
FluidStack
SFCompute
KubeFlow
Lightning AI
DStack
Metaflow
Flyte
Arya AI
Dagster
Coreweave
Vultr
Nebius
Vast.ai
Weka
Vast Data
Slurm
CNCF == Cloud-Native Computing Foundation
Kubernetes
Terraform
ECS
Helm Chart
Block Storage
Object Storage
Container Registry
Crusoe
Alluxio
Data Virtualization
GB300
H100
Spot Instance
AWS Trainium
Google TPU (Tensor Processing Unit)
AMD
ROCM
PyTorch
Google Vertex AI
AWS Bedrock
CUDA Python
Mojo
XGBoost
Random Forest
Ludwig - Uber Deep Learning AutoML
Paperspace
Voltage Park
Weights & Biases

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering Podcast Niklas Gustavsson, Chief Architect at Spotify, talks about scaling AI across engineering and product. He explores how Spotify's highly distributed architecture was built to support rapid adoption of coding agents like Copilot, Cursor, and Claude Code, enabled by standardization and Backstage. The conversation covers the tension between bottoms-up experimentation and platform standardization, and how Spotify is moving toward monorepos and fleet management. Niklas discusses the emergence of "fleet-wide agents" that can execute complex code changes with robust testing and LLM-as-judge loops to ensure quality. He also touches on the shift in engineering workflows as code generation accelerates, the growing use of agents beyond coding, and the lessons learned in sandboxing, agent skills/rules, and shared evaluation frameworks. Niklas highlights Spotify's decade-long experience with ML product work and shares his vision for deeper end-to-end integration of agentic capabilities across the full product lifecycle and making collaborative "team-level memory" for agents a reality.

Announcements
Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.
Your host is Tobias Macey and today I'm interviewing Niklas Gustavsson about how Spotify is scaling AI usage in engineering and product work

Interview

Introduction
How did you get involved in machine learning?
Can you start by giving an overview of your engineering practices independent of AI?
What was your process for introducing AI into the developmer experience? (e.g. pioneers doing early work (bottom-up) vs. top-down)
There are countless agentic coding tools on the market now. How do you balance organizational standardization vs. exploration?
Beyond the toolchain, what are your methods for sharing best practices and upskilling engineers on use of agentic toolchains for software/product engineering?
Spotify has been operationalizing ML/AI features since before the introduction of LLMs and transformer models. How has that history helped inform your adoption of generative AI in your overall engineering organization?
As you use these generative and agentic AI utilities in your day-to-day, how have those lessons learned fed back into your AI-powered product features?
What are some of the platform capabilities/developer experience investments that you have made to improve the overall effectiveness of agentic coding in your engineering organization?
What are some examples of guardrails/speedbumps that you have introduced to avoid injecting unreliable or untested work into production?
As the (time/money/cognitive) cost of writing code drops that increases the burden on reviewing that code. What are some of the ways that you are working to scale that side of the equation?
What are some of the ways that agentic coding/CLI utilities have bled into other areas of engineering/opertions/product development beyond just writing code?
What are the most interesting, innovative, or unexpected ways that you have seen your team applying AI/agentic engineering practices?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on operationalizing and scaling agentic engineering patterns in your teams?
When is agentic code generation the wrong choice?
What do you have planned for the future of AI and agentic coding patterns and practices in your organization?

Contact Info

LinkedIn

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

Spotify
Developer Experience
LLM == Large Language Model
Transformers
BackStage
GitHub Copilot
Cursor
Claude Skills
Monorepo
MCP == Model Context Protocol
Claude Code
Product Manager
DORA Metrics
Type Annotations
BigQuery
PRD == Product Requirements Document
AI Evals
LLM-as-a-Judge
Agentic Memory

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode Joe Devon, co-founder of Global Accessibility Awareness Day (GAAD), talks about how generative AI can both help and harm digital accessibility — and what it will take to tilt the balance toward inclusion. Joe shares his personal motivation for the work, real-world stakes for disabled users across web, mobile, and developer tooling, and compelling stories that illustrate why accessible design is a human-rights issue as much as a compliance checkbox. He digs into AI’s current and future roles: from improving caption quality and auto-generating audio descriptions to evaluating how well code-gen models produce accessible UI by default. Joe introduces AIMAC (AI Model Accessibility Checker), a new benchmark comparing top models on accessibility-minded code generation, what the results reveal, and how model providers and engineering teams can practically raise the bar with linters, training data, and cultural change. He closes with concrete guidance for leaders, why involving people with disabilities is non-negotiable, and how solving for edge cases makes AI—and products—better for everyone.

Announcements
Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
When ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.
Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.
Your host is Tobias Macey and today I'm interviewing Joe Devon about opportunities for using generative AI to improve the accessibility of digital technologies

Interview

Introduction
How did you get involved in AI?
Can you starty by giving an overview of what is included in the term "accessibility"?
What are some of the major contributors to a lack of accessibility in digital experiences today?
Beyond the web, what are some of the other platforms and interfaces that struggle with accessibility?
What role does/can generative AI utilities play in improving the accessibility of applications?
You recently helped create the AI Model Accessibility Checker (AIMAC) to benchmark which coding agents produce the most accessible code. What are the goals of that project and desired outcomes from its introduction?What were the key findings from AIMAC's initial benchmarking results? Were there any surprises in terms of which models performed better or worse at generating accessible code?

The automation offered by using agentic software development toolchains reduces the manual effort involved in building accessible interfaces. What are the opportunities for using generative AI utilities to act as an assistive mechanism for existing sites/technologies?
Beyond code generation, what other aspects of the AI development lifecycle need accessibility considerations - training data, model outputs, user interfaces for AI tools themselves?
You co-host the Accessibility and Gen AI Podcast. What are some of the common misconceptions you encounter about AI's role in accessibility, either from the AI community or the accessibility community?
There's often tension between moving fast with AI adoption and ensuring inclusive design. How do you advise engineering teams to balance innovation speed with accessibility requirements?
What specific accessibility issues are most amenable to AI solutions today, and which ones still require human judgment and expertise?
As AI models become more capable at generating code and interfaces, what guardrails or validation processes should engineering teams implement to ensure accessibility standards are met?
How do you see the role of accessibility specialists evolving as AI tools become more prevalent in the development workflow? Does AI augment their work or change it fundamentally?
For engineering leaders building platform and data infrastructure, what accessibility considerations should be baked into foundational systems that AI applications will be built upon?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on acessibility awareness?

Contact Info

LinkedIn

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

AIMAC GitHub

Global Accessibility Awareness Day (GAAD)
GAAD Foundation
AltaVista
Cursor
Accessibility
Braille Display
Ben OgilvieState of Mobile App Accessibility Report

VT-100
Ghostty
Warp Terminal
LLM-as-a-Judge
FFMPEG
Aria Tags
Axe-Core
MiniMax M1
Codex Mini
Qwen
Kimi
Google Lighthouse
GitHub Copilot
Be-My-EyesBe-My-AI

WebAIM
XRAccess
XR == Extended Reality
Deque University
Fable accessibility feedback organization

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode product and engineering leader Preeti Shukla explores how and when to add agentic capabilities to SaaS platforms. She digs into the operational realities that AI agents must meet inside multi-tenant software: latency, cost control, data privacy, tenant isolation, RBAC, and auditability. Preeti outlines practical frameworks for selecting models and providers, when to self-host, and how to route capabilities across frontier and cheaper models. She discusses graduated autonomy, starting with internal adoption and low-risk use cases before moving to customer-facing features, and why many successful deployments keep a human-in-the-loop. She also covers evaluation and observability as core engineering disciplines - layered evals, golden datasets, LLM-as-a-judge, path/behavior monitoring, and runtime vs. offline checks - to achieve reliability in nondeterministic systems.

Announcements
Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
When ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.
Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.
Your host is Tobias Macey and today I'm interviewing Preeti Shukla about the process for identifying whether and how to add agentic capabilities to your SaaS

Interview

Introduction
How did you get involved in machine learning?
Can you start by describing how a SaaS context changes the requirements around the business and technical considerations of an AI agent?
Software-as-a-service is a very broad category that includes everything from simple website builders to complex data platforms. How does the scale and complexity of the service change the equation for ROI potential of agentic elements?How does it change the implementation and validation complexity?

One of the biggest challenges with introducing generative AI and LLMs in a business use case is the unpredictable cost associated with it. What are some of the strategies that you have found effective in estimating, monitoring, and controlling costs to avoid being upside-down on the ROI equation?
Another challenge of operationalizing an agentic workload is the risk of confident mistakes. What are the tactics that you recommend for building confidence in agent capabilities while mitigating potential harms?A corollary to the unpredictability of agent architectures is that they have a large number of variables. What are the evaluation strategies or toolchains that you find most useful to maintain confidence as the system evolves?

SaaS platforms benefit from unit economics at scale and often rely on multi-tenant architectures. What are the security controls and identity/attribution mechanisms that are critical for allowing agents to operate across tenant boundaries?
What are the most interesting, innovative, or unexpected ways that you have seen SaaS products adopt agentic patterns?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on bringing agentic workflows to SaaS products?
When is an agent the wrong choice?
What are your predictions for the role of agents in the future of SaaS products?

Contact Info
LinkedIn

Parting Question
From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Links

SaaS == Software as a Service
Multi-Tenancy
Few-shot Learning
LLM as a Judge
RAG == Retrieval Augmented Generation
MCP == Model Context Protocol
Loveable

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode Craig McLuckie, co-creator of Kubernetes and founder/CEO of Stacklok, talks about how to improve security and reliability for AI agents using curated, optimized deployments of the Model Context Protocol (MCP). Craig explains why MCP is emerging as the API layer for AI‑native applications, how to balance short‑term productivity with long‑term platform thinking, and why great tools plus frontier models still drive the best outcomes. He digs into common adoption pitfalls (tool pollution, insecure NPX installs, scattered credentials), the necessity of continuous evals for stochastic systems, and the shift from “what the agent can access” to “what the agent knows.” Craig also shares how ToolHive approaches secure runtimes, a virtual MCP gateway with semantic search, orchestration and transactional semantics, a registry for organizational tooling, and a console for self‑service—along with pragmatic patterns for auth, policy, and observability.

Announcements
Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
When ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.
Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.
Your host is Tobias Macey and today I'm interviewing Craig McLuckie about improving the security of your AI agents through curated and optimized MCP deployment
Interview
Introduction
How did you get involved in machine learning?
MCP saw huge growth in attention and adoption over the course of this year. What are the stumbling blocks that teams run into when going to production with MCP servers?
How do improperly managed MCP servers contribute to security problems in an agent-driven software development workflow?
What are some of the problematic practices or shortcuts that you are seeing teams implement when running MCP services for their developers?
What are the benefits of a curated and opinionated MCP service as shared infrastructure for an engineering team?
You are building ToolHive as a system for managing and securing MCP services as a platform component. What are the strategic benefits of starting with that as the foundation for your company?There are several services for managing MCP server deployment and access control. What are the unique elements of ToolHive that make it worth adopting?

For software-focused agentic AI, the approach of Claude Code etc. to be command-line based opens the door for an effectively unbounded set of tools. What are the benefits of MCP over arbitrary CLI execution in that context?
What are the most interesting, innovative, or unexpected ways that you have seen ToolHive/MCP used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on ToolHive?
When is ToolHive the wrong choice?
What do you have planned for the future of ToolHive/Stacklok?

Contact Info
GitHub
LinkedIn
Parting Question
From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?
Links
StackLok
MCP == Model Context Protocol
Kubernetes
CNCF == Cloud Native Computing Foundation
SDLC == Software Development Life Cycle
The Bitter Lesson
TLA+
Jepsen Tests
ToolHive
API Gateway
Glean

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0