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Ereli Eran is the Founding Engineer at 7AI, where he’s focused on building and scaling the company’s agentic AI-driven cybersecurity platform — developing autonomous AI agents that triage alerts, investigate threats, enrich security data, and enable end-to-end automated security operations so human teams can focus on higher-value strategic work.

Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale // MLOps Podcast #361 with Ereli Eran, Founding Engineer at 7AI

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// Abstract
A conversation on how AI coding agents are changing the way we build and operate production systems. We explore the practical boundaries between agentic and deterministic code, strategies for shared responsibility across models, engineering teams, and customers, and how to evaluate agent performance at scale. Topics include production quality gates, safety and cost tradeoffs, managing long-tail failures, and deployment patterns that let you ship agents with confidence.

// Bio
Ereli Eran is a founding engineer at 7AI, where he builds agentic AI systems for security operations and the production infrastructure that powers them. His work spans the full stack - from designing experiment frameworks for LLM-based alert investigation to architecting secure multi-tenant systems with proper authentication boundaries. Previously, he worked in data science and software engineering roles at Stripe, VMware Carbon Black, and was an early employee of Ravelin and Normalyze.

// Related Links
Website: https://7ai.com/
Coding Agents Conference: https://luma.com/codingagents

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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Ereli on LinkedIn: /erelieran/

Timestamps:
[00:00] Language Sensitivity in Reasoning
[00:25] Value of Claude Code
[01:54] AI in Security Workflows
[06:21] Agentic Systems Failures
[12:50] Progressive Disclosure in Voice Agents
[16:39] LLM vs Classic ML
[19:44] Hybrid Approach to Fraud
[25:58] Debugging with User Feedback
[33:52] Prompts as Code
[42:07] LLM Security Workflow
[45:10] Shared Memory in Security
[49:11] Common Agent Failure Modes
[53:34] Wrap up

Nick Gillian is the Co-Founder and CTO at Archetype AI, working on physical AI foundation models that understand and reason over real-world sensor data.

Physical AI: Teaching Machines to Understand the Real World // MLOps Podcast #360 with Nick Gillian, Co-Founder and CTO of Archetype AI

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/ Abstract
As AI moves beyond the cloud and simulation, the next frontier is Physical AI: systems that can perceive, understand, and act within real-world environments in real time. In this conversation, Nick Gillian, Co-Founder and CTO of Archetype AI, explores what it actually takes to turn raw sensor and video data into reliable, deployable intelligence.

Drawing on his experience building Google’s Soli and Jacquard and now leading development of Newton, a foundational model for Physical AI, Nick discusses how real-time physical understanding changes what’s possible across safety monitoring, infrastructure, and human–machine interaction. He’ll share lessons learned translating advanced research into products that operate safely in dynamic environments, and why many organizations underestimate the challenges and opportunities of AI in the physical world.

// Bio
Nick Gillian, Ph.D., is Co-Founder and CTO of Archetype AI with over 15 years of experience turning advanced AI and interaction research into real-world products. At Archetype, he leads the AI and engineering teams behind Newton—a first-of-its-kind Physical AI foundational model that can perceive, understand, and reason about the physical world. Before co-founding Archetype, Nick was a Senior Staff Machine Learning Engineer at Google and a researcher at MIT, where he developed AI and ML methods for real-time sensor understanding. At Google’s Advanced Technology and Projects group, he led machine learning research that powered breakthrough products like Soli radar and Jacquard, and helped advance sensing algorithms across Pixel, Nest, and wearable devices.

// Related Links
Website: https://www.archetypeai.io/https://www.archetypeai.io/blog/timefusion-newton https://www.nature.com/articles/s41598-023-44714-2https://www.youtube.com/watch?v=Pow4utY9teU https://www.youtube.com/watch?v=uE0jjdzwe9w https://arxiv.org/abs/2410.14724
Coding Agents Conference: https://luma.com/codingagents

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Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Nick on LinkedIn: /nick-gillian-b27b1094/

Timestamps:[00:00] Physical Agent Framework[00:56] Physical AI Clarification[06:53] Building a Repair Model[12:41] World Models and LLMs[17:17] Data Weighting Strategies[24:19] Data Diversity vs Quantity[38:30] R&D and Product Creation[41:22] Construction Site Data Shipping[50:33] Wrap up

Kris Beevers is the CEO at NetBox Labs, working on turning NetBox into the system of record and automation backbone for modern and AI-driven infrastructure.

Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth // MLOps Podcast #359 with Kris Beevers, CEO of NetBox Labs

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// Abstract
Hundreds of neocloud operators and "AI Factory" builders have emerged to serve the insatiable demand for AI infrastructure. These teams are compressing the design, build, deploy, operate, scale cycle of their infrastructures down to months, while managing massive footprints with lean teams. How? By applying modern intent-driven infrastructure automation principles to greenfield deployments. We'll explore how these teams carry design intent through to production, and how operating and automating around consistent infrastructure data is compressing "time to first train".

// Bio
Kris Beevers is the Co-founder and CEO of NetBox Labs. NetBox is used by nearly every Neocloud and AI datacenter to manage their networks and infrastructure. Kris is an engineer at heart and by background, and loves the leverage infrastructure innovation creates to accelerate technology and empower engineers to do their best work. A serial entrepreneur, Kris has founded and helped lead multiple other successful businesses in the internet and network infrastructure. Most recently, he co-founded and led NS1, which was acquired by IBM in 2023. He holds a Ph.D. in Computer Science from Rensselaer Polytechnic Institute and is based in New Jersey.

// Related Links
Website: https://netboxlabs.com/
Coding Agents Conference: https://luma.com/codingagents

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore
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Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Kris on LinkedIn: /beevek/

Timestamps:
[00:00] Observability and Delta Analysis
[00:26] New World Exploration
[04:06] Bottlenecks in AI Infrastructure
[13:37] Data Center Optimization Challenges
[19:58] Tech Stack Breakdown
[25:26] Data Center Design Principles
[31:32] Constraints and Automation in Design
[40:00] Complexity in Data Centers
[45:02] GPU Cloud Landscape
[50:24] Data Centers in Containers
[57:45] Observability Beyond Software
[1:04:43] Tighter Integrations vs NetBox
[1:06:47] Wrap up

Mike Oaten is the Founder and CEO of TIKOS, working on building AI assurance, explainability, and trustworthy AI infrastructure, helping organizations test, monitor, and govern AI models and systems to make them transparent, fair, robust, and compliant with emerging regulations.

Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces // MLOps Podcast #358 with Mike Oaten, Founder and CEO of TIKOS

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// Abstract
As AI models move into high-stakes environments like Defence and Financial Services, standard input/output testing, evals, and monitoring are becoming dangerously insufficient. To achieve true compliance, MLOps teams need to access and analyse the internal reasoning of their models to achieve compliance with the EU AI Act, NIST AI RMF, and other requirements.

In this session, Mike introduces the company's patent-pending AI assurance technology that moves beyond statistical proxies. He will break down the architecture of the Synapses Logger, a patent-pending technology that embeds directly into the neural activation flow to capture weights, activations, and activation paths in real-time.

// Bio
Mike Oaten serves as the CEO of TIKOS, leading the company’s mission to progress trustworthy AI through unique, high-performance AI model assurance technology. A seasoned technical and data entrepreneur, Mike brings experience from successfully co-founding and exiting two previous data science startups: Riskopy Inc. (acquired by Nasdaq-listed Coupa Software in 2017) and Regulation Technologies Limited (acquired by mnAi Data Solutions in 2022).

Mike's expertise spans data, analytics, and ML product and governance leadership. At TIKOS, Mike leads a VC-backed team developing technology to test and monitor deep-learning models in high-stakes environments, such as defence and financial services, so they comply with the stringent new laws and regulations.

// Related Links
Website: https://tikos.tech/
LLM guardrails: https://medium.com/tikos-tech/your-llm-output-is-confidently-wrong-heres-how-to-fix-it-08194fdf92b9
Model Bias: https://medium.com/tikos-tech/from-hints-to-hard-evidence-finally-how-to-find-and-fix-model-bias-in-dnns-2553b072fd83
Model Robustness: https://medium.com/tikos-tech/tikos-spots-neural-network-weaknesses-before-they-fail-the-iris-dataset-b079265c04da
GPU Optimisation: https://medium.com/tikos-tech/400x-performance-a-lightweight-open-source-python-cuda-utility-to-break-vram-barriers-d545e5b6492f
Hyperbolic GPU Cloud: app.hyperbolic.ai.
Coding Agents Conference: https://luma.com/codingagents
~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore
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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Mike on LinkedIn: /mike-oaten/

Timestamps:
[00:00] Regulations as Opportunity
[00:25] Regulation Compliance Fun
[02:49] AI Act Layers Explained
[05:19] Observability in Systems vs ML
[09:05] Risk Transfer in AI
[11:26] LLMs and Model Approval
[14:53] LLMs in Finance
[17:17] Hyperbolic GPU Cloud Ad
[18:16] Stakeholder Alignment and Tech
[22:20] AI in Regulated Environments
[28:55] Autonomous Boat Regulations
[34:20] Data Compliance Mapping
[39:11] Data Capture Strategy
[41:13] EU AI Act Insights
[44:52] Wrap up
[45:45] Join the Coding Agents Conference!

Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, working on AI-powered creator and learning experiences, including intelligent tutoring, content automation, and multilingual localization at scale.
Join us at Coding Agents: The AI Driven Developer Conference - https://luma.com/codingagents
MLOps GPU Guide: ⁠https://go.mlops.community/gpuguide

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// Abstract
“Agent as a product” sounds like hype, until Hotmart turns creators’ content into AI businesses that actually work.

// Bio
Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, where he leads efforts in applied AI, machine learning, and generative technologies to power intelligent experiences for creators and learners. He holds an MSc in Computer Science with a focus on artificial intelligence and is also a co-founder of Data Hackers, a prominent data science and AI community in Brazil. Paulo regularly speaks and publishes on topics spanning data science, ML infrastructure, and AI innovation.

// Related LinksWebsite: paulovasconcellos.com.br
Coding Agent - Virtual Conference: https://home.mlops.community/home/events/coding-agents-virtual

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore
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Sign up for the next meetup: [https://go.mlops.community/register]
MLOps Swag/Merch: [https://shop.mlops.community/]
MLOps GPU Guide: https://go.mlops.community/gpuguide

Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Paulo on LinkedIn: /paulovasconcellos/

Timestamps:
[00:00] Hotmart Data Science Challenges
[02:38] LLMs vs spaCy
[11:38] Use Cases in Production
[19:04] Coding Agents Virtual Conference Announcement!
[29:27] ML to AI Product Shift
[34:49] Tool-Augmented Agent Approach
[38:28] MLOps GPU Guide
[41:24] AI Use Cases at Hotmart
[49:34] Agent Tool Access Explained
[51:04] MLOps Community Gratitude
[53:22] Wrap up