In this episode, we discuss the rising cost of using AI and how usage-based pricing, model changes, and capacity limits are affecting daily work as AI moves from experimentation into operational use. We also talk about multi-model workflows, hybrid infrastructure, and examples of using hosted models alongside open models locally for tasks such as writing and named entity resolution. We get into the need for enterprises to run their own AI infrastructure, including questions around GPU pooling, routing, reservation, data sovereignty, and service levels.
Tag Archives: Data
MCP Agents and Context
In this episode, we continue our journey even deeper into how agentic vibe coding and other AI-based automation. This time we focus on Model Control Protocol (MCP) and its application in our bare metal automation solution, Digital Rebar. We examine deterministic versus stochastic AI approaches and the importance of reliable system integration without competing with other agentic systems. We highlight MCP’s role in streamlining interactions across data sources, with a focus on practical applications in finance and infrastructure resilience. The episode ends with a preview of future conversations on user experience transformation in infrastructure operations. Enjoy!
Transcript here: otter.ai/u/LmtQ9QAc79izN0PacE…=transcript&tab=chat
Lean In Data Science
How do we apply the principles of lean to data science and data engineering? We discuss this broadly into using AI and machine learning more generally.
This is a topic that we had discussed over the summer and wanted to come back to six months later because so much has changed and transformed in the industry. What does agile lean process control look like in an infrastructure automation platform? How can we make these very difficult and challenging components of data and data management, more agile, more lean?
I think you will get a lot out of this conversation considering our current hypercharged AI ml and LM environment.
Transcript: otter.ai/u/1ZuALgSXcPw-bIf2GO…?utm_source=copy_url
DALL-E Prompt: please create a picture of a very large truck stuck under a low bridge. please label the truck as ai and the bridge as lean
Identity vs Privacy? Trade-offs required?
How can digital identity be used to build better trust and systems in our daily transactions? There are really significant challenges and consequences to having a national guaranteed identity – a single identity provider.
Knowing who you’re interacting with, in every form, in every situation is not as simple as you might think. There’s a lot of analogues to physical identity that are worth considering.
What would it mean for us to not have privacy? Does identity mean we don’t have privacy in our interactions? Who can we trust and what authority do they have?
Transcript: otter.ai/u/o_43fyGjxu24Ur5rpz…?utm_source=copy_url
Image by Dall-e prompte: a cartoon like image of a humanoid robot looking into a mirror and seeing a masked pirate version of itself
Data Darkages – do LLMs drive paywalls?
A coming Data Darkage is on its way, where we’re watching Reddit, Twitter and other companies take what used to be publicly available information and put it behind a paywall or gate.
Because of the way large language models are using this data and the value of the data, we are expecting to see that trend accelerate. This will have profound implications for how we think of, share, and use data in the coming years.
Transcript: otter.ai/u/e1XCyhSa9V81bgMpbo…?utm_source=copy_url
Photo by Pollianna Bonnett: www.pexels.com/photo/young-brune…e-chair-17687131/
Data + Operations = DataOps
We talk about DataOps, but if you’re expecting this to be DevOps for data – you are mistaken. Today we talk about engineering data through the idea of data stewardship or how you manage and control the data.
Beyond permissions and access into the costs and how things are stewarded, how logs are handled, who controls how much access and how quickly people have access, where you’re putting the data to improve its effectiveness, and more. There is so much going on above the infrastructure, but below that actual analytics, this conversation will open your mind to a whole layer of challenges related to governing and managing data.
Transcript: otter.ai/u/SDaRomv41JtQYM7a6C…?utm_source=copy_url
Photo by Taryn Elliott: www.pexels.com/photo/person-hold…g-on-top-4099125/
Domains And Access For Metadata
What actually is used to describe the provenance and information that comes with our data? Today we discuss metadata and the governance, security, hint, domains, date that accompany data in ways that in some senses are more important than data.
How can we move, change and transform data? We had a really robust conversation about how access to data is so critical in actually understanding how data is used.
This is a topic we struggle with: figuring out the naming, how things work, and the context. More than anything else, the context makes things challenging. As you listen to this, think through how challenging it is to define data, data governance, and using data effectively.
Transcript: otter.ai/u/VE2u_jTwG_h4z9ewfI…?utm_source=copy_url
Image: www.pexels.com/photo/cheerful-bl…hoolkids-5905918/
Strengthening Security’s Weakest Link
How do you deal with the weakest link in security?
Today we talk through how we can secure systems, all the way from what technical processes put in place to the people involved to legal enforcement, and who pays the price when data is compromised? There’s a lot to digest here that comes back to thoughtful ways in which we can deal with the weakest link in the systems.
How do we create robust security models?
Transcript: otter.ai/u/mkup2hKSzyP0Pkpxkk…?utm_source=copy_url
Image: www.pexels.com/photo/brown-thread-2072872/
Data Cartels Book Discussion
The book Data Cartels by Sarah Lambda serves as a starting point for our discussion today. www.amazon.com/Data-Cartels-Comp…ion/dp/1503633713
A dense and thoughtful book, it is straight up the alley of the type of conversations of the2030.cloud has. Our analysis of the book and the challenges it provides – the data compliance governance, the legality, the threat, and broader implications of what Dr. Lambda lays out – are all really important.
Today’s podcast is understandable and interesting without having had to go through and read the book, but I still recommend that you do.
Transcript: otter.ai/u/T5CJzO8pMrpGnLVGo4…?utm_source=copy_url
Image: www.pexels.com/photo/lady-justic…-a-gavel-6077123/
Metadata Architecture
Every time we look at data analytics and data systems, the idea of having a way to manage control and explain the data is actually as important as the data itself. This episode is all about metadata, specifically, metadata related to data analytics analysis, Big Data computation, sort of the data lake metadata problem.
Today we discuss the challenges of data management, but also the potential of understanding so much more about how data is used. If you are an IT professional or a data professional, you will find this conversation about how we’re going to draw inferences, manage and control all of that data that were collected fascinating.
Transcript: otter.ai/u/dFF6CfBYOMR3QFydae…?utm_source=copy_url
Image: www.pexels.com/photo/woman-stand…-mirrors-6208385/