In this episode, we dive deep into the emerging world of building and training small language models. We’ll discuss the benefits, risks, and challenges companies face as they work to create more targeted and efficient AI models. From managing hardware and power requirements to ensuring data privacy and governance, we’ll cover the key considerations for enterprises looking to leverage the power of small language models. Join us as we unpack this fascinating topic and consider the implications for the future of AI and infrastructure operations.
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.
We use ChatGPT to live create DevOps, automation, Ansible, TerraForm, Python, and interact with different clouds to get advice on how to set up clouds.
This discussion includes a screen share session, so if you’re listening to this audio there will be times when we are talking about something you can’t see but I do make a point of working to explain what we’re doing. There’s also a video of the screen share session if you prefer.
We dig into a topic written about by Eric Norlin or SK ventures about technical debt and AI. In this episode, we discuss the consequences of generative AI could be radically transforming the way in which we generate code and deal with code that has been generated in technical debt.
We explore some fascinating concepts about how fast we can iterate, how we change the dynamics of building software, building automation, and the expertise required to architect systems. This leads pretty far down in the path towards disruptive thinking, and how this could reshape the entire industry.
In a discussion on the Cloud 2030 podcast, CEO and co-founder of RackN, Rob Hirschfeld, highlighted the changing landscape of expertise in emerging technologies like AI. With the cost to build and iterate dropping significantly, expertise is no longer primarily applied during the building process, but integrated into design and testing sequences. The advent of generative AI has the potential to revolutionize how we design and build automation, software code, and technical systems, necessitating a redefinition of expertise in this rapidly evolving field.
NOTE: This is Rob’s Gluecon topic on 5/24. Save $300 if you register with speaker300 at www.gluecon.com
We dive into the question of whether or not generative AI can be used to productively change DevOps automation and the control of infrastructure.
We’ve discussed the closed loop side of using AI to manage infrastructure in the past, but this episode we really dive into the idea of creating automation and using generative AI.
How does AI chat and generative AI have the potential to disrupt everything we know about social media? Today we talk Twitter versus mastodon.
We spend most of our time talking about the power, influence and simple use cases for generative AI.
Is this going to break Mastodon, Twitter and other forms of social media? We have a pretty compelling conversation about that, too.
If you’re a fan of Mastodon and Twitter, jump forward to about 30 minutes in when we really start getting down to that topic. Stay tuned for our agenda as a bonus extra in the back half of the podcast.
What is generative AI and what are people now just generically calling ChatGPT?
We put these things in a technical frame, meaning can we use generative AI to improve our programming, testing or automation? What does it take to use these concepts in ways that iteratively improve IT infrastructures.
We review the state of chat, ChatGPT, AI infrastructure and things like that.
In a discussion on the DevOps Lunch and Learn podcast, Rob Hirschfeld, CEO of RackN, explores the complexities of generative AI and its impact on coding and automation. Hirschfeld raises questions about trust in generative AI models, emphasizing the need to understand how they are trained, updated, and refined to eliminate errors. He highlights the importance of creating reliable training sets to ensure the technology’s applications, focusing on enhancing system resilience and maintainability.
We do a 2022 retrospective slash 2023 prediction episode – a sort of end of the year classic for us, except our predictions and look ahead are different from most people’s.
We’re looking at some broader trends around software, build materials, impact of GPT (which will be a future episode), edge Technology, cloud adoption, security, faults and failures.
Not your garden variety look back look ahead type of show.
In the Cloud 2030 Podcast’s December 15th retrospective on 2022, Rob Hirschfeld discusses the significant momentum and necessity behind the adoption of Software Bill of Materials (SBoMs) for describing deployed software in a structured, programmatic manner. While emphasizing the immediate security and resilience benefits, Hirschfeld also highlights the broader economic advantages of SBoMs, drawing parallels with their use in manufacturing to provide controls and structure for normal activities. The conversation delves into various insights about the industry’s direction in 2023, making it a valuable episode beyond the focus on SBoMs. Those interested in these discussions are encouraged to check out the full episode on the2030.cloud and participate in the ongoing conversations.
