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15 results for “ai deployment”
that my agent should not do, and let me build a list of datasets so that I'm going to do well on those. And in terms of production monitoring, what you are doing doing there is, you're deploying your application, and then you're having this some sort
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from transcript34:28 – 35:45
of what is your what is it that you're trying to build? Like, you're trying to build a reliable application for their customers that's not going to do a bad thing. Like, it's always going to do the right thing. Or if it is doing a wrong thing, you ar
35:49 / 37:04
from transcript35:49 – 37:04
If you take you use the Databricks and Snowflake example. If you look at the companies that use that software, those companies generate enormous revenues and enormous margins, and these products are in critical production workflows that underlie thos
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from transcript32:57 – 34:04
Prompts could be a sentence or an entire archive. Outputs can end instantly or stretch on indefinitely. Thousands of users can arrive at once, each making incompatible demands on the same hardware. And all of this has to happen in real time on GPUs t
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from transcript1:28 – 2:34
So developers actually had their ChatCha BT moment at least twelve to eighteen months months before everybody had their ChatChibuty moment on the consumer side. And so very similarly, Cursor, Windsurf, Replit, these products are now having their AI a
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from transcript5:59 – 7:02
To make this even more real, is there an example of a product or company that is a really good example of this of this doing this well, living this kind of loop life? I think most companies that we're seeing in the space from an AI perspective are do
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from transcript14:49 – 15:53
to the liability management transactions we talked about. Mhmm. So perhaps folks are thinking that that will precipitate. Perhaps it's a reaction to some of the maturity walls that folks understand. Or perhaps it's some of what I was saying in the op
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from transcript33:44 – 34:50
stay functional and keep delivering value? I think the end goal is for us to to disappear and for the the the AI products and the AI platforms to be fully usable by the business users. Today, this is not the case. Today, this is still too complex. An
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from transcript18:44 – 19:49
the same kind of loop that we talked about becomes increasingly important, like fine tuning and self healing, observability, really good evals, all of that. I mean, the good news is that there are systems that manage this for billions of people today
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from transcript24:43 – 25:53
shared things they shouldn't have shared, customer data, marketing plans, financial data. So the first layer was, how do we make sure our employees are using somebody else's AI in the form of some chatbot, ChatGPT, Gemini, so forth and so on, safely.
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from transcript1:29 – 2:33
insight into the quality, durability of this revenue. There's not a single good example that we can find of sustained positive margin expansion and impact of AI inside of a true corporate enterprise that is not right now a small test. There's not. So
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from transcript32:14 – 33:15
All investing involves risk of loss. See complete disclosures at public dot com slash disclosures. So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrat
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from transcript30:43 – 31:45
During my sales career, probably the number one thing that used to break my back was that the underlying software with, like, Salesforce CPQ and others, just to, like, create a quote, get it approved is horrific. Like, you think if you think you've s
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cited, oh, they're deploying but not getting value. What's the reality as you're seeing it? Because you're embedded inside these enterprises, and and really trying to help them on prem, it seems like, close that gap between this is a pilot, a project
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from transcript15:45 – 17:11
If I were to put it, in baseball terms, I would say we're probably in the second inning. And don't ask anymore about baseball because all I know is the eight innings. I know what I don't know. But the, I think we're in the second inning. What we have
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from transcript10:38 – 11:47
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