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Harnessing the Power of AI with Data Streaming: An Essential Guide for Business of all Sizes
In the rapidly evolving landscape of digital innovation, businesses are leveraging AI technology to drive internal efficiency. However, successful AI implementation necessitates the incorporation of real-time proprietary business data into the models. Industry insiders are rapidly recognizing the critical role data streaming plays in AI technologies, particularly Generative AI, necessitating businesses to sharpen their focus on efficient data streams.
The Importance of Real-Time Data in AI Operations
In an era where data is the new oil, its management and delivery have major implications on the efficacy of AI-driven processes. From large-scale language models to in-house decision-making algorithms, AI platforms thrive on real-time, up-to-date information. An example worth considering is an airline company needing up-to-the-minute flight data. This information can't be pre-trained into a model and must be supplied in real-time.
Ethics and Practicality in AI Avatars
Increasing comfort with AI integration in daily life due to advancements by key tech companies such as OpenAI, Meta, and Microsoft is paving the way for acceptance and utility of AI avatars. However, it's critical to address the ethical concerns of AI, especially in relation to deep fakes and the need for explicit consent and publication rules when creating custom avatars. The debate around generic stock versus custom avatars is ongoing, yet it's clear that the current cultural readiness leans towards the more practical and beneficial custom avatars.
Understanding the Risks and Requirements of Data Streaming
While introducing such real-time data streams into business models enhances efficiency, it also comes with certain risks, especially for large businesses involved in sensitive sectors like finance and healthcare. Failures can cause significant disruptions, emphasizing the need for resilient and reliable data streaming systems.
Data Governance and Ethical AI Practices
Moreover, good governance is non-negotiable to ensure the data streaming process is smooth, reliable and in compliance with privacy standards. This includes the tracking and filtering of sensitive information from being processed by AI models. Assuring diversity and unbiased data inputs remain crucial across AI systems to maintain ethical AI practices and prevent errors in the outputs.
Traceability and Provenance
Within data governance, understanding the origin and transformation of data, also known as data provenance, is a critical aspect. It assists in troubleshooting and correcting AI-generated outputs, which gets challenging with current large language models.
The Future of Data Streaming and AI in Business
As AI technology evolves, it is anticipated that businesses irrespective of their size will expand their use of AI and incorporate data streaming. Thanks to service providers simplifying the process of integrating data streaming technology with AI, businesses are now empowered to enhance their operational efficiency without the need to comprehend the tech's complexities.
Sector-Specific Services: Catering to Diverse Data Needs
The future also holds the promise of industry-specific data services for sectors like healthcare and finance, aiming to address complex data needs.
The Undeniable Power of Data in AI
In conclusion, the importance of quality data delivery for business applications of AI cannot be overstressed. As the AI landscape continues to change, companies of all sizes must pay heed to the impact of data streaming. Please stay tuned for more insights on data streaming and its intersection with AI through our newsletter.
Topics Covered in This Episode
1. Internal Use of AI
2. Data Integration
3. Risks and Importance of Data Streaming
4. Data Governance and Traceability
5. Future of Data and AI
Podcast Transcript
Jordan Wilson [00:00:16]:
When it comes to generative AI, sometimes it seems like yesterday's data may as well be last year's data. Right? I think for especially enterprise companies and for all of us interacting with them. Right? If you're chatting with a a large language model on a big company's website, you need today's data. No. You need this hour's data. You need this minute's data, and that's where data streaming and the intersection of data streaming and AI become so important. So that's what we're gonna be talking about today, and doing a deep dive into not just what data streaming is, but kind of how it helps power AI that we all need and we all use. Alright.
Jordan Wilson [00:00:59]:
So that's what we're gonna be talking about. I'm excited. If that sounds like something you wanna know, well, you're definitely in the right place. So if you're new here, thank you for joining us. My name is Jordan Wilson, and this is Everyday AI. This is a daily live stream podcast and free daily newsletter helping us all learn and leverage generative AI to grow our companies and careers. So if you haven't already, please go to your everydayai.com. Sign up for the free daily newsletter.
Jordan Wilson [00:01:22]:
We will be recapping today's conversation and a whole lot more, everything you need to know. Speaking of everything you need to know, let's start off as we do every single day with a quick recap of the most important AI news. So NVIDIA has launched an open source AI model called the n v l m Challenging Industry Giants. So in this, NVIDIA's release of NVLM, the family of multimodal language models, is a significant development in the AI landscape as NVIDIA will now be competing with established proprietary models from companies like OpenAI and Google and the open source or open weights leader in meta. So the n v l m d seventy two b, that's that's a mouthful. It showcases exceptional performance across both vision and language tasks, enhancing its capabilities in text only tasks, which is a notable achievement in AI development. So NVIDIA's decision to make the model weights publicly available along with plans to release training code mark a pretty significant shift toward open source practices in a field dominated by closed systems. So benchmarking comparisons early ones for NVIDIA's new model show it performing competitively against leading models such as GPT 4, Claude 3.5, and Llama 3.
Jordan Wilson [00:02:41]:
Alright. Speaking of models, yeah, tons of model news. So Google is working on a reasoning based model similar to OpenAI's o one or strawberry. So according to reports from Bloomberg, Google is making significant strides in the development of artificial intelligence software that mimics human reasoning, a move that intensifies its rivalry with OpenAI and their recently released o1 preview model, what a lot of people refer to as strawberry. So multiple teams at Google are reportedly making progress in AI reasoning software, which excels at solving complex, multi step problems in areas like math and programming. So the company's efforts come in response to OpenAI's recent advancements, particularly the launch of its o one model known internally as strawberry previously as Q Star, which has raised concerns within goop Google DeepMind's team about falling behind. Alright. Last but not least, speaking of OpenAI, they secured, their $6,600,000,000 funding round while reportedly restricting investment in competitors.
Jordan Wilson [00:03:47]:
So OpenAI has successfully closed their $6,600,000,000 funding round from some pretty prominent investors, including Thrive Capital and Tiger Global, marking the largest round of funding ever at 6,600,000,000. So OpenAI has requested, though, according to reports, that investors refrain from funding 5 of its competitors, including Anthropic x AI from, Elon Musk and Company, Safe Super Intelligence, SSI, as well as 2 AI application firms in Perplexity and Glean. So the exclusivity the exclusivity agreements could reshape the VC landscape, concentrating funding around fewer larger companies and potentially stifling innovation among smaller startups. However, this ambitious strategy from OpenAI may open it up and attract some increased regulatory scrutiny and could push competitors to accelerate their innovation efforts to remain relevant in the market. Alright. That's a lot in a very short amount of time. Don't worry. We're gonna have all of that in today's newsletter and a lot more.
Jordan Wilson [00:04:57]:
But today, we're here to talk about data streaming. Like, what the heck is it and why is it so important, for both big enterprise companies and for all of us. Right? Functioning in our daily lives because now it's like we need, you know, all big companies to have data and to have chatbotches like we need them to, you know, have a website. It's kinda the same thing. Right? So, enough of me chatting. I'm excited for today's guest. So let me bring on to the stage. There we have him, Will LaForest, the global field CTO of Confluent.
Jordan Wilson [00:05:26]:
Will, thank you so much for joining the Everyday AI Show.
Will LaForest [00:05:29]:
My pleasure. That's that's a it's not a $1,000,000,000,000, but it's a lot of money.
Jordan Wilson [00:05:34]:
Yeah. My gosh. Right? Yeah. Or or or 7,000,000,000,000. Right? Those those those numbers that That
Will LaForest [00:05:39]:
would have been a little bit of a problem. I knew it was in the trillions. Yeah.
Jordan Wilson [00:05:42]:
It's wild. So let's let's start here. Well, first of all, Will, thank you so much for joining in. You know, for our livestream audience, hey, it looks like, LinkedIn streaming's back. That that's great. So if you have, questions for Will, please get them in now. But let's start at the top, Will. What the heck is data streaming?
Will LaForest [00:05:59]:
Yeah. Yeah. Absolutely. Sometimes people call it event streaming as well. But essentially, it's a it it's a category of data technology that's really, created to handle the continuous flow of data. Right? So as data is generated, changes occur. It's published, and it can be continuously processed, enriched, filtered, and acted on. And so this data or the derivatives of it then can flow to any number of downstream, consumers that are subscribed and at massive scale.
Will LaForest [00:06:33]:
So, like, great example of one of those consumers that we'll be talking about today, I'm sure, is things like a vector database to support RAG architectures, which are really, really, critical. I think the easiest way to explain this this technology to people who are not, like, data nerds, not data infrastructure people, is to is to use an example. Right? So if you think about Uber, for instance, if I'm a driver, you know, I I have this phone and I'm moving around like it's constantly sending these events, this data. It's like I'm Will. I'm in a Mercedes. I'm at this specific location and I may be with the passenger or maybe not. Right. A second later, I'm a new place and a second later, I'm someplace different.
Will LaForest [00:07:18]:
Okay. If I'm a writer, same thing's happening. Like I'm walking around trying to get to the front of my hotel or, you know, walking in the terminal of an airport. And so the most obvious thing is with these constant streams of data is like if I ask for a ride, it needs to match me with someone, you know, Uber and pretty much every rideshare company does the same thing. You need to match riders with drivers. Right? If you don't do it fast enough, like I'm going to get pissed off and say, okay, let me check Lyft or let me just take a taxi. I mean, we all know like consumers now are like super temperamental. I said time is money.
Will LaForest [00:07:52]:
Okay? The other thing I'd say is, like, those streams of data, like, if you're a data driven company and you're applying machine learning, like, that data needs to flow to all these other systems as well. So, like, Uber was did a lot of early work in traditional machine learning. They had this platform called Michelangelo, but, like, that data is flowing there. They're looking at, like, how do I affect surge pricing to, like, squeeze as much money out of us as possible? You know, how do I look at driver behavior, ratings? You know, when do I add more drivers? Like, that all that stuff has to be done on the data. And the faster they can do it, the better the business. So same thing with, I would say, AI. Like, the fresher the data, the better the results.
Jordan Wilson [00:08:34]:
Yeah. And and, Will, I do wanna kind of, for our more nontechnical audience, I wanna put this into perspective because you you you know, you mentioned vector databases and embeddings and and RAG. Right? Retrieval augmentation. Why are these things so important, right? When when people think, oh, you know, I can just slap, you know, use an open AI API or I can use a Google API, throw a chatbot on my company's website and we're good to go, right? But you need that layer of real time data about your company or what your customers need. So can you explain this concept of of rag and and vector and embeddings and and why this is ultimately important when we're talking about generative AI and large language models?
Will LaForest [00:09:15]:
Yeah. I'll I like how you started the the the conversation with saying, like, you know, a data an hour late is super late. I think that was the the time increment we're talking about. I would argue seconds late is too late. So, I mean, a great example is imagine the best way to think about this is you have these amazing, you know, general purpose large language models that are trained to really effectively understand, all these facts and communicate with you, the consumer. But if I'm an airline this is a great example. If I'm an airline industry and I'm providing a chatbot, okay, you it's not possible at the moment to train a model so that it's constantly up to date to the second. So if I log in to some app and say, oh, what's the status of my flight? Well, there's no large language model that was trained a year ago, a month ago, a day ago or even an hour ago.
Will LaForest [00:10:07]:
That's going to have the relevant information. Right? So you need to actually provide that to these models that you were describing, whether it's GPT or WOM or whatever. It doesn't matter. It all works the same way. You need to provide the information so that I can, you know, reason with it. So it will, like, for instance, say, okay. Well, this person is Willa Forrest. He was on this flight.
Will LaForest [00:10:31]:
Here are all the bookings, and this is the status of the airplane. That all has to be provided when you ask the question. Right? So that's that's sort of the the the criticality of real time data, is that businesses that are looking to use these large language models, they have to bring it to the table, if you will, when you ask the questions because they're not trained on all this up to date information.
Jordan Wilson [00:10:55]:
And and we kind of just got straight to the to the heart of today's conversation. But maybe, Will, if you could explain a little bit, like, what the heck does Confluent do? And, you know, we we all assume it's in the data streaming, but maybe just describe real quickly what it is that you all do.
Will LaForest [00:11:10]:
Yeah. So I would say data streaming about 14 years old now. So relatively new in, I would say, data infrastructure world compared to, like, databases and mainframes. They're still out there, people. I know we're this is an AI conversation, but and it was created at by our founders at LinkedIn just to solve this problem. How do we deal with massive scale data and and do it in real time? Okay? Confluence was founded about 10 years ago. And really the idea is how do we take this technology that the these super giant technical companies are all using, the Ubers and Netflixes and LinkedIns of the world, and how we make it possible for mere mortals to use it for their businesses. And so, you know, that's that's really been our mission, to make it as easy as possible to people for people to do things with real time data.
Will LaForest [00:12:01]:
And this has never been, I would say, never been more important than before with generative AI for for, you know, just the everyday person, if you will, the everyday business. So that's really our mission. We've had all these pieces on top of it, but they that's the general idea.
Jordan Wilson [00:12:16]:
Yeah. And so, you you know, obviously, you all work with with enterprise companies, and most enterprise companies have had their kind of data game strong for many decades. Right? And many companies have been using artificial intelligence for many decades. But how does this kind of, you know, even you said quote unquote newer concept of of data streaming, right, been around for a decade and a half. How does this change what's possible for everyone else, right? Even for, you know, medium sized businesses, mid market companies, right? Like can you talk a little bit about just how much data is truly available, and you know, how even those medium sized companies that are growing can start to leverage this data streaming, concept and pair it with AI? Hey. This is Jordan, the host of Everyday AI. I've spent more than a 1000 hours inside ChatGPT, and I'm sharing all of my secrets in our free prime prompt polish ChatGPT course that's only available to loyal listeners like you. Listen to what Lewis, a business owner, said about the PPP course.
AI Insights Expert [00:13:24]:
I can tell you that when I went in, I I understood a little bit about ChatGPT. I understood some of the stuff. I was able to use some of the prompts, but what I discovered going through, Jordan's webinar was that there is so much more I don't understand that ChatGPT can do, and I really should be using it. And, if anything, I got that from the webinar. I would highly recommend this to anybody from beginner to advanced. You will absolutely learn something from this from this experience.
Jordan Wilson [00:13:50]:
Everyone's prompting wrong, and the PPP course fixes that. If you want access, go to podpp.com. Again, that's podpp.com. Sign up for the free course and start putting ChatGPT to work for you.
Will LaForest [00:14:07]:
Yeah. I it's actually a great question because oftentimes when people hear about, like, these, like, massive scale projects going on with data streaming, like, well, clearly, this is not for me because, like, I my data just doesn't change that fast. I don't have that much data. I'm not Uber. Right? But if you're doing generative AI, it all gets back to time. Mhmm. Right? Time is is is absolutely critical to how quickly you can make use of data that's in these databases. So let me just step back.
Will LaForest [00:14:36]:
Traditionally, the way data worked is it's really been done the same way for, I don't know, 5 decades, 6 decades. Take your data, you stuff it into some place and then you ask a question and it gives you back a batch of data. Okay. And then you do something with that data. The and really nothing's changed. I mean, we've added like new ways to do it faster and do more data and make it cheaper. But But it's the same principle. The problem is, is that doing things in these batches immediately introduces this time it takes to actually make use of the data.
Will LaForest [00:15:11]:
So if I'm a let's just say I'm a business and I have a customer set of data that lives in a database. Right? And, you know, I'm constantly updating, oh, I have a new customer. Like, here's his address or whatever. Like, you want that data as soon as it's changed to be available to these downstream systems. And if you use sort of the traditional approaches, you're basically you have to do these batches and usually you do them like on an hourly basis. Every hour, I'm going to take all my customer data and I'm going to move it to my to this vector database I'm using to provide context for my generative AI. So now you move to this model that every time I update my customer user base, you know, that that is immediately sent to my destinations and is immediately enriched and provide context, etcetera. So so that's that's really where I say data streaming helps mid level.
Will LaForest [00:16:06]:
It's not it's not necessarily about the skill. Yes. It can be used for massive scale. So you can grow like I'll I'll like, OpenAI is a customer of ours. They do massive amounts of data. Right? But it's about the time. Mhmm.
Jordan Wilson [00:16:21]:
So So so, Will, you kind of gave us some great examples, and I love those, by way. Right? Like, Uber needs to know whether I'm in the front of my building or the back of my building. Right? You know, we need to know if that Uber has taken a left yet. Right? But, so some great, external use cases or use cases that the everyday person might, you know, need or rely on data streaming. But I like that example that you just gave there. Right? Like, a customer, you know, changes their their address. Right? But I wanna, you know, maybe kind of explain how data streaming is not just, you know, an external thing. Right? Not just for me to know about Uber, but also for internal teams to know.
Jordan Wilson [00:16:59]:
Right? Because I think when we think of large language models, I think, unfortunately, we just think of, oh, I'm a consumer using this on the company's website. So how does data streaming in AI help internal teams work with more real time data? Because I can see in those instances, yeah, last hours data could be very bad to have right now.
Will LaForest [00:17:18]:
Yeah. Well, not only that, but, you know, generally speaking, if you're using a public model, which almost every, you know, medium sized business, they're not training up their own large language models. They typically aren't even using the open source models and trying to operationalize it. You talked about like Llama, etcetera. So those models don't even have your business information. They know nothing about your customers and you don't want them to know about your customers either. Like, you don't want to send that stuff. There's like sensitive information.
Will LaForest [00:17:46]:
So you have to provide that to the model when you ask a question. So, you know, actually, it's kind of interesting because if you think about businesses like start ups, maybe not as much, but medium sized large businesses. I would say the first use case for generative AI is just increasing the efficiency of employees. Right? And and this means, like and I do this all the time. Like, internally, we have all this information. I wanna be able to ask a question rather than, like, search all these different silos of data. Right. And it is and the only way that works is if it if that information is provided to the large language model at the time that I ask the question.
Will LaForest [00:18:31]:
And yeah, these are internal sets of data. This is like customer information, employee information, product information. So that's a very, I would say, a very common use case, especially even for large businesses. If you're a bank or you're a health care and, you know, company, it's actually kind of hard to start using these, generative AI stuff for your customer facing stuff. There's just so much risk. But you can use it for internal information relatively risk free. Right? Because there's always gonna be a human in the loop. Right?
Jordan Wilson [00:19:06]:
Yeah. And that's exactly what I was just getting to. Right? Because we just talked about all this this promise of of data streaming and marrying it with with, you know, generative AI and how that can be so, impactful both for, you know, real front end users and for internal teams. But maybe Will, talk to us a little bit about those those risks and challenges. Because it all sounds great, but what happens when data streaming goes wrong? The data stream never goes wrong. Numbers don't lie. Right?
Will LaForest [00:19:36]:
Well, I do like to say and I don't think you know, again, if you're not a data person, you're not familiar with the category, like, it's not, it's not an exaggeration to say that unless that data streaming one day just woke up and had a bad, bad day, like it didn't drink its coffee, that literally the global financial server system would just fail. Like because like all these banks, the way money transferred, behind all these things, there's data streaming. Like, it's just everywhere. It's ubiquitous. So, like, data streaming is super rock solid. Right? So but, of course, anything can fail. But, like, I think the bigger concern, is, especially if you're a big business where it's always a risk reward. Like, if you're a small business, you don't care that much about potentially what happens if it goes wrong.
Will LaForest [00:20:27]:
You're like, okay. Well, maybe I'll just shutter my doors and just start up a new company. Right? But, like, if if you're a bank and you provide false information to Wall Street or you're or you leak patient information, that's a pretty big deal. And so I think where it could go wrong, I think this is where I one of the things that Confluence does is really important on top of data streaming. Data streaming fundamentally is just about delivering data really, really fast and acting on it. But the question then is if I'm using generative AI and I get some sort of response, How did it achieve that response? How do we ensure the wrong data doesn't get to the model and then get turned sent to the wrong people? Right? Those are really important things. And so that's where this there's this category called data governance, which is all about as this data is being produced, I need to look at the data and make sure that like PII information doesn't get sent through. I need to make sure that, you know, PCI information, that's a banking standard for payments like your personal information, doesn't get through.
Will LaForest [00:21:34]:
So so data governance is important. And the second thing there, I think, is if something does go wrong and this is where, like, all the big generative AI players have to look at this. Medium the medium guys do too. Like, how do I know what went wrong and why? So let's just say I enter in some prompt. I get some crazy ass hallucination, that leads people to make bad business decisions with my customers. How do I if all this data is in real time, it's not in the model. How do I know what that was? So you need this ability to trace the what's called the provenance, the the the source of the data that was made in that decision. And I will say where I think we're we're gonna be going in large like, right now, that doesn't really exist very effectively in the large language models themselves.
Will LaForest [00:22:24]:
Like, essentially, they're trained on the entire Internet. Right? And and it doesn't really track, like, where the pieces of information are coming from. They kinda don't want to, to be honest, because then it opens up a can of worms, which I'm sure you've talked about on podcast. Yeah. So that's another place where it can go wrong. Like, you need to know how these answers were provided.
Jordan Wilson [00:22:43]:
Yeah. And and speaking of that, great great question here from our audience from Cecilia. So she's asking, Will, how do you ensure that data is truly coming from diverse sources so that you are getting complete information? How do you discern who is providing the data or maybe what the is that data actually telling the the the full story?
Will LaForest [00:23:02]:
Yeah. Yeah. I mean, that is a great question, and it is it is a challenge that big challenge we have right now in terms of applying generative AI in an ethical, manner. So, I mean, first of all, I wish I wish I had the magic incantation to to solve that problem. I think I'll I'm gonna speak to how do you know you're getting the complete information and who is providing that data. So, again, I think the the data that's provided to the large language models, these generative AI models, you can actually track what that is. So so this is basically I'll just simplify the way these things typically work. Someone enters a prompt.
Will LaForest [00:23:43]:
You take that prompt. You get a bunch of other data. You throw it on top of the prompt. Then you send it off to the model and you get a response. Right? All that extra data that you're that you're sending to the model, you can track that and you can know what source it's from and you can know when it was created. All that is what's called lineage information. So if to have ethical generative AI, we have to do a very good job of tracking that to ensure that you're not getting biased results and that when you do get biased results, they can figure out what the problem is. So hopefully, that kind of answers the question.
Jordan Wilson [00:24:22]:
No. Yeah. It it it does. Absolutely. So, you know, one thing that I I kinda wanna, you know, shift toward, and I I never put gas really in the hot seat or ask you to tell the future, but, you know, one thing I'm always talking, talking about and and wondering about is just this availability of data. And and even for, you know, smaller businesses, which we kind of already referenced, you know, but, you know, 5 years ago, small businesses weren't thinking about using AI until generative AI came along and kind of democratized that. Yeah. You know, might there be a a similar, you know, realization when it comes to even data streaming.
Jordan Wilson [00:25:00]:
Right? So where do you kind of see your industry headed as this generative AI landscape is changing so so quickly, which brings more more and more people in using models and wanting data. So where do you see it headed?
Will LaForest [00:25:16]:
I mean, I think what there's a couple things we're gonna see. One is, to be honest, like, the the average company is probably not gonna be directly be dealing with data streaming themselves because data data streaming has become so, so, I would say, inextricably linked with how generative AI is being done, that you're going to be using a service that makes it incredibly easy for you to do this without ever having to touch data streaming. Underneath the covers like that service will be doing for you. I mean, there's lots of great examples. Let's take something like a Notion AI. I don't know if your listeners and probably some of them are familiar. Like underneath the covers, they're using data streaming. If I'm using Notion for productivity reasons, I don't need to know about how they're using data streaming.
Will LaForest [00:26:02]:
So so that's one thing. You're just gonna see these services do a much better job at connecting to your data sources and doing this all for you. So you don't have to do this crazy rag vector database stuff like this. Even that is too much work. It's a lot easier than it used to be, like you said, like tremendously. But that's a lot more work. Second thing I think is we're going to see a lot more industry specific integrations, training services take place. So if I'm a health care company, there's gonna be companies that specialize in how do I deal with patient information.
Will LaForest [00:26:34]:
That's a very hard problem right now. You You know what I mean? So I think we're gonna see a lot of industrialization, energy, health care, financial services happening already, but it's gonna become mainstream.
Jordan Wilson [00:26:45]:
Alright. So, Will, we've we've covered so much in a very short amount of time. Right? From from data privacy and and governance to how data streaming is is changing both internal operations, external expectations. But as we wrap up here, maybe what's your one most important piece of advice for business leaders out there, you know, who are really wanting to maximize the power of AI with data streaming?
Will LaForest [00:27:12]:
I mean, this is so cliche now, but it's so true. Let's just just remember this. Garbage in, garbage out. Like, large language models are great, but if you wanna use it for your business, you will have to have high quality, well governed data that's always up to date. Because as you said to start the show, an hour late is far too late. Right? You're just gonna piss off your customers. Right? I don't even wanna see data a second late, to be honest. Like, I get I get crack I get cranky.
Will LaForest [00:27:41]:
Right? So so making sure you get high quality data that's fresh and up to date is absolutely critical. So, like, the first thing in any major, if you wanna make use of a generated volume, it's a data problem. It's always a data problem first.
Jordan Wilson [00:27:56]:
Alright. Well, this was I think whether you're a data geek or just trying to better understand data streaming and how it how it impacts us all, I think today's conversation was an especially important one. So thank you so much for taking time out of your day to join the Everyday AI Show. My pleasure. Alright. And hey, everyone. Yeah. We just all got, like, some kind of degree in in data streaming.
Jordan Wilson [00:28:21]:
That was great. If you weren't taking notes as quickly as I was in the background, if you hear, you know, pound pound pound on the keyboard, that's me getting our newsletter ready for you. So yeah, we covered a lot, and it's going to be a lot more, some of the most important takeaways. So if you haven't already, please make sure to go sign up at your everydayai.com. If this show was helpful, tell someone about it. Yeah. I know everyday AI might be your little secret to make you the smartest person in AI at your company, but share the wealth, please. If this is helpful, if you're listening on the podcast, leave us a rating, subscribe to the podcast, and go to your everydayai.com.
Jordan Wilson [00:28:54]:
Sign up for that free daily newsletter, and we'll see you back tomorrow and every day for more everyday AI. Thanks y'all.