Here is Ren talking about demystifying machine learning. So please give her a warm applause. Thank you. Hi, everybody. All right. So yeah, I wanted to talk about machine learning, since it's such a hot topic these days everywhere. Just a small thing about me. I've done a lot of undergrad research. I only graduated a couple of years ago, but I did a lot of undergrad research in machine learning, especially in image classification. So my background comes from that. And I use machine learning techniques. Definitely in my work right now, but I'm an infrastructure engineer. And you can pretty much apply machine learning to anything that you're doing. It's really a tool. So anyway, we're going to start. So I was inspired to make this talk because I was in this Lyft line in the Bay Area one day. And there was a machine learning engineer from, I guess they were working on like Android platform.  That's kind of like literally what this person said. And then kind of went off on a rant about how like it doesn't do what we want it to do, and yada yada. And then I guess I was just sitting there and I was like, for people that don't quite know a lot about machine learning yet, I feel like that could be not such a great summary for what machine learning can and can't do and what it should be used for.  But I figured it can't hurt to know a little bit more and get a little bit educated about what machine learning is. I'm not talking about AI. So like machine learning and AI, they're kind of like synonymously used in colloquial terms, but I'm only talking about machine learning and some of the techniques. And hopefully you guys can learn one thing or two from it. And yeah, cool. So yeah, like I said, this talk is for anybody else. I also think that machine learning is a little frustrating. It doesn't quite do what we want it to do yet. And I wanted to mystify that. Yeah, so let's get right to it. So when I heard that statement in the car from the Android engineer saying, machine learning is stupid, right? And I get that he was kind of like really, I think, frustrated about it. But the way I deconstructed that statement is basically why don't machines or machine learning have same kind of understanding, recognition, intuition, kind of automated learning? Like all these things that kind of, I guess we expect from machine learning because of all these sci-fi examples like Jarvis and Hall and all that stuff that kind of like do everything on their own and it's kind of magical, right? And so that's kind of how I understood it. And I was just also like thinking, I think the frustration is that like why don't machines also have like learning and execution skills like humans have right now, if not any better? And I guess kind of like expecting machine learning to be some sort of like an accelerated learning version than what humans can do. So let's kind of demystify that and kind of see where we're at in terms of reality with machine learning. So yeah, who here actually knows machine learning techniques, worked with it before a little bit, a lot, experts here? Okay, whatever, it doesn't matter. So this talk, I'm kind of assuming, I wrote this talk for more of a generalized audience. So I didn't really wanna get heavy into dragons, I didn't wanna like get into like statistical modeling and statistical algorithms and things that people are just gonna sleep on, right? But I do wanna go over like really, really simple examples of how you can build a simple neural network and use that example to kind of build on top of that and kind of go into comparisons of how machine learning would tackle some problems versus how a human would. Anyway, yeah, so I'm not gonna get into like super jargony stuff. Yeah, so machine learning is heavily rooted in statistical analysis, mathematical models, concepts, but they're also deeply rooted, especially in neural networks, they're deeply rooted in biological evidence for learning and how the brain, the biological, I guess, the structure of how the brain works. And in case you feel like this about math, I'm gonna just kind of leave that part out, yeah. And then so machine learning, it has a name like learning in it, but it really has to do with how do we build models that can answer questions? So let's say, yeah, not actually, yeah. So let's say you have a certain question you wanna answer, that could be like classifying an image or figure out what is the best next move to make in a game, right? Those are kind of questions that you wanna ask and you build the model supposed to give you the answer to that in a preferably really fast, faster than how humans can. And generally, when you're building models, you kind of go through this, this is a pipeline that you would go through normally in a very high level format. So you first start with some training data of what the question is and what the answers might look like. So for example, if I had a bunch of images of cats, that would be, the image itself is my question and then the answer might be it's a cat in the image, right? So you have a bunch of kind of like what we call the labeled data like that and that's usually used as training data so that you can start building the model that you're looking for, right? And the models are usually statistical algorithms and you test the model by basically seeing given this training data, does it actually give me the answer, does the algorithm give me the answer that I'm looking for? So if you give it a bunch of cat images, does it say, does it label each image as cat, right? For example, and if it doesn't, then you know, you correct it and you tune and you kind of go through this iterative process, a feedback loop of sorts and you kind of do that over and over and over and over again until you know, your accuracy, general accuracy kind of goes up, right? And so that's like the very high level of how any like researcher or anybody using machine learning would go about training a model. And I kind of wanted to go over what are like the two big types of models that are out there. These are not the only ones, but these are the main two types. So there's machine learning models can be split into either supervised learning or unsupervised learning. So what that would mean is supervised learning, you basically give it some data knowing what the answer already is. So the cat example that I keep talking about is a classic example of that where you know exactly the image that you just given is supposed to be a cat and you want the algorithm or the model to give you that same answer. And these are vastly I guess easier to test because you already know what the answer is supposed to be. Then the unsupervised learning model which you use these models or build these models to discover patterns that you yourself humans can't see, right? So there are algorithms called like K-means clustering which is supposed to basically find given the plop of data, give me what are the clusters of data that I can't see as a human by myself, right? But you don't really know when you just have that data what it might look like until you have run something like K-means clustering algorithm. So it's a little bit harder to test just because you don't really know exactly what the answers you're looking for but you're hoping the model or the algorithm to basically chuck through a lot of data fast and give you some sort of answer, right? So in terms of example purposes I wanted to kind of hone in on one example that uses supervised learning model just because it's a little bit easier to relate to. But yeah, and then there's reinforcement learning models there's bunch of other stuff that, you know if you're curious, you're welcome to go browse. So yeah, there's a lot of algorithms don't try to read that because my whole purpose of this is like I wanna just find an image online that has like a lot of these really famous known algorithms that are typically used in machine learning and kind of wanna say there's a lot of them, right? And there's no by no means am I gonna go into even like some of them but I want to just focus on you can't read that, it's fine. So this says here neural networks we're gonna go into kind of what perceptrons are and kind of how they are the building blocks of building a simple neural network. So there's simple neural network there's a deep learning neural network there's a bunch of types of neural network but the simpler one is easier to relate to and easier for to understand right now, cool. So perceptrons, there's the simplest building block in the simple neural network. It's a linear binary classifier that really gives zero or one at the end. So I promise this is literally the only equation I'm gonna put up on my slides today. So this is what we would call like an activation function dragon here, activation function. It really just means like you have certain inputs. So actually let me just go over this right quickly right now. So this is saying the activation function is saying I want the output to be one if some weighted value dot product of their variable X that you're looking for plus B is a bias. If that is greater than zero then I want the output to be one. So that really just is saying like given some variable looking for and given some sort of weight which weight just basically means how important this variable is and the weight will come, you guys will understand it a little bit more when I get to the next slide. So it gives you basically how important a variable is plus bias. And if you just ignore all this equation right here if you see this diagram right here this activation function literally is trying to split a problem space into two different quadrants, right? And this is a very simple one so it really only does zero one of given one feature, right? And I keep calling this variable in ML language we call it the features. Features basically means the things that we care about that help us determine an answer. So in this case this might be like the X might be hey, the question is basically like is the color blue or red on the dot, right? And then the model in this question would be a this line which is represented by, by the way this is not it, but an activation function that basically says oh okay is the color blue or red? I mean that's pretty simple. This is basically a linear line that cuts the problem space, right? And so given that, so this is a great example where we have only like one feature we're looking at which is in this case like for example a color. You can expand the perceptron to have more than one feature that it looks at and it has a lot more weights. This looks a little bit complicated but all it is is like there is an activation function you have a bunch of inputs. So it's a vector, right? It's a vector of features and where each feature is represented with a weight of how important it is. So to translate like a mathematical equation like this I would think of it as let's say if my question was would I buy some model of a car, right? And my inputs might be like price and color and size and yadda yadda, right? And then those would be literally my inputs, right? And then my weight would be for me I care a lot about price of the car so I would give a lot more weight to the price feature than any other things like color or whatever else I'm looking at, right? And then that would at the end give me zero one. That's the whole point of perceptron. It gives you zero or one. So I put this slide cause you know how I was saying a lot of machine learning is based on biological evidence for learning or like biological structures. Perceptrons are literally meant to represent how neurons will work. So you have a picture of a neuron right here there's dendrites which receive information from other neurons which is literally synonymous to a bunch of inputs in the perceptron. I mean the image even looks really, really similar to one another. And then you got some output that comes out and these axon terminals right here connect to other neurons, right? And then that's literally what also perceptron does. It takes a bunch of input, calculates it and then gives us zero one and that gets transmitted to other neurons or in this case, other perceptrons. So yeah, one perceptron can be really synonymous to well how a neuron will work. So if you put a lot of these neurons together that's actually if you just have a lot of these neurons together in many different layers that's the simplest version of a neural network you got. So here, by the way, all these circles are perceptrons. So you got a bunch of perceptrons at the first layer that's considered the input layer which usually that only means that these are the perceptrons that take the raw data you give it, that might be color, bunch of numbers like the car example I was using might be prices of things, it might be size of things. And then usually neural networks are defined by the fact that you have a lot of different abstract layers in between that might compute on the output of the first input layer and all the abstract layers in between. Until you get to the output layer that finally ultimately gives you a bunch of variables that you can use as an answer to your question. So, but the most important thing here is that neural network, the simple neural network you just put a lot of perceptrons together in many different abstract layers and that's how you get a neural network. And going back to the whole biological example of things does it look any kind of like similar because the way we have abstract layers of perceptrons to create a neural net, simple neural network is exactly almost like how brains work also. Brains have, they have neurons that process the raw input that a bunch of other neurons that keep on processing after that, after that, until we, I might see an apple and think it's tasty. But there's a bunch of neurons that are abstracting the information that I've seen in apple until I get to that point, right? So, right, right, so I was talking about apples right now. So, given that kind of like super fast overview of how like a neural network, simple neural network might work and what like perceptrons might be. So, I kind of wanted to apply that to our original problem was how are machine learning different than how like humans learn. Or what are like the challenges that I guess impacted in a way that it's not as, I guess, as robust of a learning as humans do yet, yet is the key word there, because we're getting there. So, I wanna take this simple example. Let's say you want to classify an image of an apple. How would a machine learning approach it versus how any one of you might approach it? So, if a machine were to look at this, so let's say if you were to try to classify this image, some of the features, oh, I guess it's, that's really hard to see, huh? I'll read it off. So, some of the features that might be used to identify this object, which is an apple, in the image is, for example, size of the object. The color gradients, so there's a lot of reds here, some greens there that's not really part of an apple position. So, where the apple is in context of everything else. And it might also look at stuff like, as it's looking at the color gradients on the image, like anomalies like white spots, where there's like a shine on the surface right here, that all shows up when you're calculating the color gradients across the object you're looking at. And I also put up here, like, so those might be the features you're looking at, and there might be some other anomalies that might throw off, the algorithm from thinking this is an apple, which is like the leaf. And so, these are kind of like the features that machine learning might use to create the model that it wants to use to identify this object as an apple. And a lot of the times when you're building models like this, you run into many different problems. So like, for example, if you have, so I only listed very few features here, but once you expand out your feature set, which you normally, usually, more like always have to do when you really want accurate results and classifications, you might end up with too many features, and you might end up with features that really don't really matter in the answer at the end. For example, if I was already looking at color gradients here, I might not really care about the fact, the feature that only one feature that looks at like, whether the pixel's red or something like that. Something very specific, right? So there are a lot of features that can bias or hinder or be completely redundant to the models that you're creating. And then, and if you keep on adding more features to your model, that's yet another permutation of the feature that you need to calculate, which just basically expands out your computation space. So unless you have a lot and lots and lots of computers to just chug out, chug out, chug out numbers, right? You might end up with a model that has like 100 different features. It takes like two hours to calculate for one image, just because there's just so many features, right? So there's like a fine balance between what are the right features to use? And you might end up with too many features to calculate. You might end up with features that you don't really need, and how to do this, how to calculate fast, right? Versus, so I don't want to compare that to like, if I was looking at this apple feature picture, these are some of the things I might think of. And one of the things I really want you guys to notice is in this previous image, it was, had to do a lot with like, you look at the apple, the machines look at the apple, these features are related to this physical properties, right? Like the color or the size of this position, et cetera. And then the real distinction I really want to make here was if a human were to look at the apple picture, they also look at the physical properties of it, right? Like the color, size, and everything that the machine, the machine learning model might look at. But you also have all these other contextual data, because for example, if I'm looking at an apple, I might think, oh, that last time I ate an apple was really juicy, or like, I might think, oh yeah, this is a red apple, I've seen green apples before, or like, I might think somebody else that I know really likes apples. So like, it's not just the physical qualities that you're looking at, right? But you also think of many other, like, you draw from memories, you draw from other examples of apples that you've seen. So if you kind of, if you can kind of think of this as like, you have all these physical features that you look at, but as a human, all this other contextual data that you can draw information from, right? Which machine learning doesn't have right now, like the machine learning right now, as I've shown you, it only really looked at the physical features, and then try to figure out what it was in that image, right? And then as a human, you have so much other contextual data that altogether comes together that you don't even notice. It's like all subconsciously processed, right? That altogether helps you to learn something as an apple. So to kind of hone in that a little bit, this comparison I want to make was basically the difference in machine learning and a human in the power of basically the feature dimensionality and contextual data that you have. So one other really big comparison I really want to make was if you remember in a couple of slides ago where I showed you guys how you would use a training data set to start building a model. A lot of the problems, and I've definitely noticed personally because I've worked on image classification, is it's difficult to get a very large as well as varied training set data. So for example, let's say in the context of an apple, you might be lucky if you get something like 10,000 or 100,000 labeled images and examples of apples that you can use to train your model, right? And like literally 10,000 is like a lot if you're looking at images or objects, or I can only really speak for image classification because that's the work that I've done. And if you're thinking about it, it's like 10,000 is not that many examples to look at something. Something as simple as apples, sure, that might work, but if you get into more complex problems, right? Like for example, how do you solve like, I'm gonna go into the AlphaGo example in a bit, but like if you're trying to compute for like a very complex game, right? There's probably, you're gonna need a lot of data and examples to make sure your model actually is accurate, right? So this is one of the biggest tasks in when building a model. Like you can't get enough of the training model, training set to actually make your model accurate. And then if you think about it, if you compare it to a human being, like how many apples do you think you've seen ever since you were born, right? Like probably a lot and you don't even have to eat an apple or see an apple yourself to count it as an example, right? People talk about apples, other people might eat apples and you have all this like even indirect examples of apples that you might be exposed to that it's not like just like looking at apples and oh, that's an apple, right? You might see a bunch of different colors of apples. So there's a lot of data that you're just always subconsciously kind of absorbing in that FARC surpasses the label data you can give to a model to train it. So there's actually a MIT paper on this about why, I linked it here, why humans right now are far ahead in learning in terms of, far ahead in learning than machines right now. Because we seriously just have so much contextual data that we get all the time, right? About everything. You don't even notice it, but you are always absorbing some sort of example about any of the objects or people or the language or whatever that you're interacting with that the machine learning just can't, right now at this moment, can't keep up with because you're artificially creating data sets to feed into a model to train it, right? So there's that kind of, sorry, am I running out of time?  Okay, yeah, anyway, so there's that. I'll go through it really fast. So I put some summary of comparison here. You guys can take a look. I was gonna go into detail, but that's fine. So yeah, I've given a couple of examples how machine learning is, in some ways have a lot more challenges than humans do. A lot of them is like, obviously access to a ton of data, subconscious data that we always process. Feature sets, what are the things, what are the features that you can reasonably calculate? For example, like Google has about one million servers. They're able to use all that computation power to power all the AI provided services versus how many of us here have one million servers and you can do stuff with that, right? And if you compare that to the human brain, you got one billion neurons. It's like all provided infrastructure to compute whatever you're looking at, right? Sorry, I'm severely running out of time, but let's see if I can speed this through. Yeah, so if you have one thing to take away from this talk, machine learning learns very differently. It has a lot of different challenges and especially it doesn't have access to all the data and then the learning capabilities that we already have. So until we get to that, you might not be able to compare it directly to a human learning. I'll put the slides up if you guys wanna take a look. Oh, this slide, please. You might wanna take a look at this paper really quick because you'll learn some really cool stuff about how neural networks see the world that might be different from what you think. All right, thank you.


Hi everyone, as tag runtime maintainers we're used to not having like a huge turn on. So this time around we got most speakers just to keep ourselves company in case we don't feel lonely. So over to you, Alexander. All right let's start, welcome. I know it's long day, we are like last in a row of sessions for today. And we would like to talk about the tag runtime, so like very interesting and very wide range of things what we are doing. And on stage we have like myself, Alexander Kanievsky, Rajas, Ricardo and Daniel. All of us are part of tag runtime but we are doing different things and we are going to speak about parts which we are mostly involved on. Very briefly, overview, few things from our working groups and then how to get involved with all of those. Let's start with tag runtime. You probably know like how the scope of the six in Kubernetes are done. You've probably seen some of the tags in CNCF which also scope it to particular subject. For us we are, from one hand we are standard tag. So like we have like Slack, we have regular meetings, we have like chairs, we have technical leads, all the standard. What is different for us is the scope of the projects what we are covering. And what you can see from the statement is like it says the whole area, the whole range of work loads, how to run and get it run not only on Kubernetes but on CNCF tags. Just to glance what it can be about, like you see like different scale of the projects. You can see something like monstroses like Kubernetes, you can see something small like Cryo container, you can see very different from a perspective like confidential containers. You can see something like QBH, ACRI and so on. All of this can be summarized in few particular areas what we are looking at. Like general orchestration, like Kubernetes, VMs and runtimes. So again like Cryo container, Decata and similar edge, like all the projects related to it. Again what is happening on your host, like specialized OS images like Flatcar, Talos, many other projects which is specific to cloud providers or different companies which is also part of the CNCF landscape. AI, machine learning workloads, we all have our interest in those. So far it's like some working groups but looking at the hype what we currently have around all of those, maybe we will soon see the tag AI or something like that. And with that huge scope you can understand what we have working groups which are concentrated on specifics in each of those areas. So a little bit about some of the project presentations that we've had in the tag. The big one. So we have a variety of different tools and projects. So in terms of containers, we've had things like Cryo, container deep present, workloads, you have things like the QB edge graduation, Open Tofu which is an open source project that is a fork of Terraform, HashiCorp Terraform. We also have Kubernetes related projects like KDA for cluster auto-scaling, Kubernetes auto-scaling, we have things like Cube Stiller for Kubernetes management, Cube Clipper also Kubernetes management. So these are some of the examples. Operating systems, a special operating system like Alice was mentioning, examples of flat car or Kairos. And finally the interesting topic that is very relevant now that a lot of people are talking about AI related type of projects like Q-Ray that helps you scale AI type of workloads, training workloads in case GPT that allows you to monitor your Kubernetes clusters using natural language connected to NLM. So one example project that presented is Cube-Ray and this is again an initiative project in the AI space and it's composed of different parts. You have a core, you have a ray cluster, a ray job and that's the main part of the project and there's also some community managed optional components that are external like the Cube-Ray API server, the Cube-Ray CLI and the Python Client. Another project that is part of the runtime is Flat Car as mentioned before, it's a container optimized Linux, which means it's got a minimal distributed, it's a minimal operating system for distributing containers, it's got a minimal surface attack, it's immutable, it runs AB partitioning, which means you can run updates also automatically and you can declare your configuration with Ignition File before and during provisioning. So, but what I want to show about that is not focus on Flat Car but how it comes to the bigger picture with other CNCF project and other working groups. So, Flat Car contributed to CAPI, so currently how you create an image in CAPI, you kind of have complexity around there because you need to create an image that have like three dimensions of complexities like of the Kubernetes version that you run to run, the cloud provider that you run to run on and the OS image, so all of them needs like all of these three parameters creates a specific image that you need to maintain and when you update you must to replace the node itself. So, it's kind of painful, therefore we started to work on system DCSX and what's interesting about that is that it's really simplified things, so instead of having a matrix of all of these like different things working together, you got a linear complexity because what you take, you take the basic OS from the cloud provider and at the top of that adds the bits that are interesting for you, for like for the Kubernetes version or so on. So, you can also create updates that are don't requires to create a new node but you could create also in place updates. So, it's basically like seem link for slash usr or slash opt that could be used. Now, it works well on flat card because it's a mutable operating system and it requires that slash usr would be read only and now we collaborated upstream and now system DCSX got a feature that allows also to operating system that don't have only read only slash usr petition also to enjoy this functionality. So, let's create an interesting story also upstream with Cappy and system D. So, another story that comes out of that system DCSX is a collaboration with Wazel. So, there's a link here to a library of different baked images of system D. So, basically in the declaration file that I mentioned before, you can kind of point to a link to pull the image from and basically run whatever was a distribution you are interested in. So, just simplify a bit things. So, a little bit about our working groups. So, we recently created the cognitive AI working group. So, we're very excited about this. This is happening everywhere in terms of AI. That's the conversation. Genitive AI made a big last year with the release of child GPT. So, one of the things that we're very excited about is that we created the white cognitive AI white paper. This is our first deliverable, but we're thinking about doing many more things. So, check it out. It's how it was published two days ago. If you have any feedback, send us a Slack message or any way we can help, but just let us know. So, another thing that we're working on is the cloud native AI landscape. So, as you may be aware, there's a big landscape in the cloud native ecosystem. And I think sometimes it's hard to read. So, what we're trying to do is just constrain that landscape into AI type of workloads and the cloud native ecosystem to make it easier for people to find the different projects and to get started. So, that's something that it's at the top of our minds. Additionally, we have a repository where we're thinking about a place to store artifacts or things that the community is working on. Examples of that are little tutorials or things like a workshop that can be created for people to learn AI in an easy way. For example, the full lifecycle management of AI that includes training and server and machine learning models. And another thing that we're thinking about is starting a reference implementation to make it easy for people to understand AI across the board, including the machine learning lifecycle. So, finally, we are thinking about reaching to open source projects related to the cloud native AI ecosystem. And these include the AI-specific projects that are maybe part of the Linux Foundation data in AI. So, what we're trying to do is create that collaboration across both foundations so that we can work together and people can understand each other from both sides. And with that, I think about the problem in organizations when you have data scientists on one side and on the other side of the organization, you have developers or DevOps engineers or operations engineers and they don't quite understand each other. So, the idea here is to bridge that gap and make it better and more efficient in the future. So, a little bit on another working group that we have is the Wasm Working Group. Danielle already alluded to some Wasm features in the slide card, but they're working on very exciting things. They've had presentations from Wasm Vert that allows you to virtualize Wasm components. One of the big things that they're working on is OCI support for Wasm modules. You may have heard that the Wasm ecosystem has created a model to share components or Wasm components between different applications. It's called the Wasm component model and OCI is a big part of that because that means that these components can actually be stored in artifact stores that are OCI compliant, just like containers. And they're working on other things like the Wasm observability standard and the Wasm cloud interface and that includes Wasm modules that allow users to talk to a cloud provider, like an S3 blob storage or a way to talk to a messaging application or a way to set up an HTTP service. So, there's another working group. It's a special purpose operating system working group. So far, we started it a couple of months ago. And as you can see, we covered anyone interested to show what they're doing. So, like, first presentation is any operating system that's kind of interested to be part of the group kind of joined and just demoed. There are links there to all the different meetings. If anyone in the crowd wants to join, always welcome. We're open for new attendance. And I just want to share what we do and how to get involved. So, there are two QR codes. The small one is for a panel tomorrow. So, in case you are still here, because it's the end of the day, I must say. And if you are interested to join. So, there is a schedule for just a meeting talking about, well, whatever we want to talk about, like if there are any different, whatever, standards that are interesting issues that we run through, what we consider is a special operating OS for containers and so on. And the other QR code is just like to get to in contact, like about the meeting and so on. So, we're kind of at a starting point. So, if you want to get involved, it's a good time. So, a little bit about this other work group that we have, the IoT Edge Working Group. So, the scope of the IoT Edge Working Group includes a lot of cloud-native projects. You may have heard of them. Cubatch, Open Yard, K3S. It's a very relevant cloud-native space where you run workloads in a constrained environment. And there's different requirements for these constrained environments, like smaller modules, a smaller footprint, or you have things like lower energy consumption requirements. One of the example projects that are part of the ecosystem or that is part of the ecosystem is Acree that helps you discover devices automatically at the Edge. So, you plug a device and it's automatically discovered and then you remove that device and that device is automatically removed. So, this working group is also working on the similarities and the differences between what it is cloud-native and Edge-native. So, with cloud-native, a lot of these applications are based on observability at a higher level and then also managed in a centralized location, whereas in Edge-native applications, you have considerations for being in that constrained environment. And for example, you cannot scale that much as much as in a centralized location. Your security is different too because maybe your Kubernetes or K3S cluster is located in a small box at the Edge or maybe at a toll booth or some place where there's a camera and there's a little box. So, security considerations need to have some sort of log there or some sort of encryption to the access to that mechanism. So, what we're seeing here is the differences between cloud-native and Edge-native and they're helping out with this. And another working group is the Batch system initiative. This focuses on large-scale batch workloads. They're very relevant to AI now, especially for training type of workloads. So, they created a landscape for these type of projects. So, you have projects like Volcano, which you mentioned before. We have projects like Carmada. It's another one. There's one called Armada and there's another one called Carmada. They're both related to orchestrating batch workloads. And they've also created a white paper on batch scheduling tools. So, they look at the different features and characteristics about this type of tools and how they can be used for running this type of workloads more efficiently. All right. So, the next one is the one which I am passionate about. So, a container orchestrated devices working group. It's all linked to what was previously said. Nowadays, we have a difference in the workloads. So, most of them seem to be like the new one is machine learning and AI. And obviously, all of those require accelerators and access to those kind of devices. This working group is also about collaboration, but a bit collaboration, a bit different one between the companies, between the hardware vendors on how to get the best user experience for utilising accelerators. And actually, not only accelerators, but overall hardware in the CNCF ecosystem. We are not very good on producing white papers. However, we are producing with specs and libraries. We were first a repository which was created under the new organisation in CNCF, CNCF TAX. And thanks for our lessons who helped us to do it. We are trying to keep all the information there. We have several fellow travellers. You might see some talks here on this QtCon. And maybe you can watch later in the recordings, like search about with hardware, like CPU, DRA, GPUs, more or less, it all converged to what we are doing in the background. We have a few updates. The new release of our spec is coming out. We are constantly trying to fix more corner cases or more advanced usage scenarios, like user namespaces where you are not running containers as root to access your accelerators, and so on. So, soon it will be published. Where? Where we are working? Where are things I used? Short answer everywhere. Let's start with non Kubernetes use cases. So, if you are on Red Hat systems with Podman, if you are on other systems with Docker, if you are in HPC world, in OCI mode, all of those things, we work tirelessly to get it enabled. Like, you can ask, I want GPU zero. And when in background it's happening what GPU zero is actually about. In Kubernetes, one thing what you need to know is what we think what we are doing is the basis of DRA. DRA, well, we have this DRA con nowadays. With CDI, we think what we are producing is things what enable it from a like lower layer of a stack. We also did this implementation for device plugins. So, like even all the accelerator devices can utilize with new approach of communicating what accelerator is about. And we have several fellow travelers what I already mentioned. We have NRI in container times, both cryo and container D. Those are helping to tune your native resources like CPU and memory to get the best out of the rest of our system, like accelerators and our peripherals. We are working on some caps. Have a look. You will have a link later on. Please join if you have some performance or like hardware visualization things what you might be interested to get in. All right. So we've looked at what tag runtimes up to what's happening with all of the working groups. So now let's try to look at how you can get involved in contributing to tag runtime. One of the aspects that we would like, you know, more contributions from is to reach out to other projects. Reaching out to other projects looks like something along the some something along the lines of a of opening an issue or starting a discussion at an open source projects, GitHub repository, which basically tells them that, hey, we are from runtime. We would like y'all to present at our tag runtime meetings. And what these presentations look like are basically an update or an overview of what the project is up to. How is it relevant to cloud native ecosystem? And these projects are mostly focused towards the runtime bits on how to run workloads on cloud native infrastructure.  Right. This also helps us see whether we can invite those projects to be part of CNCF of the CNCF landscape. And this also applies to existing projects in CNCF and help them move through the CNCF landscape. So these are some of the avenues. These are some of the avenues that you can try to reach our projects to one easy trick is to go to landscape.cncf.io, figure out projects which are not part of CNCF but listed on the landscape and then try reaching out to them, mostly from say container registries, orchestration provisioning, things like that. The other aspect is to interact with end users. So most of the contributions in tag runtime are maintainers specific right now, but a gap exists wherein we are looking forward to talk more and more to the end users. All of the, for example, the working group AI white paper that got out that had a lot of maintainers co authoring that white paper. And we would like more contributions from end users. So this is why if you can help us, you know, start conversations, end users see what's working out for them, what's not working out from them, that's like a great another avenue. So technical, so the end user tab is a way you can get engaged with end users and try to see how we can, you know, build synergies between the tab as well as the tag runtime aspect, right? Moving on, staffing a booth at, staffing tag runtime booth at KubeCon. So thanks to Steven, Steven's over here in the crowd. We have tag runtime booth. We started this from KubeCon cloud native on Chicago last year. And now here in Paris, we have another part time booth. So staffing the booth being there like maybe presenting an update from a working group or like just helping out with, you know, getting people introduced to tag runtime, that'd be like a great value add, right? So that's another avenue. The other aspect is annual sandbox review. So to give you some context around this, earlier last year, a technical oversight committee in CNCF came up with this proposal to help tags review annual sandbox reports. So these are the reports presented by sandbox projects on what the project has been up to, what their roadmap looks like, you know, what the adoption has been so on and so forth. So as a tag member, we had to give them recommendations based on whether we want the project to continue as part of CNCF sandbox landscape, whether the project is ready to move from sandbox to incubating stage, or whether the project has concerns and how we can remediate these concerns, right? So we had to work with the TOC liaisons that were part of tag runtime. As part of this, we got to know a lot of projects that are out there in the landscape. Tag runtime had most of the projects. Clearly, we couldn't meet the deadline of reviewing all of the projects. We got help from, say, other tags as well. So that was great. But this was this was a great avenue to like, you know, for the community to come along and collaborate on all of these things. From from now on, after after this exercise, we did a retro, we figured out that this was a very resource intensive bandwidth intensive exercise. And we try to automate all of these reviews. But I just wanted to call out all of the contributions that went out through this. So watch out for such avenues, which may come up later on and you can help, you know, help us part help us be part of this or drive these efforts. Speaking of technical oversight committee, a lot of contributions are about helping the technical oversight committee in CNCF. So any, so to give some more context, tags are supposed to provide updates to the TOC. And as part of tag, and as part of the process, these updates have moved from like sync meetings to async updates. So at tag runtime, what we've done is like collated a document with all of the updates that go out every month. So if you can help us bubble up these updates, right? So all of the tag leads bubble up updates for the month, and then send it out to TOC. These updates may include what projects have contributed to tag in that month, what the working groups are up to, whether the tag is working on something else, or some white paper, any of the contributions that are forthcoming, whether we need help in any particular area, so on and so forth. So just helping bubble this up, bubble up the information and sending across that can be like a huge value add as well. Recommendations to TOC about project moving levels is another great one. So in the CNCF ecosystem, the tags operate as reviewers while the TOC operates as an approver to a project, kind of an equivalent. Here's an example where we got pinged from TOC on asking a recommendation for the coordinate or project. This basically means that we get to have our say in terms of what projects get into CNCF, what projects transition through the levels of CNCF and so on and so forth. So if you want to have a say in contributing towards the project moving levels, this is a great avenue as well. The other aspect is to start attending tag runtime meetings. But tag runtime meeting is not just yet another meeting, it's the coolest meeting out there. This is the one where all the project maintainers, which are in the purview of runtime come and present their project, you get to interact with the maintainers, ask meaningful questions and get involved with the project as well. So we meet on first and third Thursday of each month at 8 a.m. Pacific. You can scan the QR code and like, you know, navigate from there. We've talked about working groups so you can get involved in any of the working groups. That's a great avenue as well. Another bit is the contributor ladder for tag runtime. So your contributions to tag runtime may roughly look like this, you join the tag, you start attending meetings, you start reaching out to projects. Maybe you start contributing to white papers, you get involved into some of the tag deliverables. Then you help with leading the initiatives and eventually you become a lead, maybe a co-chair or a tech lead, so on and so forth. So your journey may not exactly look like this, but it may be an overall summary of what's happening over here. So this is an effort that we're trying to drive across all the other tags as well. So to get up, to get to a contributor ladder, which is like cross tags and very much not specific to one, but just wanted to call that out as well. Another role that's coming up is a tag ambassador. So these are CNCF ambassadors who can get involved with tags as well. So if you are more interested in this, feel free to chime in on this issue. This is what all of us look like to see all of you all attend a tag runtime maintainer session at KubeCon and just people attending our meetings. So thank you for making it to this session. We really appreciate it. We hope to see some of, at all of you all, but at least some of you all at our tag runtime meetings and helping us out in the tag as well. So thank you one and all. We're open for questions now. Yeah, thanks again for the talk. I learned a lot. I was just curious about the project outreach part. I got the impression that anyone who's sort of starting to get involved can help out with that effort, but I'm curious about what the process of doing so might look like. So do we reach out on the tag runtime channel saying that here is an XYZ project that I feel like could be interesting if presented a tag runtime and if there's like a template ish thing. And what is the process in general? Yes, a question. The question was, what's what's a process for project outreach within the tags? It's kind of pretty wide open. One of the things I do is I scout the GitHub repositories for projects. I connect with other community members. I look at newsletters or things that are out there and find the place where I can contact that project. But typically that's the GitHub repository of that project. And what we do is actually open an issue or open a discussion. And we encourage the project maintainers to join the tag runtime meeting anytime in the future. And they're free to add time or when they want to meet. We actually have fixed time, but they're free to add the date when they want to present and they can add it to the agenda. So it's very wide open. And they get the time to present. So yeah, I think it varies between different community members on how they reach out to these different projects. Some community members might be more involved with other working groups or Kubernetes groups or other open source communities. And they have their own means of contacting the projects. But the idea is just to constantly be out there and outreaching to different technologies related to the runtime and see if they're interested in presenting. The idea is not also to force them to present, but to just open it up and see if they're interested. We don't get presentations from all of the projects that we reach out to. But we do sometimes numbers and try to get as much as possible. And I think the communities are very friendly. And overall, I think we have a pretty high success rate in terms of projects presented in the meetings. Yeah, I just had a comment, not a question. So I remember Steve, so Stephen and Daniel are the new chairs of tag runtimes. Can we at least get a shout out for them? So Stephen's not on the stage, but I just want to I think Stephen, you got involved in tag runtime from coupon Chicago. I remember meeting you in the hallway and we were pitching your tag runtime and now you're here. Do you want to talk about your journey and how you got involved as a chair and how your journey has been? That is going to be inspirational. Should put it in a spot. Just put me on the spot. Sure. Yeah, it's just a matter of volunteering and getting involved and kind of following through the the ladder that Rajas mentioned. So I would say first to talk to contributing or suggesting projects, you know, please post in the Slack as well, suggest we can add a template to the doc if you want to reach out to other projects. But it really just started from, you know, starting with that project outreach, attending meetings, being involved. That's the most important part is just, you know, showing up, contributing in whatever way makes sense to you, and then focus on your area of interest. Clearly, there's a lot of different areas of interest here, which is great. You don't have to be an expert in all of them, right? But you're whatever you bring to the table is very important. Yeah, I mean, you don't have to write code or to be a contributor. So any so just look at the or think about the your strengths and how you can help. So everybody's different. We're big on diversity of opinions, diversity of backgrounds of, you know, the type of career path that you follow. We're pretty open. And we understand that everybody has a different background and can help in many different ways. I just have like a follow up question, you know, of course, great topic, great discussion. You're mentioning projects. I mean, are we talking about related projects? Are we talking about CNS, the CNCA projects? Just wondering what is the definition of projects? And of course, the second part is like, how do we go in? Like, for example, if I'm interested in cloud native AI working group, how do I find, you know, the related projects?  Yeah, thanks. Thanks for the question. So if you're interested in cloud native AI working group, first step would be to join the WG artificial intelligence slack channel on CNCS slack. The other one is these projects are not necessarily part of CNCS landscape, but are relevant to cloud native and specifically relevant to tag runtime. That means they're either orchestrators or help or projects with help in running workloads on cloud native infrastructure. And with that into consideration, they can intersect with any of the other working group areas. So if you talk about artificial intelligence, projects which are related to artificial intelligence, but are also relevant to cloud native. But now in artificial intelligence, we're also trying to look or get opinions from projects which are specific to artificial intelligence and not very much intersecting with cloud native as well. Because one of the one of the deliverables of the working group is to figure out what artificial intelligence for cloud native looks like. And we need perspective from the other side as well. Like, you know, what, what, what are the gaps in those projects which can be filled by cloud native. So a thumb rule can be if you're interested in any project that you would like to reach out to, or you think is a good fit for tag runtime to reach out to is to just reach out on the tag runtime slack channel and just let us know that here, you know, this is a cool project that's out there and you may either want to reach out to it or you help you want help from us and we are free to, you know, extend help in any way possible. Does that answer your question? Thank you. I had, does anyone else have questions? Otherwise, I can have one more. Yeah. First question you need to prop up. Yeah. So I just had one question, like about the scope of the tag, right? So I did a, I did an unconference at AI have yesterday about just like batch schedulers for AI and what do we need better out of Kubernetes and general. And there was a maintainer from Q and there was a maintainer from Volcano. That was there. And turns out that they don't really work well together in some aspects and no one's talking to each other. So it's, and I saw like in the batch landscape, there was Q and there was Volcano. So is tag runtime like a avenue for these people to talk to each other or do they need to do it individually as projects? Because I'm just wondering like, how can we get these projects to talk to? Otherwise, you'll end up with a similar problem, right? I mean, tag runtime can be an avenue where we can help build synergies in terms of like how we can, you know, find out ways where the projects can collaborate better. So coordinator was an example where we found out synergies between coordinator and the Kubernetes 6 equivalent of it in-house. It can help out in six scheduling as well and things like that. So yes, definitely. But if you want to add. Yeah, absolutely. I mean, tag runtime can be an avenue. They can come together. We can, I think one avenue is what tag runtime can help out is propose some sort of standard or propose to the projects to create a standard standard so both projects can collaborate with each other. So not just like, okay, you guys talk to each other and see what you work out, but maybe create some sort of like deliverable in terms of a standard that has come up to both projects and they can work together. So make it, you know, do some work to come together. Another example, what we are doing in our working group, like we are trying to make we experience exactly the same across the whole landscape. So we are talking both to cry or to container the maintainers how to get one thing out. So it's not, it's not about what we are not talking to together. We talk, but we helping like to drive feature and to be consistent across the landscape. And just one final comment I think that a tag can help out with is awareness about other projects. So projects are doing like the same thing and multiple projects do like a lot of workload schedulers all that kind of thing. So just having the presentations, having them come to kind of a central place just for awareness or us promoting those project presentations just brings awareness and then more collaboration hopefully for those duplications. I mean we can take also the example of the operating system working group. We're sitting together at the same table like you know working in different companies, big companies that you actually don't sit together at the table. So I think yes it's definitely create an avenue to have an open discussion and see if you could you know find common goals together to work and contribute also upstream. So yeah definitely. Thank you so much for joining us today.


and welcome to Quality of Life, the show where we look at the different attributes that can affect one's quality of life. Today we're going to talk about an EAP program or Employee Assistance program and today joining us to help us with that is Pamela Krieger from Aurora Healthcare. Welcome Pamela. Thank you. To get off started with what's your education and background in the EAP arena? My education is I have a master's degree in social work and I have five years of experience working with employee assistance programming. Okay. What is an employee assistance program? Let's just get the basic definition. An employee assistance program is an employee or an employer sponsored benefit that allows any employee to access services. Those services are a huge host, a range of different services to really maximize an individual's quality of life. Okay. Why would one need to attend an employee assistance program? Well there's a couple different components to an employee assistance program. One is the direct counseling services. Through Aurora EAP, individuals can call in 24 hours a day, seven days a week and talk to a master's prepared counselor. The employer themselves can access and use services by having any type of if they have a crisis. They can access those services 24 hours a day as well. So employee and employer benefits. There are two separate areas. Okay. The counseling services, again, we have in-person and telephonic consultations. When we look at other services we're looking at those things that we call work life services. Those are things like as an employee, if you have a child or you have school-aged children and you need daycare, EAP can help navigate that. You call the 1-800 number and indicate the times and the location for which you need the childcare. And we do a comprehensive search and help them identify those services because if an employee doesn't have daycare they can't come to work. Or if they don't have quality daycare then they're going to be not focusing on work when they're at work because they're going to be worried about their child. Another feature and component is elder care services. So you have aging parents and you need some guidance or assistance on home making skills if a parent has increased or need for medical service. Anything like that legal home making, a whole host in a range of services that we can aid and assist an employee access locally and throughout the United States. So elder care and childcare consultations is one piece of our work life service. We also have the benefit of adoption services. So if somebody wants to adopt a child from where and how much to support services afterwards as well. We also have a legal consultation component to our work life service so that if any employee or their family member has a legal question anything outside of employment law that's the only type of law we can't consult on because again it's an employer-sponsored benefit and we won't help the employee take action against the agency that's providing that service. But anything from traffic accident to a consumer issue, a will, anything like that they can call and access those services as well. And in a nutshell how would it work? I think there's a couple different components of it let's say in general how does it work where an employee is referred to or seeks a service. Employee assistance programming works through a 1-800 number again that's answered 24 hours a day seven days a week to schedule an appointment or to access those direct services you would want to call between eight and five central time. But we have intake specialists that will direct any individual whether they need to talk to an assessment counselor immediately or talk to somebody legally. So it's really accessing the service by calling, calling our 1-800 number. In the situation of the workplace can the employer refer the employee to employee assistance program? Absolutely one of the pieces of my job that I get to do is I work with about a hundred organizations in the Sheboygan County area and so one of the things that I get to do is go and educate not only the employer so the staff the supervisors and the employees about what benefits EAP offers. A lot of times supervisors will encourage employees to seek the service if they can tell there's a work performance problem or issue. And one of the benefits to the employer is that we have what's called a supervisory referral component so that when an employee has maybe received verbal reprimand you know along the progressive discipline lines maybe a suspension somewhere along the lines they're going to maybe make it mandatory to come to EAP so that is a benefit. So the supervisor would meet with the employee and indicate it's a requirement that you attend EAP to kind of address the issue. So EAP could be mandatory or voluntary? Absolutely seven percent of our business is the supervisor referral component and a lot of organizations use that really because they care deeply about their employees but also understand that they don't want to violate sort of that personal piece and understand the dynamics of what's going on. That's a great a great segue to saying that employee assistance programming is completely confidential so just as anyone would call and contact their physician we operate under the same laws so that basically the only thing as a supervisor referral that the organization knows about the employee signs a consent form which allows us to tell the employer yes they came and hopefully in the end what they're what the employer is going to see is that whatever the problem was that was impacting the employee's work performance would be addressed. So like with the confidentiality laws like with HIP and seeing the physician that's bound by that as well so that way if I voluntarily go there it's confidential information? Yes absolutely or if you mandatory you know through a supervisor referral go the only feedback that is given to the organization is that you were cooperative and compliant and if there are further recommendations beyond the scope of EAP then then then they know that but they don't know about the specific situations that you're bringing forth because we want it to be a very trusting open helpful environment. How long if an employee is either referenced or voluntarily wants to look at one of the services how long does the program usually last or the session last? Great question so employers have the opportunity to contract with Aurora and so they Aurora EAP and so they can have a three session model or a six session model so voluntary individuals that come through EAP have three or six sessions per situation so if in January they call because they're stressed about a situation they can come in potentially and meet with an assessment counselor three to six times and then later in the year something else happens they can come in and meet with our assessment counselor three to six times again as well. On a supervisory referral we require that the individual comes in two times so that our assessment counselors can fully gauge and understand the dynamics of what that employee is experiencing. Okay what's the format of the sessions or the program is it just you know a physician patient or counselor you know patient conversation or is there a formal testing or I guess how does that all work? So in EAP we do not do any diagnosing of individuals if individuals feel more comfortable doing strictly telephonic consultations within with you know the assessment counselor they can do that as well or it can be face-to-face Aurora EAP has a location in Plymouth and one in Sheboygan where they meet face-to-face with those assessment counselors so it's really the comfort level of the individual. However again if it is a required a supervisory referral then we do require face to two face-to-face sessions and that really is one component of EAP. The other component of EAP is that when organizations maybe are struggling with a particular issue high levels of stress some things going on within a team or a department that's one of the things that I get to do is go in and provide training so I will provide any type of supervisory or employee training ranging from anything from stress to strength we talk about wellness overall wellness looking at the seven components of wellness and give employees and supervisors very tangible materials to take back and use with their employees okay we touched upon before you know someone can get referred to or voluntarily you know go how are employees or people in general educated on that that this is what's all available you know so they can go some place so the the organization can do a variety of different things we provide monthly focus of the month highlighted topics which we have on our website so that communication is distributed to employees however the organization feels it's most appropriate we also have three times a year we send out a newsletter directly to supervisors so as a generalized reminder to the supervisor who is in touch with those employees on a day-to-day basis and then three times a year we have employee newsletters where we focus on a particular topic or issue highlight that on our website and then provide additional information accordingly we do corresponding posters so that people are aware of the of the topic of the month okay who can refer me to an employee assistance program and who is usually all involved when this does happen so in general we certainly hope that all organizations offer employee assistance programming to their employees it is a great benefit that allows individuals to focus on work when they're at work and focus on home when they're at home and so that's why we highlight the issues that we do so that we know that happy people produce quality results and can stay focused at work when they're at work and have a good life when they're at home as well so that it's balanced so it depends on the organization that you work with so you need to check it as an employee I would check with your human resource department about what employee assistance program service you have that you're eligible for did that answer yeah yes definitely thank you is it required by law it is not required by law for for organization to have them offer employee assistance program it is not but it is a service that allows an employee to access services without any payment so it's an employer sponsored so the employer pays per month fee for the employee to access that service counseling services again those work life services we connected on earlier whether it be financial consultation childcare eldercare legal consultation that kind of thing okay is the method usually an employee excuse me an employer wants to you know start up an employee assistance program how to I mean do they go out and just research and you know talk to agencies like yourselves or is it the other way where agencies like yourselves talk to employers and approach employers how does that relationship work it can happen either way again we hope that all organizations offer employee assistance programming to their to their employees it's a win-win for the employer and the employee we oftentimes find that having employee assistance program will reduce healthcare costs particularly the mental health component or piece of that so through insurance brokers or um you know contacting any um agency that would provide the service absolutely so that's one nice benefit for an employee is they can go go to the service take advantage and help themselves as well as then helping the employer you know ultimately in the workforce and it's a benefit provided by the employer yeah it's a win-win for everybody so it's a win-win for everybody yeah so it helps the whole man the workplace absolutely it helps the community yes and the because the individuals that work in the community live in the community and when they are happy healthy productive individuals we all win absolutely the concept of EAP how long has it been in existence is this a fairly new thing or has it been around for a while it's not a fairly new thing but it's a it's a great question Aurora EAP has been actively involved in the community since 1983 and they were originally started years and years ago unfortunately I can't give you a specific year but they were originated when individuals were struggling with severe significant alcohol and drug abuse issues and they were brought into the workforce they really evolved over time to be a very voluntary program that offers way way above and beyond service above and beyond the alcohol and drug abuse although it is a primary issue that we educate and highlight particularly in our community in here in Sheboygan what types of organizations provide a EAP program like yourselves is it a usually a health care provider a group a hospital or I guess what are the agencies that provide these services so as I indicated earlier I work with about a hundred organizations and they range anything from any you know employees of 10 to over 1800 employees and they are can be manufacturing environments they can be school districts they can be banking industry it can be pretty much any organization that really cares about the quality of life that an individual who's working an employee works for them if they want to if they want to provide that that benefit and service so as an individual if I would be looking for counseling I have to be an employee of an organization that has an EAP program I couldn't just like say you know I want to come to your facility because I need help on something so I couldn't come on my own absolutely you're correct and so that's why I would encourage employees to ask their human resource department do we have an EAP and understand those very comprehensive or not comprehensive benefits because all EAPs are not the same and so what they offer are very different and so I would absolutely direct an employee to connect with their their human resource department you just follow right into my next question how do employee assistant programs differ from one another well the type of service they offer ranges some employee assistance programs only offer telephonic Aurora EAP is local and we have you know that face-to-face we have we have locally we have three assessment counselors that see the bulk of the employees within this community that use Aurora EAP I work with organization organizations that consist of about 20,000 employees so the face-to-face component is different can be different and then the type of service so maybe not all EAPs offer those work life services the free 30-minute consultation with an attorney the financial assistance the education assistance that kind of thing so it depends and that's why really your human resource or your benefits specialist within your organization will understand that and know know the benefits that are that the employee is eligible for okay to touch briefly I know you mentioned Aurora has a EAP program that sponsors one has one offers one for employers what types of agencies you know other than Aurora can can or do provide that type of a program for employers to go to is it like basically any health care provider you know is it a hospital type thing or you know what how does that come about so sometimes other organizations such as insurance organizations will or a short-term disability sometimes there are add-ons to a company that accesses insurance benefits for their employees they may offer an EAP program so that I mean it just depends again the quality of what you receive and the type of benefit you offer your employee depends on you know the organization that you're getting the service from sure like it says not everybody's the same as what we talked about before it depends on you know the EAP provider who actually provides the counseling service what's their menu of you know services that they want to provide absolutely as far as that goes yeah across the country or let's say within you know what to say Aurora you know it's nationwide or whichever does your EAP cover the whole country you know where the services are pretty much the same throughout or do you have to specifically gear it towards region you know the country so we offer Aurora EAP we work with a variety over 300 organizations some of the organizations have affiliate or sister parent companies in other parts of the state of the United States and we do offer and extend those services to those organizations as well and so we do work with some affiliate sites that understand our practice and our business how we do things and we enter into agreements with them to provide our service okay what's your visitation you know volumes usually on a yearly basis around well when looking at overall utilization we have national average for an individual coming in to using EAP is anywhere from four to six percent of the population our average within EAP our Aurora EAP is is approximately five percent so right in the middle and that's our total book of business and so when we say book of business we are looking at Green Bay across the Oshkosh down to the Illinois border I will say specific to Sheboygan County I tend to see with organizations I work with a higher percentage of utilization I sense that I work with a lot of organizations that know their employees rather well and care deeply about their their employees and so I tend to see anywhere from eight to nine percent utilization and that's we we keep track of that on a one time or an initial use so so yeah we're very I mean people are really taking advantage of employees are taking advantage of that service that's good and then of those eight or nine percent that you see what's your success rate where a positive impact has been made that's a great question and so individuals that come to EAP we are able to aid in a system 84 percent of those individuals do not require or need a further referral so EAP is really designed to aid and assist individuals with you know somebody that's experiencing an issue or a problem that may not be you know a long-standing issue and so if again we don't diagnose individuals so if we feel or assess that a situation or a person might need more than what EAP can provide we will provide that outpatient referral and that happens anywhere you know 14 percent of the time a small percentage sure and that's a great question or a great answer which comes into my next question is if somebody goes through EAP and the council realizes we may need more help here for this individual we can provide you can do referrals to other agencies such as you know mental health or you know physical you know cardiac and such we do we provide any type of referral that an individual needs as it relates to whatever problem they're experiencing and so we are aware of the organizations that we work with their insurance so it doesn't have to be Aurora insurance so if somebody has a different carrier we are aware of who those preferred providers are and guide those employees appropriately okay because we want them to live the best life that they can and have the least out-of-pocket expense possible absolutely with that so the whole EAP program have you seen shifts or volumes increased during let's say the latest economic downturn that we had or you know natural disasters or you know events like that that happened you know in our either in our community or in our world as far as that goes you see the trends follow that for the usage of utilization of you know EAPs yeah I would say the use of EAP has been pretty consistent perhaps the issues for which the individuals come through you know might fluctuate somewhat 33% of the individuals we see in EAP have a relationship issue of some type whether it be child and family or a marital relationship 21% stress anxiety and depression 25% do those work life services so the child care elder care financial legal consultation a slight increase in occupational over time particular and specific to the Sheboyin County we do see a higher incidence of substance use alcohol and or drug abuse need for those services and we do track sort of you know the primary issue that individuals struggle with and and why do we do that because we care about what this community is struggling with and we want to make sure that the resources are there and available for the individuals okay as far as the benefit of an EAP program have you done any studies to you know try and measure the cost savings or impact on a business by using these programs you know so they don't lose productivity and while as we keep a healthy workforce you know work related you know injury incidents such as that do you have any statistics compiled on that that's that's a great question I did bring some information that is through our national organization actually employee assistance professional association through EPA companies with employee assistance programs report 68 percent increase in morale 62 percent higher productivity 59 percent lower absenteeism rate 35 percent reduced reduced turnovers and 21 percent fewer accidents so when we look at a return on investment the organizations that I work with um on an annual basis we do a dollar for dollar um and anywhere they see one dollar invested in aurora you know or EAP program right we'll see anywhere from a seven dollar to a 25 dollar return nice along along with that you know one has to look at it's not only the benefit of where the employer is you know benefiting increasing profits and such it's also the home life of the people themselves I mean that's where we really need to focus is you know as our show quality of life it's the whole the whole circle of quality of life not just one aspect of how it all fits together absolutely you know as far as that goes um our time is just about up do you have any closing remarks or any other comments or charts you'd like to show our audience I certainly just thank you for highlighting employee assistance programming I think it is a great benefit um that oftentimes is underutilized within organizations and just really your emphasis on quality of life because the reality is is that work employment situations um employers impact employees who in general um make our community what it is and so um I thank you for for that opportunity and certainly any partnerships moving forward um we welcome no I thank you for joining our show and one thing you know that I think people should remember to realize is if they're not alone if they need help there's a place to go you know benefits they're asked for don't be afraid not to say well I can do this on my own because there's many times especially in this day and age things are so fast the stress level is so high you're being pulled in how many different directions asks for help because it's there absolutely couldn't have said it better thank you on behalf of quality of life we covered employee assistance program Pamela I'd like to thank you for being on the show and if you have any questions or comments please visit our website at www.wscsheboygan.com thank you for watching I'm Dave Augustine


The place that's called the mistake on the lake by people in Pittsburgh and other places was no longer the mistake on the lake It was the nuclear factory on the lake with Baker Mayfield running wild on Thursday night football Then there are some down times But now we are back a playoff win over the Pittsburgh Steelers Baker Mayfield Has skippered that show boat of the Cleveland Browns into new waters that they have not seen in a long long time They know that the football gods could potentially say I am the captain now if they grab Baker Mayfield and throw his off of the ship that potentially guided them into much more pleasant Worlds But I don't think that offer is going to be anywhere near what we have seen for other quarterbacks I think Baker Mayfield potentially knows that this is going to be a this is a massive year for him That's why I think he is so determined to play and I think he knows how good the team is and everything like that I just don't think it's going to be that next record level setting contract offer


Welcome folks, we have the down industry trading up 25 Nasdaqs up 11 S&Ps of flat, gold contract also flat 18.34 oz. We have silver down 6 cents, $21.08 an oz, light sweet food, getting hit again, down a buck 85 at $82.36 a barrel, notes and bonds. A 10-year note, up 6.6, and 107.09 in a 30-year, down 2 at 111.14 and King Gala, so King Gala's down 466, 106, 338, Euro's at 105, Yen's at 148, British Prime's at 121. Let me show you something on the U.S. dollar here, so check this out. You know, you're in a beautiful channel here, right? And right from the lows, going all the way back to July, you know, the start out at 8, and we haven't had more than two down days, so tomorrow's going to be a big deal folks, okay? Right from this very low, you had more than two down days in the dollar. Now you can see what's happening. It's approaching the trend line, right? We'll see what happens. It breaks that trend line, guess what? Then you're going to see the S&P move, then you're going to see the commodities move. We're going to take a look at the bond market, and this is intriguing because we have the bonds and we have the dollar, and the real question is, is that okay?  So what we've had first is that you did have a rejection of lower price, and you have a slight fall through today, nothing heavy. You know, rejected lower price yesterday, so the yield is still 4.71, and the high was just two days ago is 4.79, and this market folks is all about yields and all about the dollar. I mean, the correlation is so direct. Now oil, let's take a look at oil. Oil is getting absolutely smoked, man. CLA, there we go. The oil market is in trouble. It's pretty amazing that it can get in trouble so quick. Yeah, look at this thing, man. It's non-stop. I mean, you're talking about three, six days ago, we're at 95 and we're at 82. There's a haircut, man. And the way this is set up, well, we'll see how it's gonna, it's already inside the lower range. Yeah, it is. As soon as it's inside 83.59, that's the lower range. That sets up 72. Bottom line, you're gonna see those equities go lower, but we're also gonna see his lower inflation numbers. Stay right there, folks. Come right back.


So this is the Innogear, this thing, Innogear Aromatherapy Diffuser. And you don't need to remember this, I'm going to put a link in the description, basically. Basically this thing is a essential oil diffuser, so the idea is you pour into this, I assume this small jug and somehow that goes in here, you pour a couple of drops of essential oils, mix it with water and it lasts for four hours and it just creates this lovely cool mist of like essential oils and it relaxes and infuses the environment with something really relaxing. So this is really cool, I'm super excited about this, but I'm going to try some peppermint essential oil, I'm not sure why, I haven't actually researched yet what they each do, they have different effects, but I'm going to try the peppermint one. So we've got this little tank here of about a hundred millilitres of water, put in a couple of drops just in there, that's probably more than you need, that's like four drops, but okay so we're then going to pour it into here, just like that, all hundred millilitres of water in the main tank, then we're going to put this on, that's locked on now, now we just need to connect to the power. So yeah I'm super excited about this for a number of reasons, firstly because this is going to be a really nice sort of thing for the background of my videos, you know you're going to see the cool mist come out hopefully with the nice LED lights, but secondly and most importantly for you guys, this is really a good way of making a room relaxing, I've never owned one of these, as a full disclaimer I wasn't paid by this company at all, this is just something I've ordered, but what I did is when I was out shopping the other day I walked into a small shop and they had loads of these sort of on right, they were on and they were diffusing the essential oils into the air and as soon as I walked in that room I felt relaxed and calm and just really serene and nice and I just wanted to replicate that feeling, I just wanted to have that effect in my room. So this, I'm going to do a complete review of this thing, I don't know what model this even is, it's like the best-selling one on Amazon, I just thought it would be cool to try it out and show you guys what this does and maybe you might want to get one, maybe you might not, I don't know. It's got a mist setting and a light setting, I assume that's where the power goes and there's the fan there, these are my essential oils, I've had these for a while and you know I use these anyway in bars and things and for making the rooms feel nice but I've got a few different ones, lavender, orange, peppermint, rosemary, lemongrass, all these things, the idea is you put a couple of drops in here, mix it with water, pour this in there and then it evaporates or you know diffuses the essential oils into the room. Okay so I got it working, it's on like a 30 second interval thing so I just really want to show you guys the lights here, it's cycling through LED lights because I press this thing and I press the mist button which makes it sort of every few seconds it spurs mist out so how cool is that? And this is like a really refreshing cool mist that smells incredible because I put peppermint oil in it and it's just amazing. That is incredible, I'm very impressed with this, this is a perfect addition to any room, anyone who wants to lose a dream or just even just have a relaxing room, this is highly recommended, I'm very impressed.


Hi everyone, I'm a dumb. I forgot to do the break credit So here it is break credit randomizer for pick your team at 17 When it sells out jz number and it'll be on the schedule when it does we still have to do some fillers for that so for pick your team 17 if we If you follow those instructions right there, you got a chance at some break credit So basically you have to buy an even number of teams. I'm gonna alphabetize your names By your first names Aaron bought four teams. That's two entries Eddie bought two teams. That's an entry Jeremy only got one Caroling. Oh and Logan M. But six teams. That's three entries Niko bought four teams. That's two entries Patrick got four teams. That's two entries Peter got two teams. That's an entry Rick T got two. That's one Sean Robinson got two. That's one Simon got two and One oops, and then Stephen K. Got two. It's one like I said Jay-Z for the second time that there's fillers that we have to Do for it to unlock that break. They're right next to Those they're random number blocks to check that out pay attention to those All right now let's roll it and randomize it Four to three seven times. So let's make sure I got everyone right Aaron Eddie Logan Niko Patrick Peter Rick Sean Simon and Stephen alright, so that's Four three seven times top two get break credit one two three four five Six and fingers crossed seventh and final time after seven Patrick and Aaron congrats to you I don't know if I called your name too often during the break But hundred twenty five dollars a break credit each going your way to jaskiescasebreaks.com


We continue today with Lesson 255. This day I choose to spend in perfect peace. It does not seem to me that I can choose to have but peace today.  Let me this day have faith in Him who says I am God's Son. And let the peace I choose be mine today. Bear witness to the truth of what He says. God's Son can have no cares and must remain forever in the peace of heaven. In His name I give today to finding what my Father wills for me, accepting it as mine, and giving it to all my Father's sons along with me.  The peace you gave Him still is in His mind. And it is there I choose to spend today. Amen.