Hello, and welcome back. Hope you had a nice lunch break. We're approaching the final straight here at the Big Things Conference. This afternoon is our final block. Now, to build a machine learning model, the quality and quantity of data are important. Of course they are. But our next speaker believes that there is another often overlooked type of data, metadata. To explain more, we have with us Jörg Schatz, CTO of Orango DB. Jörg, are you there? Yes, I'm here. Thank you so much for the introduction. Great to see you. Thank you so much for organizing all of this. And yes, we'll dive into many different ways in which graph or data in general can be useful for your machine learning pipeline. Whenever you're ready, Jörg, take it away. I'm happy to take it off. And let's get started. Yeah, as mentioned, we will talk today about different ways in which machine learning can actually depend on different kinds of data. And so in short, different ways graph data is important for your machine learning pipeline. What is ML metadata or metadata in general and why is it so important? Why should you actually care if you want to build a production grade machine learning pipeline and we'll briefly touch up on different open source solutions to solve that problem as well. Interestingly, when I looked at the schedule, the other talk which is happening right now is about improving predictions by automatic drift detection that actually touches upon a similar related field is like when your data changes and we'll see how that relates to metadata as well. So about me, I'm, as already mentioned, CTO over at Orango DB. Basically my entire career has been building large-scale systems, either database systems or large-scale machine learning platforms. And that's why I'm very happy that in my current role, I can combine both a view from an ML operations perspective, but also from a database perspective and combine all those passions within one role. And yeah, let's see how this combination looks like. So I think still for many people, I interact with the typical view of someone writing machine learning code or data scientists like we're sitting in front of your laptop, we have TensorFlow installed, everything's great, and we can really achieve really cool stuff. So we are kind of like the superhero. We have nowadays lots of data available. We have huge compute centers, a lot of compute resources, and we also have really cool machine learning models or machine learning research, which enables us to do really cool things. But the question is, how can we actually move from like and prototype over to something which actually involves our business, which moves us forward, which generates value out of there. And that's actually a bit less exciting than we might imagine. I think for a lot of people, this image of the data scientists, and they get some data from business, they write really intelligent machine learning codes, they train a model that performs great. So they run that model and then they start over with the next problem. Unfortunately, in reality, it looks a little different. So this is a chart from a Google paper. Probably many of you have seen that about the hidden depth in machine learning systems or machine learning pipelines. And as we can see here, so they try to quantify like where is their machine learning teams are spending their resources, their time on, and only this little black box here in the middle is the actual machine learning code. And basically every single round is from like data collection, data verification, resource management, et cetera, is actually way more cost intensive in terms of time compared to the actual ML code. So how does it then typically look like? So in reality, most machine learning pipelines I've seen across different companies follow roughly a similar scheme as this one here. So on the very left, we have some data and that can also be some streaming, stream data stream. Then our data engineers, our data scientists will go in and engineer some features. So try to prepare the data, try to get rid of missing data, try to form meaningful features out of set. Next is then we would define our model and then train our model out of set. So this is usually very compute intensive. Also often we have multiple iterations here and then we have either one or often also multiple models out there. So multiple candidates for model serving and these models need to be managed stored and also we need to have a catalog to decide like which of those models do we then want to really deploy onto our live production system. And this is then where machine learning serving comes in. This is where we actually get value out of it. Imagine we have a web service distinguishing cats and dogs. This would be when we can deploy our machine learning model, some user can upload an image and we try to predict whether it's a cat or dog or anything out there. So next, this is actually in, if you look at different open source systems. So for example, TensorFlow extended, which is the ecosystem around the well-known TensorFlow framework. We actually follow a very similar model here. So on the left we have the data ingestion, then we have data validation, the actual transformation. So going into feature engineering, then our training, our model management and then the pusher and model serving. So this is how it comes down to, if you look at some other, if you look at the virtual real world systems out there, if you look at Kubeflow or other systems, they typically follow this very similar pattern that we've seen earlier. What has that to do with databases? So as mentioned, I work at the Rengar DBs. This is a graph and beyond database. So as a core, we are really scalable graph database and we also can do full text search. We can do document, et cetera, which is not really the core here of this talk, but why is that actually interesting to me working at a database company? So what we see more and more happening to really build enterprise great machine learning systems, machine learning pipelines, database systems will play a pretty big role in that. A lot of the things we have invented or in need for database systems like role management, permission management, tracking of different resources can actually also help us to move from like a prototype machine learning solution, our research team built to something which we can actually utilize in our enterprise. This is a paper by Microsoft, really interesting read, but we'll see also some aspects of that in the coming slides. So if we go through this pipeline, let's just have a look for where databases can actually play a role. I think the obvious part of the schema of this pipeline where databases play in is on the data in streaming side, obviously. So we could either load data from a database or store data in a database, but that can be the source of our training data and verification data. Next part where it can also play in is in the feature engineering and model training. Interestingly, especially graph databases are often used for analytics and there we can already have some unsupervised machine learning models like K nearest neighbors, like community detection or other things where in that sense we don't really train a model, but we can extract, for example, features which can then be leveraged for our model training. So basically we often see databases and also in particular graph databases being used in this preparation of data. Now, most interestingly, this is also where we see a lot of the future of machine learning is basically by explicitly leveraging graph machine learning. If we look at, for example, what DeepMind is doing in terms of Google Maps ETA predictions, if we look at what computational chemistry is doing in terms of drug discovery, molecules, if we look at them, they're actually also graph. A lot of the new research, also if you look at NeurIPS and other big conferences are actually revolving around graph machine learning around graph analytics and I really like this quote here. We can really make better predictions if we utilize relationships explicitly. The example I typically give here is, imagine here in the middle we have some social network and we want to predict churn in our network. In traditional machine learning we would go in and basically for each user we would have a feature vector out there and then try to predict its churn probability. Imagine now actually all our friends have just left LinkedIn, Facebook, whatever favorite network we are talking about and we now want to predict the probabilities that I hear in the middle will churn as well. Obviously this will be pretty high because now all my close friends have just left the network and probably I'll have a similar reason as them for leaving as well. So this is kind of key to this idea of graph machine learning that we can explicitly leverage those neighborhood or connection information out there. So kind of what is the different stages there? We can have simple graph queries. So for example, imagine you're at LinkedIn and you want to show a user like, hey, who could give you an introduction to another user? So basically who is on that path between you and the other user? That's a simple graph query, basically any graph database systems can deal with. Furthermore, we can have graph analytics. Who is the most connected person? Who are the influencers I need to target? Who are the kind of recommender systems are also often built on top of graph analytics. And then we have the graph machine learning which can actually help us to build statistical machine learning model and then do predictions such as, for example, who are potential connections? So what edges are missing in our graph? Who is likely to churn as we just discovered in the example of predicting churn in the social network or any other kind of network. And those are the different things we can actually deal with from a graph analytics from a graph ML perspective. I think the interesting insight here is that often traditional machine learning has the assumption that individual records are independent and identically distributed data. In reality, this is often not the case. So in real world data scenarios and real world graphs, we often have assumptions, for example, as homophily that neighboring nodes are similar. As I said, if all my friends have just left a particular network, probably my likelihood to churn is also higher than some random person in that network. And this is actually what we see a lot happening right now that people go in and explicitly exploit the graph structure in terms of network. Most interestingly, and this is what most of this talk will actually be about, is that databases can play a role for the entirety of your graph ML pipeline. What do I mean by that? Let's actually take a look at an example. So I used to work in a healthcare scenario, healthcare I start up, and we had that challenge that we were training models based on some privacy-relevant data. And now the question came up, what actually happens if one of those people is withdrawing his consent for us to use his data? It's kind of a bit still under debate whether under CCPA or those privacy laws, we would have to withdraw our model because the model is actually influenced by this sensitive data on the other hand. But at least we wanted to be able to identify which models here on the other end were actually impacted by that data. So we went through our entire pipeline. So we looked at the data site, identified the data files, which had those patient records in there. We went next, which features did we generate out of those datasets? Manual stab, looking through locks, very lengthy. Then we looked on what models did we train with that data? Again, very lengthy, looking through locks, very annoying to check. What models resulted out of that? What models were in our training, in our model management software? And then last step, which of those models did we actually deploy to production? So this was actually a manual lookup across all those different pipeline stages, which was very annoying and very time-consuming to figure out. And this was basically the point where we were like, oh, we need a solution here, which helps us to identify that automatically. If you're more interested in what kind of information can be pushed through a machine learning pipeline, there's an interesting paper, the secret sharer. So unintended memorization in neural networks, which is pretty much about how much your training data can be influenced or can be retrieved by the models later on. So basically, can I actually guess a credit card number which was used during training in the live model later on? So really interesting read. And again, kind of relating to like, how does our initial data impact the model in the end? When going through there, we identified a number of other challenges we would like to solve as well. So as mentioned, the biggest problem for us was we wanted to understand the provenance of a model. Where was it trained from? Which features did it come from? And understanding that also gave us, should give us a complete version history. And also an audit block. If you're working in constrained environments, often those audit logs are really important. Like who trained what, who was all involved in training a certain model, which different parties. Then also comparing the performance across different models. They say it really were evolved over time, finding reusable steps for what can be reused different features. I think that notion of feature stores, feature catalogs is becoming more popular as well. This is something we looked at as well. And then also the question is my serving data. So is this data, I actually see the data distribution on my serving side. Is it actually the same than what I've seen on my training side? And by the way, this is what the parallel talk is right now about. And we basically said like, oh, this is currently pretty hard to answer. What do we need? We need metadata. What is metadata about? So here is a definition from Kubeflow. So Kubeflow is basically a way to deploy a variety of different machine learning to deploy machine learning pipelines on top of Kubernetes in short. And here is the definition is basically metadata is information about runs, executions, basically anything but the data itself. So we can basically see it. We know when a certain, we have meta information about a certain data set. When was it created? When was it accessed? How big it is? Et cetera. Who has access? We have information about transformations from which data set was it derived? When was it transformed? When was it trained if we talk about models? So this is basically all metadata for us. And next we were actually looking at collecting the metadata across the entire pipeline. So we wanted metadata across all those different steps to be stored in a single database store. As mentioned, when we went through that exercise, it basically was manual mergers. We somewhere had that meta data about our data sets somewhere else about our features, somewhere else about our models. But the realization was actually, hey, we need that in one single database. And this is when we actually started to develop an open source solution called Arango ML Pipeline, where we used Arango DB as a common store for that. So it's a simple Python interface on top, which plugs into different machine learning systems and easily allows us to store this metadata. Main goal when developing that was A, to keep it open source so anyone can contribute and extend it. But then also really to make it extensible because already in our environment, we had different training jobs, we had some Spark jobs, we had some TensorFlow jobs, some PyTorch jobs, et cetera. And all of them were creating different metadata. And this actually also led to our choice of using Arango DB down here. Because we first evaluated relational database systems, there we really faced a challenge of, oh, we would have to put that in a fixed schema. Next, we evaluated document databases, and there it was really hard to track the lineage in between. And this is where we ended up at Arango DB, which both supports a graph model, but also that document model. So on an individual level, we were able to store in a flexible schema different information. So for example, the description of a dataset is just a JSON format. There are no fixed fields. We had a general schema for it, but it could really vary whether data was coming from HDFS or whether data was coming from, for example, S3 or some other data source. Similar for the transformations, similar for the features, they all, the information, they actually stored heavily dependent on which system we were using in the end. Then the next step we needed is, we needed to connect that information. And this is really where the graph part is coming in. So by having a graph database in general, we can go in and then connect those different pieces of information. So we can say like, hey, this dataset is related to this transformation. This transformation is related to this feature, this feature, again, and so on and so on. And keep in mind, there might also be branches or even loops in that one feature might be derived from another feature out there. And so with this kind of information, that question which dataset is impacting what kind of models just turns into a graph reachability query because you're basically asking what models can I arrive from a certain dataset in this graph? And this actually, I think, is a very nice insight that we can query all of that from a graph perspective because in the end, this forms a graph of how different components are depending on each other, what's the provenance of certain artifacts within our pipeline. And here, for example, this would be just a simple query to just look up all the entities being derived from a certain feature. And you can also be ended up having really simple queries for all the different challenges mentioned above from finding out the provenance, from finding out then the audit log, like who all contributed to training a certain model, which again, if you work in restricted environments such as finance, healthcare, those can be pretty important questions to ask. And yeah, with that, it actually turned out to be a really useful tool for different kinds of people. Initially, it was mostly driven from like a data ops perspective. They wanted to track that lineage. They wanted to make sure we are compliant with GDPR, CCPA, like regulations. But in the end, with that, we could also, the data ops person can also use it for the audit trails for reproducible model building. So like if we want to go in and retrain a certain model, we can really identify what is all the, what are all the different artifacts which went in there, what was the random seeds used for a particular training run, and those kind of things because it's all stored in the associated metadata and enabled us to really retrain a model with a single click. Similarly, the data scientists themselves, they also get really interested in that because it makes it very easy to look up relevant entities for a given model. Similarly, at a large company, you have a model predicting, let's take again, user churn, and you want to build a similar model. And if you can now go in and really understand the pipeline, which was used for the previous model, this will make your job a whole lot easier, especially if you're new to the team and you don't have the history. So onboarding new data scientists really became much easier for us. Similarly, it's also, it's very nice to track performance differences. So as we can serve multiple models in parallel, we can then compare where's actually the difference, why does it behave different in terms of runtime or in terms of precision as well. And I think overall it makes it much easier to reuse certain entities across your pipeline if you have a catalog of that. I've seen way too many companies who just built such a kind of pipeline, they train a model and then this model is just used as an entity by itself. I would hope that kind of one of the takeaway messages from this talk has said, you shouldn't treat a model just as a singular entity, but treating a model with its history makes it actually valuable in production grade scenarios. Then last but not least, also like our administrators, tracking the usage or SREs nowadays, tracking the resource usage became much easier because they could just see like who ran how many jobs on our training, various training platforms who use those models in the end and this actually helps us to do resource accounting across those different, across different teams, across different models in the end. So maybe just going a little bit into details on how a Wrangler ML treats that by itself. I mentioned in the beginning, it's an open source project designed to be extensible. So this is why I put schema in quotations here, but we try to come up with a connection, with a graph, so to say, of useful entities for most people. So we, for example, have an entity for, a preset entity for data sets, we have preset entity for transformation features, experiments with different performance characteristics for model later on, then for serving performance. So this is all kind of like very easy and it's in the pre-existing API. Again, the advantage of graph here is that we are flexible. So most, actually any system I saw wasn't like any other system. So I feel they really vary in what they do and also how they combine certain things, real-world pipelines, despite following this general scheme. But this is, as you have a graph, you can dynamically change it, change the edges in between. So you can add new entities, you can change the edges in between what they derive from, and basically set your graph schema for your specific ML pipeline. In the end, it's a Python package, it has an HTTP API, which most people use, that allows you to plug into any existing system out there. We also have a TensorFlow extended integration and then from that within your machine learning code, from within your machine learning system, you can basically just register different entities and basically then also register the provenance in between. You can, there comes with a handy UI, you can discover certain entities, you can search your metadata, for example, you want to build a new model for user predictions, you might want to search models which are doing something similar and which are being used in production. And then in the end, of course, there's also a graph view the graph of your entire metadata. So this is not the only open source solution out there, there are also others. Unfortunately, they weren't available when we developed it, otherwise we probably would, might have co-developed on this as well. But for example, also TensorFlow extended, so TFX in short, also came up with its own metadata solution out there called machine learning metadata MLMD. And this actually plugs into the existing TensorFlow extended system and each component in TensorFlow will automatically if you enable it write its metadata into that store. Interestingly, if you look at it, the metadata store is following your graph pattern. And this is something where we also reached out to the TensorFlow extended team to come up with a more extensible interface to actually support the graph interface because in the beginning, they actually just used to write it back to relational database, which felt like, hey, you're turning everything into a graph, then you transform it into a relational model, then you retrieve it again for querying it. And I think this is just, again, an interesting revelation that this metadata is actually forming a graph and is also becoming more and more important as TensorFlow extended implemented it. Qflow is now also supporting metadata tracking and most of the big machine learning systems, open source systems I see out there are supporting some kind of metadata storage. And as mentioned, if you're building your own platform, a Rango ML pipeline can be the solution for you because it's just an HTTP API, you can leverage with Spark, with TensorFlow, with PyTorch and your favorite solution and your favorite custom stack. So you don't need a pre-built machine learning pipeline supporting it out of the box, even though I must say, of course, that makes it much, much easier because it's like really deeply built into the system. And here, by the way, you can also build a connector to plug into TensorFlow extended. So you can even, if you're using TensorFlow extended, you can still leverage the UI and other components out there. How to get started. There's a simple Docker image. You can simply run and then there are a number of tutorials. We have a number of Jupyter notebooks out there getting you started. If you have any feature requests that you want to use, you can simply run it on a cloud service and you don't have to set up a database or anything by yourself to get started out there. So that's basically a fully managed cloud solutions. There are certain SLAs. You can either have temporary cloud instances for trying it out or also just exploring the idea of how to do it. So you can simply run it and also just exploring the idea or there are also production instances. So thank you so much for listening. I hope the kind of key points I wanted to make here. If you're building a production ML system, keep in mind that for really operational value, you should keep track of the provenance and actually the metadata of what's happening in your system. The machine learning model by itself won't really buy you a lot of value out there if you can't reproducibly train it, if you can't explain where the data is coming from in most scenarios. So I would urge everyone to really try to build a pipeline and then keep track of the individual items, the flow between those different items in your machine learning pipeline. We also have blog posts around that. Also these slides include various links to, for example, TensorFlow Extended to MLMD and all other systems out there you might want to use for managing your machine learning metadata. I think that gives us still a few minutes for questions. Is that correct? Correct, Jorg. Thank you so much. Super talk from a superhero. Oh, I'm not clear. Are you Batman or are you Deadpool? Which superhero are you? I feel like I'm more Robin helping the Batman data scientist to be more successful. I don't believe that. No, I don't think you're the sidekick at all. So yeah, you're right. We have a few minutes. You have a few minutes left. So let me pick up on something you said right at the beginning, which is that you compared what data scientists are actually doing so let me ask you why aren't they doing what they should be doing? I don't think that came across perhaps as clearly as it could. I wouldn't necessarily say what they should be doing. I think there's just a perception if you talk to your favorite CTO or to your favorite management in some kind of company. There's often the impressions. They read like an article about oh, deep learning is the greatest best thing ever. They need to do deep learning. So they'll get a team to build a deep neural network model out there. And I think there often the perception is that they will just start out by building a cool model and once this is there kind of task solved and then move on to the next task. I think the challenge of actually operationalizing that model is really undervalued. I wouldn't say that data scientists should be doing something else, but I would say the challenge of operationalizing your machine learning pipeline beyond a research project is often underestimated. And that's also what I've seen across many different verticals across many different companies is the main challenge for really adopting machine learning and gaining value out of it that you kind of focus on oh, we have a cool model done with the effort which needs to be taken into building such model. So I think if you take that into account you can enable data scientists a bit more to focus on data model training, exploring data by having really a team around that. So we saw on one of the last slides kind of that different distinction of teams where you have like a data engineer who is actually in charge of that pipeline. So from my experience, most data scientists they come from more a mathematical background they really understand data and analyzing data very well, but they're not necessarily the operations people who will build this full-fledged distributed system for model training, for inference and of course all of that should be fault tolerant. Okay, some viewers are asking if they're building their own platform should they always include metadata in the ML pipeline or are there certain cases where perhaps it's not so important? I would say if I'm building my own pipeline I would at least keep in mind that this will become crucial. So I would I personally would say it's crucial for any pipeline because you will want to look up this one model which is running in production and now all of a sudden is throwing some errors like how was it trained and imagine this is like someone being woken up at night 3am on pager duty you really want to have like all the information available especially if it's someone new on that team or someone who hasn't built that initial model and especially if you grow if you grow your team it becomes more and more important if you have a simple like two-man team which is managing the entire pipeline all of the training models they probably will have most of that context like they will have the metadata in their head most most of the time so if it's some very small scenario or some research project where really this is not meant to be operationalized then it's probably not that important but my general recommendation would be keep it in mind for anything you build out there. Alright so there you have it. Anyone wanting to know more please get in contact directly with Jörg. We are unfortunately running a little bit tight on time so all that remains for me is to say thank you very much for this fascinating talk Jörg and we'll stay in touch so thanks again. Thanks a lot.


Welcome back to the Sportsmax zone, newly appointed interim head coach of Rebecca's Reggae Girls, Xavier Gilbert admits it will be a difficult task as his team begins preparation for their two upcoming Konkika Gold Cup qualifiers. Gilbert believes that given the limited time before the team's upcoming set of fixtures, the preparation will be primarily mental and tactical. The Reggae Girls are scheduled to face Panama on the 25th of October before squaring off against Guatemala at home on the 29th. Gilbert, who served as an assistant to former head coach Lawn Donaldson, was appointed to the role following the non-renewal of Donaldson's contract. Now Gilbert was scheduled to join us to have a discussion with us on the show today, but he has since indicated his unavailability. And I guess Mariah and Ricardo can help me break down what faces the Reggae Girls at the moment. One of their biggest fans, Mariah Ram-Harak, of the Reggae Girls, but they're in a spot of bother as far as preparation is concerned for the inaugural Konkika Gold Cup. We've all welcomed the advent of a Gold Cup for women and now the Reggae Girls appear to be short of preparation for this.  And Lawns, because of the few days that we have left, it can only be mental and tactical because there are a couple days left, we get ready to face Panama. And I think for me, having Xavier Gilbert, who is, who was a part of the Lawn Donaldson setup, it has positives for me, especially because he would be a part of the plan that the ladies were accustomed to, you know, he knows the same. And I was looking forward to talk to coach to find out, you know, if he's going to deviate from the plans that they were going with initially, because I think that would be a, it would play a major part in what happens to the girls and the chemistry around the team. Because if coach now continues on the path that they were going under, and I'm talking about strategy, style, tactics, all these different things, then the ladies would not have to adjust differently. It's not a new person. It's not a new setup. So for me, I'd be okay with that because there's some form of continuity. Now that we can't speak to coach because he's unavailable. I still have those questions in my mind, Ricardo, because all those three of us sit on the show every day. We do things differently. So we may have similarities, maybe in the way we edit something or maybe not. And for me, I would have loved to hear from coach, is it that you're going to stick to what Lawn Donaldson was doing, just to ensure that the girls are continuing something they started? Or are you going to come out with your new style and change things up a bit? Yeah, I take the point, Mariah. But I think Xavier Gilbert has some bigger issues at this stage. As it is at 446 Jamaica time on this Friday, the 20th of October, a squad has not been released for the reggae girls and their two matches. The Panamanian squad for the 25th has already been released. And based on what we're hearing in the local media, a number of the first choice players, those who went to the World Cup, those who competed in Olympic qualifying against Canada have said they are unavailable for the matches that are upcoming. And so a bigger issue for Xavier Gilbert as he takes this interim head coach position is where he is going to get players to take part in this two-match series. And it could be a massive blow for Jamaica's women's football if come the Gold Cup next year, they are not part of it. And these two matches will go a long way in determining whether the reggae girls will be involved in the Gold Cup next year. But massive issues for coach Gilbert taking over this team. Yeah, you know what, I heard the technical committee chairman Rudolph Speed on local radio this morning on hits FM. And there is clearly an issue that you just pointed to Ricardo, because at the moment we are unclear as to the number of girls that will be available. When I say the number of girls, I mean the regulars who competed in the World Cup. And it seems that quite a few of them have acknowledged or stated their unavailability and speed appears to be quite upset about that because there is a contractual arrangement as far as he's concerned for them to be available. He did suggest that there are a lot of other players that are keen to play and qualified to play. So he isn't that worried that they won't have a squad. But the fact that they haven't named the squad yet is, as you said, Ricardo, a huge problem because the match is five days away. And to Gilbert's job, how does a coach prepare a team for such important matches, two of them coming five days apart, and he doesn't even have his squad to work with. And Mara mentioned earlier on that, yes, he had been assistant to Law and Donaldson before, so he had been accustomed to working with the girls. But it seems quite evident that a lot of the girls that they had worked with may not be there. So he may have a new slate to work with. And he did suggest, Rudolf, speed that they have about 18 or 19 players that are available to play, but they wanted to get a few more to complete a 23 setup. But there's obviously a problem there that is huge and it needs to be hurdled in time for this opening game. Yeah. And they're not starting on a good foot with coach. It's very, very unfair this situation that he has found himself in. What I will, and I want to add this, what we've seen from the regular girls before is they bind together. And the letter that we saw with all of their signatures recently is one example of that. So you heard on that interview that they have 18 players available. Let's just say the senior ones may not have gotten the chance to speak to them as yet because the ladies, they all feel the same way. They have always expressed as a group and as a team, as a unit, all the different issues they're having. It's not as if you alone have this course that you're championing. I'm on my own. They have really always bind together and spoke out as a collective unit. So what I will say is Lance, as of this morning, he could say he had 18. We don't know what's happening during the day today in the communication. You know, they all feel the same way. You know, I have to be very careful how I say this because I don't want to review us to get the wrong impression of what I am going to say. No, it is clear that the Jamaica Football Federation has failed the regular girls and the regular girls have every right in the world to be upset with the Jamaica Football Federation, whether it be the financial issues or it is the issues surrounding the coach, not renewing the contract of Lauren Donaldson and then not having a timely plan in place as to how you would replace Lauren Donaldson. So the regular girls have every right in the world to be upset. But from my standpoint, Lance and Mariah, one of the things I like to look at whenever I'm making a massive decision is what is the big picture here? The big picture for the regular girls should be moving women's football forward. And so everything that they do as a unit should be having in mind that they want to move the game. They want to move women's football in Jamaica forward to the next level. You've already had historic performances by getting to the World Cup. And so the question I would ask by making ourselves unavailable for these two matches against Panama and Guatemala, are we advancing our overall goal of moving Jamaica's women's football forward or are we more likely to hurt that ultimate goal? If the answer to that is yes, you are more likely to hurt the ultimate goal, then for me, this strategy is therefore not the right one in terms of making yourself unavailable. I would be looking at some other ways of bringing the point across that what is happening is unacceptable. And I know I'm going to be told that the girls have tried before, before the World Cup, they wrote a letter. One of the things I don't think that the girls have done enough of, yes, we see letters, I think two or three of them now.  Yes, when they got to the World Cup, we saw a lot of stories coming out of the international press. But how many opportunities has the local media had to speak with the girls and hear their side of the story? And I would like them to think about it from that standpoint, because in my opinion, Lance and Mariah pulling out of this two-map series, goal qualifying series does not help Jamaica's women's football. And in my opinion, will hurt the game in Jamaica significantly more than it will help. And I understand what they're trying to achieve. And I understand that they feel hurt by the way they have been dealt with by the Jamaica Football Federation, but this is not the way I would have approached it. Well, I'm hoping and being mispositive here that come October 25, these payment issues are sorted and the girls are ready to go because I mean, Lance, we've spoken about every time we talk about the reggae girls, they do something well on the field. And then we have to come back on this set again and talk about all the issues that happen off the field. And I feel like it's so demotivating. It's so discouraging. I had the opportunity to sit with Marlo Spetman for a 25-minute interview. She hasn't played for the reggae girls for some time, but she really looks up for that spot. She's working on it. She really wants to represent, but sitting on the outside and hearing everything that's taking place back home, maybe she got away. You know, when you think about it, because we're always sitting here discussing things that don't concern on the field performances. Everything else is getting in the way. Yeah. Confrontation between athletes and administration is nothing new. And I hear you Ricardo and you've put it accurately in the sense that from the standpoint of advancing the game, the move that the girls, whichever of them making themselves unavailable, is not the best for moving the game forward. Having said that, it is clear for the years now that there is a combative, confrontational even relationship between the reggae girls and the Jamaica Football Federation. And I know sometimes the man in the street often takes the side of the athlete against the administration. And a lot of times that's emotion and that's just how you feel about your sports stars. My experience in this business tells me that it's not easy to deal with elite athletes. They have personalities sometimes that make them very difficult to manage. Having said that, the persons in charge of managing them have the responsibility to manage them efficiently. And that job at the moment lies with the Jamaica Football Federation. And I think it is in their hands to mend the relationship with the girls. And in a situation like this where we have seen the constant confrontation between the reggae girls and the Jamaica Football Federation, it is in the Federation's best interest to maybe have an arbitrator or something. I understand, Ricardo, what you're saying. They have to sit down and talk and thrash out the issues to advance the game. Because from where I sit, the girls are frustrated and they have reached a point that even though they may recognize that they are hurting the game, they feel it is untenable for them to interact with the JFF in a compromising way or in a consolation way to say, all right, this is Jamaica Football, so let's put Jamaica Football first. They are at a point where they are given up. And I think Lord Donaldson was at a point where he had given up as well. Because he had been criticized from the standpoint that people were saying, how can you criticize your employers and still expect to hold your job? And I guess in normal business practice, the Jamaica Football Federation probably were in the right to not renew his contract based on his outward disrespect to his employers. But I am suggesting that Lord Donaldson may have been frustrated himself and didn't care whether he was fired or not or not have a contract renewed or not. And it's the JFF's business to manage football. And it's difficult. I know it is. And because they are castrapped as well, that brings other issues with it as far as managing the football. But the JFF's performance as administrators of football isn't impressive. I would give them one out of 10. And because their reputation based on track record is so poor, it is natural that the average man in the street, the sportsman in the street will more feel sympathy for the girls than the JFF in a situation like this. And that's unfortunate. Yeah. From what I've heard on the local radio stations at least, it seems to me that it's split in terms of the support for the JFF and the support for the girls on this issue. I want to say this though, because I think there are a few specifics that keep getting thrown around. So one thing, for example, I get the impression that there is a feeling within the JFF hierarchy, definitely at the level of the technical committee, that the girls want to demand and select who the coach is or who the coach is going to be. And that the Jamaica Football Federation may be saying, that's not something we can allow as a federation. And they're correct. Yes. And they are correct. That may be fine. But if you're going to take that line as a federation, then you have to be very quick and decisive and competent in how you deal with the matter. So when you do not renew the contract of Lauren Donaldson, you must have your plan in place ready to be implemented. There is no way that you do not renew Lauren Donaldson's contract. You have no plan. And you get to October 20, and you have named an interim coach only a day ago, and no, we cannot get a squad for the game. Which is the point I'm making, Ricardo. I understand their difficulties. But if you are going to carry out your business of management in an efficient way and get buy-in from your stakeholders, then there's a level of competency that you have to exhibit to make people believe in what you're doing. And I think the JFF has failed in that regard. But I understand the point that you're making, because there's been this growing feeling that the rigor girls want to run the program. And if you're the JFF, you can't allow that. You can't allow that. But how you deal with it is also extremely important. And again, maybe the Jamaica Football Federation has not done the best job in dealing with that particular issue. But you're putting it mildly, Ricardo. You're being kind. I am a kind fellow. They always say respect is earned, and I think if they handled things differently, we would not have been in this position. Let's take a quick break, team. Yeah, we have a lot more to talk on the sports matter. By the way, before we get out of this Cool Boy football segment, remember we were speaking about the rigor girls earlier in the show. Since that discussion, Coach Xavier Gilbert has released a squad for the Conker-Calf Gold Cup qualifying matches. Let me quickly go through this. Now, the release from the Jamaica Football Federation says let's see three players, Destiny Powell, Chrison Chambers, and Chanille Buckley have been recalled from the Panam team now in Chile, and will join the delegation in Panama. The squad is now scheduled to assemble in Panama City on Monday. Training is set for 5pm. Now, I've just gotten this squad, but I don't see any member of the World Cup squad listed here. Let me run through quickly. So, Chrison Chambers and Javonte Jones are the goalkeepers. Then you have defenders, Lauren Reed, Malikey Daze, Zoe Vidari, Neva Gale Abel, Chanille Asher returns to the rigor girls setup. She was part of the unit in 2019. Alikey Keyen, Israela Groves, Rikaela Walker, Marla Sweatman, another player returning to the rigor girls setup. She was part of the 2019 squad. Davia Richards, Michaela Daze, Chanille Buckley, Natalia Atkinson, Destiny Powell, Melissa Johnson, Sidney Street, Jada Hilton, Pelaya, Alia Morgan, Shanane Nelson, and Tayisha Nelson. I think what is very obvious from reading through those names, Lance and Mariah, these are not the players that we have become accustomed to seeing in the rigor girls setup over the last year or so. Yeah, which confirms that the problem is massive. The deadlock between the rigor girls and the Jamaica Football Federation is huge. The only two that stood out is Chanille Asher and Malou, because I interviewed her a few weeks ago. Yeah, and they're returning to the squad. We'll take a break. We'll get back with more on the Sportsmax Zone.


So there's been a lot of leaks over the past year that Spotify is coming for TikTok and all sorts of other features. And anyone who follows Spotify knows that you constantly hear about new features for sometimes years before they happen. But as of today, we can finally see and touch some of these new features. So it's time to get musicians up to speed on what that means for them, you know, the people who actually make this platform worth using. So today, Spotify hosted its second big announcement event they call Stream On. And it was a star-studded affair and featured that creepy robber baron guy who owns the company and exploits musicians, and a few people who are dressed like they may recruit you into a pyramid scheme, which it could be argued is accurate when it comes to Spotify. So as fast as I possibly can, I'm going to tell you what the new features are and what they mean for musicians, as well as some things I learned watching it that would be of interest to musicians. So while the event was mostly filled with a comical level of Spotify saying, we listen to musicians, they told us to do blank. And shockingly, it wasn't the number one thing musicians actually tell them, which is pay me more. They guess that the crowd like a guy who's been mainlining Andrew Tate relationship advice content into their veins and instead pretended what we really wanted is features that will help the top earners on Spotify. But let's get into that because it's not all bad news. So the big news is the new homepage, which is divided into three sections depending on what you want to get into music, podcasts, and audiobooks. It's nice to think of listeners this path instead of pushing podcasts as hard as they used to, since we all know they were doing that so that they can drive down, listens to music so that they can drive up their profits since they don't pay as much for podcasts to be played. Anyway, if you click the music tab, it's going to give you what has been described as a tick tock like feed. As of now, mine doesn't look like tick tock in any way, except in the way that I was pleasantly shocked by how good the recommendations of what I may want to listen to were since they range from the unfamiliar to records I love, but haven't bumped in a second. It seems that this is rolling out to different users a little at a time, so you may not have it quite yet. But if you watch Spotify slick demo, it's filled with music video clips or clips of videos where artists talk about the song. But obviously you want to know how you do this. And the answer is if you head to Spotify for us, you may have a video tab where you can put up 30 second videos that are linked to your profile and album pages. Now obviously this is so new, no one knows what works video wise, but you can start with music video clips, cannon videos of you talking about your songs, since they seem to be tied to being a story that goes with a song or a playlist from what I could see so far. I will obviously make a video update as I know more. But this seems important since Spotify's language is that they will be using this to recommend music to users. As we always see with these types of features, the people who do it first usually reap the biggest rewards. So I had to Spotify for artists and see if you have that video tab and put up some if you do. But here's some really good news. If you already have canvases for your songs uploaded to Spotify, those will be used in this feed as well. But I know so many of you ignore that. And if you do hit the description since I have a video on how to do canvases in there and you should really get to work. Next we have Spotify's new countdown feature, which allows you to put a countdown on your page. So any fan of this channel knows I think for DIYRs who don't have a massive fan base doing countdowns teasing or the ultimate cringe, big things coming posts are truly the biggest waste of time since there's so many options for people to consume that if you actually get their attention then say in seven days you can have this well they're gonna be mad and you wasted a chance to do a promotion with a song that may have turned them into a fan because they're not coming back in seven days. Great work. But the big huge artists with massive fan bases are different and that they need to get everyone who ever liked them to know that they have something new and hopefully get them to consume their new release. So countdowns are helpful for them. Now since Spotify is not forcing this like a post where people are looking for value and to be entertained like on TikTok and potentially get you unfollowed I actually think these are pretty good because if a fan is going to your page and they see this well they can find out a lot more and they can see some things that they can consume already since oftentimes you have track listings that are blank here for your future release that will then show up but they can consume the songs that already exist. But sadly like most features in Spotify this is being rolled out slowly to users but if you see it is available I would implement it as it's a great way to show your fans they should stay up with you and to explain this a bit more these countdowns are pages you could point to where you could push presafes yes presafes in Spotify a damn miracle which will probably be a game changer but yet again another thing we'll probably have to discuss after we see them in action a bit since Spotify claims that 80% of the people who presave you using them then stream the record on release week that means these are pretty important but on these pages you'll also be able to allow fans to watch videos pre-order merch and preview track lists so this is pretty cool and helpful even for DIY artists speaking of Spotify is also attempting to ingratiate themselves to the musicians they don't pay well enough through their fans first program again which for those of you who've forgotten about it's the one that sends emails and notifications to the most enthusiastic fans of ours to alert them to ticket pre-sells and exclusive merch as well they're going to push your merch and concerts even more rather than hiding them at the bottom of your profile as they'll now appear more widely in Spotify's app on its now playing screen you know when people are actually listening to the artist who has tickets and or merch available so if you have a merch store through Shopify you can sell merch on Spotify I know it's confusing and if you update your tour dates on soundkick they'll appear on Spotify and get served oh yeah and for those of you who have enough listeners to use the marquee tool which I really see doing lots of good for us getting fans to listen to their music but is yet again another gross payment from musicians to Spotify that shouldn't exist well the good news is they've expanded it from just being in the US to the UK and Australia as well and lastly they announced they were expanding their discovery mode and bring more features to it so more people hear artists in the program which is getting tons of artists I know way more fans at the expense of losing 30% of their royalties of listens that happened while in that mode if you don't know what discovery mode is and want to learn more and how you can promote your music and then well just last week I made a video on it and you can watch it right now since it's on the screen thanks for watching


During all my hours of playing Among Us, there's been too much violence, too much fighting, too many hearts broken. It is time for a new era, an era of peace! Ahem, ahem, ahem, sorry about that. Something caught in my throat. Today I'm gonna be a pacifist in Among Us. Hey there friends, my name is Kevin and welcome to the channel. Oh, would you look at that, it turns out peace was always an option. Okay, well I am gonna be called peace for now and I'm gonna try and just be a complete pacifist and Among Us with some random people online and see how it works out. Okay, jumpin' right into it as an imposter. Okay, I'm gonna stick with Red the entire time. Okay, Red is obviously innocent because he is scammed and now it'll appear as if I am completely innocent. We're gonna be buddies, they'll never know it was me. I'll have the perfect cover story.  Okay, this is an issue and the thing is I saw the body and I was like, this is the part where I could kill him but that's not what I'm doing. Peace knows I'm innocent. Yellow's not being very convincing. He's like, I was doing my task. Maybe he just doesn't want to lie because he was doing his task. He was killing people. Bye. He was too violent for my liking anyway.  I really feel like I gotta kill someone. All right, look, I'll ease myself into this because this is new territory for me.   Okay, there we go. I saw White pass admin and kill him. The body was right outside. It was most likely him unless he didn't see body. This is so difficult. Like they're gonna get ejected and then go be like, well, peace.  Oh, sweet Jesus. This is so, oh, this is so difficult. I kill them right under Red's nose. Red's got to be so confused right now. Oh god, emergency meeting. Guys, it's not me. I have been with Red entire game. Okay, good. He came from storage through admin and body was right there. Oh my god.  I'm trying to pull off. If he was, he could easily kill me. Yes. Bye bye purple. How is this working so well? Red must be so confused. I don't think I'm going to be able to win this, but I think just the feckery going on in general is a reward enough for me. Oh no, they caught me. Wait, no, it wasn't me. I don't think I can, I don't think I can get out of this. It turns out peace was never an option. Bye bye peace. It was fun while it lasted. That is because I had impure thoughts of killing. That is the only reason I lost. If I didn't kill, I would have been 100% fine. I shouldn't have killed anyone. Lies, deceptions, the hand played us all. I am sorry. Just wanted peace. Okay, much easier to be a pacifist if I don't have the option of killing. Red must be like, dude, no, not you again. Even though there was an ultimate betrayal, I feel kind of a friendship blossoming. He feels like the kind of friend who robbed my tummy when I'm sad, or let me coddle his bosom if I feel lonely. Oh, he left so much for his bosom. Oh, defeat, okay. Well, that was fast. Half the lobby just left. Why does no one like peace? Can we not kill in the next game, please? All right, hopefully that works out. Why want peace? I think my new friend might be the Chloe B. They just seem to get me. Like they also wanted peace in the chat. Black's like, I understand you.  Kill Cien and bring peace to the ship. The only way we get peace is with murder. And no killing rule has sadly been broken. He knew the rules. And so do I. It's a murder, but you know, we like to keep things light or a corculean ship like that. Oh, well, back. All right, well, I'm next. I'll pack my bags. Not that it'll be any good to me out in space.  Guys, how many times do I have to repeat myself? No killing, please.  They didn't even attempt to defend themselves. Oh, great, now I'm with him. And I know who threw him under the bus. I think he's going to throw me under the bus this time. Just give me up. All right, pacifism mode engage. Kevin, be one with your fellow man and woman. We will bring peace to space. Blue just watching us. We're not going to do anything blue. I'm a pacifist. Black innocent.  They are a couple. Ying and Yang will bring peace to the ship. Who are we voting? Pink? It's like for a pacifist. I'm pretty fast to yeet people off into space. This is still pacifism. This is democracy. Come on, black, make a call. Make a call. What do I vote for? No, we're going for a skip. Okay, this is not good. Like ideally, I want people to be voted off. I don't want rational people. I want people just throwing out accusations like I do. It's the end. For no reason would be really helpful right now. I actually know he's with me. Someone else. A different color. Any other color. Oh no, don't go in. Don't go in. Don't go in. Is there going to be a body in there? Oh, okay. We're good. We're good. I was sure that my little friend here would get caught out. Black innocent. Every time I just got to reaffirm it, just so I can get them to repeat. White innocent. Every time I just want that drummed into people's brain. That white innocent. Sian was near cafe safe also. That's not very convincing because he convinced Medbay. So they're going to suspect him. So I'm just trying to say that.  No black. Oh no. Oh no. They're throwing him under the bus again. And I have no part in it. I'm just standing there watching. I'm driving the bus. Just passively forward, refusing to break. Black. What do I do? I don't know what to do. Like how am I going to win if my partner's dead? But on the other hand, I don't like that he's killing people. That's not very pacifist of him. Pink or skip black. I'm just going to ignore the fact we're voting for Sian. Pink is a guilty color. And I'm just like, good enough for me. Okay, that's another person gone. Oh black is going to be devastated when they find out it's actually me. Oxygen depleting. No one's going for the other side. This could be an easy win.  I don't think he can make it. The pacifist's win.  Well it wasn't me that pulled the trigger. I had nothing to do with it. I was over there being a pacifist. White. I only wanted peace. You follow the no killing rule though. I respect that. Please don't kill guys. Game is a lot more fun if it's just the cute mini games. That's why the game is popular. It's not like the psychological thing or anything. It's just the little mini games. I love connecting wires. It's the best. A peepoo left the game. That's almost pee pee poo poo. Anyone who hasn't watched my other Among Us videos is just like, that was a weird thing to say. Please stop killing. This loss of life is tragic. They're not listening to me. They don't want to be pacifists in this game for some reason. I'm like self report. Couldn't live with the guilt, could ya? Black said skip. Even on my votes.  What am I doing?   And then my brain's like, yes. Yeet them into space. This is true peace and freedom. Look at that. Peace in our time. Six votes for skipping. I'm wearing these little clothes. That's kind of cute. You know you're good friends when you share clothes with each other. And I supposed to be able to see the asteroids when you see them? I must save the visual tasks off. Hmm, black wouldn't betray me would they?  They double killed us. I thought we agreed no killing. Pieces of lie. There's only passion, true passion. We can't strength through strict victory. What are you drinking? And can I have some? True victory. Our chains are broken. Never heard of it. More of a whiskey kind of guy myself. Right, vote orange. Black said, I'm not accusing anyone. But orange ran away from reactor in crisis. And then I'm like, I vote orange. Like black said, it was 100% to him. Well, black has a lot to answer for now. Oh my god, it's black, isn't it?  Yes, it is. It's black. The lights were off and he knew exactly where I was. Following me along. Oh my god, it was me. It was me and black all along.  Wow, good job, black. Peaceful victory, no killing.  I hate everything right now. Do not hate peace.  It'll be amazing. Oh, what's happened to orange?  Public, no, don't say that. Join us. Be a pacifist and have fun times with us. Orange, come with black and I. You are welcome to the flock. Are we starting some sort of space cult? Thank you, white. But I'll stand next to emergency button. Wait, what does that mean? Oh my god, there's so many plot twists. Like the villain of the peace was orange. He was getting angry. And now everyone's like, we love you orange. We love orange. Orange, you best get ready for the best group hug of your life, mister. I'm just scattering around him. Oh, you left. You didn't like the human contact. To be fair, now is not the time for group hugs in this current climate. Oh, I'm dead. That means defeat. No killing. One piece. Oh, God left the game. Well, if God has gone, what chance is there of peace now? Oh my God. God is dead. No wonder God left the game. God gave up on us. Could be green. Look, all I'm saying is there is a killer and his name is killer. Good enough for me. Might have been good enough for me, but that doesn't mean it was the truth. Unfortunately, he was acting short. He called us a morons.  Ah, yes, my medical scan. I'm glad you noticed I have an ab. One single ab. Hey, anyone checking out my one pack? I will never trust anyone but peace. I trust you 100% black. How does this always happen? Always end up with like a body system going on with a best friend. This should be like a subscription service. Best friend for a day. I've gone through so many now. Oh God, I think we're moving beyond friends. Black took off all the clothes.  It's brown. He killed my best friend. Good defense. It's brown because white wouldn't kill his friend.  I'm pretty sure they know it's not me because even if it were me, I wouldn't kill him. It's purple. I saw him egging the orange guy.  No, what does that even mean? Oh my God, it's happening. Me and black. Me and black. All right. How are we going to do this? Oh Jesus. Okay. No killing whatsoever. Pretty sure yellow came out of vent. I saw the same. Okay, I trust you. That's yellow. I'm sorry. Gold gone. How do we keep killing without killing? Call it. Call it. Come on. You have the power. You have the power to call the emergency meeting. Come on. You got to do this, black.  Black ran away. Black, no. They don't get me at all. Wait, I got it. I got it. Come on. Look at me vent. Look. Look. There you go, monkey.  You should call an emergency meeting. Please. We need to talk. We won. Oh, did they start killing? Why would you do that? We were supposed to be pacifists. Black, please. Peace was an option. Oh my God. We got another chance. What are the odds?  Oh, black is killing again. I got to try and minimize the loss of human life. I think pink went in elect. Yes. Yes. I think pink go with me. Come on. The only way to remain a pacifist and preserve human life is by killing people. Okay. There goes one. Oh wait. That's just a victory. Awesome. Another victory for the peace force, even though it's only half a pacifist team. I didn't kill. I just accidentally stuck out my leg and they tripped. That's fine then. Oh my God. I'm imposter again. Three times in a row. Do not kill him. We are pacifists. No. No, no, no, no, no. Oh, no, no, no. You're not taking me down with you, mister. Bye-bye. Don't kill my friend ever again. He should have known that wasn't an option. God, they're accusing me, but I'm saying it's not me. I just caught pink. He accused me for no reason. Yes.  No, that's just his name. He's saying white didn't do anything. It's purple. I almost got voted off. I got the purple vote and they yellow voted for me. Oh, I'm in major trouble. Oh God. Now they're calling a vote. What do you think? So it's either piece or yellow. And I'm just saying that I caught pink at the start when he was AFK. But then he's saying, because right is my brother and he's innocent. I'm like, how am I supposed to win if they're playing together? I'm willing to guess yellow then. So I am confirmed innocent.  God, it's so mean, but that's his name. Okay. So I've got everyone, but the other two off. But the problem is their brother isn't playing in the same room. So they know that it's me. I'm going to turn off the O2. You're going to need your brother to start that issue. I'm still a pacifist. There's just an air leak. And look, your brother isn't moving. Would you look at that? Oh God. Oh God, he's moving. Oh dear. Oh no. Oh, that looks like you didn't make it. What a weird victory. I wasn't going to use the oxygen. I was going to go down to vote. But since they're playing in the same room, like there was no way I'm convincing. So I kind of had to just, you know, the old window and leave a bit of oxygen out. All right. Well, more friends were made. And no one was harmed in the making of this video, like by me directly. Other than the people at the start. Like I killed a few people, but then I repented. I turned over a new leaf. This was really fun. I'm just annoyed they were brothers because I feel like I could have gotten the victory there. I think I played it quite well, but I just couldn't. I couldn't get it because they were sharing info. But yeah, I hope you guys enjoyed the video. Let me know if you want to see more among us. I am pure addicted to the game. And if you have any cool little unique ideas, I'd love to hear them. But yeah, I hope you enjoyed. I appreciate you watching as always. If you want to check out more of my stuff, I post every single day. Also stream over on Twitch. But for now, I'll just say thank you for watching. Appreciate it as always. And bye for now.