Wel, wrth gwrt am bwynt y gallwn gynllun ychwanell ar gweithio'r gweithwyr cynterdurion. Rwy'n ddysgu'r gweithio gyda'r team ar Glencwr Golf. Un o'r gwrs ymlaen, teulu'r gwybod y gwybain ei gael gwympasol. Erbyn yn y gweithio'r gweithwyr, ond wedi bod yn ymlaen i chi o'n ei ddim yn y cyflodd yn ei gweithwyr. Wel, mae'n ddilema yng nghymru. Mae'n gweithio'n gweithwyr ei ddweithwyr i'r gweithwyr. Mae'n gweithwyr. Mae'r un oed o'r hyn, ond mae'n gwneud y ddwy'n dweud yma yn ymwyaf i ddweud y dywedd. Felly mae'r ddweud o'r ffordd a'i ddweud o'r ddweud sy'n cymdeithas. A'i ddweud o fynd wedi'i ddylemiadur am y ddweud hynny'n ddweud. Mae ymwneud fynd i ddweud o'r ffordd a'i ddweud y ddweud yma yn ddeud y ddweud yma yn ddweud y ddweud. Mae'n ddweud o'r ffordd am y ddweud o'r ffordd ar y pryd, A dwi'n digon yw cael i ddweud o'r adref yn cyflawni newydd, mae'n dwi'n ddweud oherwydd yna, wedi digon y profiol, a dwi'n o'n dweud o'r adreffau'r adreffau yn y ddweud i'r adref, a'r ddweud i'r adreffau. Dwi'n gweithio ddweud o'r adreffau. Dwi'n gweithio allwn o'r adreffau y gallwn y dywedig yn y dyfodol, ond mae'n gallu ddweud. A dwi'n digon, mae'n ddweud o'r ffordd o'r ddweud. Ac mae yna byddai'r ffordd sydd yn gweld, mae'n ffordd ymlaen na hwnnw, mae'n gweld yn unig yn ystod yn ystod y tîm. Mae'n ddweudio eu ddweudio'n llwyddiol ac mae'n cael y gallwn gwaith yng nghydfynu yn y ffordd, i fwy porfynu i gyd ac yn y ffordd. Ac mae'n gwybod o'r ddweudio'n gwybod a'r ddweudio'n gwybod i ddefnyddiaeth y big deal of confidence. A'r confidence, efallai, is as much in the shaft as it is in their head choice. That was with the stealth. I'm going to switch up to the PXG and see if I can replicate the similar sort of shot type. It's almost exactly the same shot. It's probably landed in a very similar position, but that will also be key factor of what we look at today to very, very similar strikes, exactly the same shot shape. And I will be interested to know just which one of these has gone further, because that will be largely due to the spin as opposed to the ball speed. That's a really interesting start from that first tee shot. They are literally side by side. And what I thought might happen was slightly wind into is that their HD might be a little bit spinny. That's one of the concerns that I have and something that I'm really interested in finding out. Pushing dry ball data to one side and seeing what happens in reality. When, like I said, we've got some wind in two and also playing the same shot downwind, what happens. And in this case, probably a little bit surprised that the lower spin model of the PXG didn't kick on a little bit further. So that's an interesting start. So we've got the first tee shot out the way, a little bit of a nervy one, not warmed up and a bit guidey, like I said. But the next challenge is a par five where not only have we got to find a fairway, which you'll see very shortly is, well, tight to say the least. But we also want to liven up a little bit in terms of club head speed. So things change and what happens when that extra bit of effort is applied. We'll start off with the stealth. And for the interests of transparency, my line is the tallest tree that you can see in what is effectively the middle of the fairway. I'll be going to the left tree line and trying to hit a bit of a cut. I've played this yesterday and the shaping of the shot means you've got to be tight down that left hand side. That's what I'm trying to do, but can we? Well, do you know what's a really solid ball? And the shame is that it started just a little bit too far left. It is cutting back, but I think it'll be tight on the tree line. But what I will say, totally different swing from the first tee. You know I really like this shaft that's in this stealth HD and the stability that it provides and the kind of tempo that I'm comfortable with is also one of the key factors that will make my decision one way or the other. So maybe not ideal, but certainly everything I got back from the club head and the shaft and the swing was what I'm looking for. I'll try and make a slight adjustment so the PhD has a little bit in its favour in that maybe I was a little bit angled down that left hand side. So we'll make the amendment, but this thing has got a lot to beat. I'm really favourable, as I've said, on many occasions towards the stealth two driver, the HD. So what can this thing do? Oh, that was a much better ball. That was the line that was intended in the first place. No curvature this time. It's not the weak sort of fade shot that I hit off the first tee. And again, I was fit for this driver and this shaft just a couple of months ago and it's that stability that this offers very similar to that pro-force shaft. This is a Graphite Design shaft VR6 and again very much suited to my swing, majorly important. So gone after a couple if you like. And again, not a lot to split on both. Today's video comes to you in association with Long Term Channel. Travel partner Glenclaw Golf holidays and we are at Kintedam Arena in Cascai, Portugal. The hotel and golf course have been a superb place to play and stay in the Portuguese sunshine and the proximity to Lisbon Airport and nearby Cascai. Make it the perfect European golf destination. So if you like what you see in today's video, then make sure you click on the link in the video description below or head on over to GlenclawGolf.com for more booking information. I've already said this isn't a comparison video because there's too many differences between these two clubs that would mean that a lot of you question why I would put these two together in a head-to-head format. Loft for example, nine degrees on one, 10.5 on the other, being the PXG. Then we've got these shaft differences again that for all that similarities, ultimately there's a different shaft in each. But one thing I'm learning is that I hear things like if you play a 10.5 drive ahead, then you should always play a 10.5 drive ahead if that's what you've been fit for. Let's say in the PXG. But I don't believe that to be the case because the ball flights of these two drivers are very, very similar, but there's one and a half degrees difference in terms of the loft. And that's because of the way the club is weighted and what it also assists you with in terms of launch. So club heads are made very, very differently and the loft and the weighting system will mean different things. So I think it's really important to notice when you go into a custom fit or when you go to buy a new driver, test the head, test the shaft combination and make sure all those things align and get the kind of thing that you're expecting to see and not maybe one that you've gone in there with that thing that I hate, which is a preconceived idea. Now another part of the decision making process, I don't care what anybody said is the way a driver looks. Even from shelf appeal, I've got to like the look of something before I decide to pick it up and give it a go. Now these are very, very different in that looks department as well. And I know that if you're considering one of these, I'll at least give you my opinion on the two. I'm going to start with the PXG and one of the big differences for me is the crown. And the crown is probably all important bit at the end of the day. That's the bit we're looking down on at address and it's this anti-glare matte finish. Little bit of accents around the edging and then this raised profile at the front in terms of the face. X marks the spot very simplistic. Really, really like this thing at address. Cannot knock it at all. Ticks every box for me a phrase that I like to use but it really does. I don't see any offset at address. It's a very neutral driver in terms of that address position as well. So yeah, everything I like.  It's the gloss finish and again I prefer what they had with sort of SIM 2 max and again that sort of muted matte finish that they had in there was superb. So I wish they hadn't of gone back to the gloss finish but albeit it's very minimalistic, let's say, minimal markings apart from just a bit of a red accent around the backside which in my opinion makes the head profile just look a little bit smaller and again that's something that appeals to me. This is a HD driver which is a draw bias and there's a suggestion, there's a bit of offset there. I've got to admit it's not something I really notice at address so I'm not put off by that at all but they are very, very different drivers and in the address position I would be leaning towards the PXG from a visual perspective. But the key factor throughout all of this looks and loft and shaft choice and all the rest of it, the big key takeaway is always going to be this idea of confidence. And the thing that I have is I have faith in the stealth driver and shaft combination and because there's been some longevity in terms of performance I've played with it for the last, what is it now, 18 months then it's confidence straight away. I've drove well with it. So PXG's got a big job to try and shift this. Right. Anyway, back to performance and let's see if anything can persuade me to let this one go and make a change. Again, my preferred choice now would be to easy swing down that left hand side. Hopefully with a little bit of cut.  You know, the thing is about this HD driver, HD head, is I'm not sure if it's the longest driver I've tested. In fact, I'm pretty sure that it isn't. But what I do know is that HD elements and the weighting system that is sort of helping me if you like, not cut that ball too much. That's the simplest way of putting it. Certainly seems to be effective. So I can hit my cutty swing. It moves a little bit, but not a lot. So next up, PXG, can I hit a similar shot down the left hand side? A little bit of cut. Well, the answer is yes.  I can't deny it every time I hit it.  And let's just see exactly where those two are in terms of difference because maybe it's just a sound thing. Carbon wood face is a very, I think it's a very soft and muted feel. So this sounds faster. And maybe that's just deceptive on my ears. What has it really done when we get down the fairway? Right, it's important to note where the T is, which is back in the tree line there. You can see those raised positions. This is where the PXG ball finished. I'm just going to walk and hopefully, Han, you can pick up where the HD ball finished. And I did have an idea that we've probably gained maybe 20, 25 yards in terms of distance there, extra distance. So it was as I felt. The PXG driver from my testing over this last couple of months, I think, is longer. That could be a combination of everything combined, but I certainly feel that I'm getting a bit more yardage and a bit more ball speed out of the PXG. So the question for me is what do I want? Do I want the reliability and confidence that I've gained over the last 18 months in the stealth and pro-force shaft combination or am I chasing distance? And that's a similar debate, I think that a lot of you golfers would have. An additional point to mention there is, let's not forget that the stealth 2 HD is not intended to be their longest driver in the range. So if I was looking to do a head-to-head comparison and wanted pure distance, then I'd go for the stealth plus and that combination of a lower spinning version would be more in line with the comparison I'm making. So really important to point out again, this is not a comparison video, this is personally about looking at the dilemmas that you face in choosing a driver. And this is what I'm faced with right now. Let me know down below what your current situation is, how you're looking at your or how you made a choice of the driver that you chose and why. And I'd love to hear from you what it is that makes you choose your driver. Like I said, is it about, when you're going for a driver fitting or trying new driver or you're really thinking right, I want to see if it goes further than my existing driver. If it goes further, then I want to buy it because yeah, I can, yeah, I get longer drives. Is it as simple as that? Or is it about control? Is it about feel? Is it about looks? Or I assume it would be a combination of all those things, but how many of you would forfeit distance over control? That's another super drive. And yeah, that's kicking on as well. Great ball flight really stayed over that one. And it's quite a bit of a drilled shot if you like and defies what this thing is supposed to do you so you can manipulate it a little bit. And like I said, I'm always confused with all of these videos. And ever since I've really come across this pro force shaft is just how much of the importance is on the head or how much is actually the importance is on that shaft choice because I really do feel since I've switched up into this shaft, I've never drove so consistently, have so much confidence and feel so much stability. I just leaked out a tad, not as good of a swing either. I'll just clip the tree actually. But I will say that in terms of stability, this graphite design shaft is as close as I've got to a very similar sort of feeling type of shaft. I'm no shaft expert in terms of where the kick point in these things are. But my guess is it's a very, very similar makeup because like I said, it feels very, very similar indeed. You can understand that personally it's a very tough decision, but I think I should reveal what my choice is out of these two drivers. Right, decision making time for me at least anyway. And from what I've learnt today, let's go there first of all and I've briefly mentioned it anyway. It's just making sure you get all those combinations right. That's the key. And then choose your driver in what it is that suits your game and what you're looking for. And for me, what I'm looking for is consistency, stability, finding fairways and distance isn't my overall rider right now. And because of that, I'm going to stick with what I know. So the stealth two is going to stay in the bag or the stealth into the stealth two will stay in the bag. And maybe more importantly, that pro-force V2 shaft will stay in the bag because that's the bit that ultimately made the decision just confidence and sort of history I suppose with the way I've played with it for the last 18 months. There wasn't a great deal to split them and I could quite easily switch up into the PXG to gain a few yards. And I did like the stability within that shaft, but I don't feel the need to change right now. And I think that everything that is in that HD certainly suits my game. And long may that continue. Right, that's another video filmed out at the Kinta de Marina in Cascai Port. We've got a great few days here and lots of content filmed. So make sure you keep tuned in, keep watching because we've got some interesting challenges that feature this driver. Right, I must not forget to thank Glencore Golf as well because they did a lot of work in making this thing possible. Right, all done. I'll see you tomorrow night.


This is a statistics course, but it's really a course in subordinating statistics to science The reason is because the audience I want to speak to Got into science not because of data analysis, but because of some fascination with natural phenomena like this dinosaur Or the seeds it's eating or the person who's feeding it for some reason And our interests in these things very quickly turn into Data because that's how we systematically record the variation and the interactions and dependencies among these organisms and To make sense of those numbers we need mathematics and then All of that mathematics that we've ignored since secondary school throughout college and our basic training into science becomes relevant again And even necessary We know this is true at very large scale phenomena like the climate and weather there's massive amounts of data, but But it's also true at the small scale the humble sciences the social sciences to make sense of people's relationships And their needs and their conflicts We also need lots of data highly detailed data at all scales So we can make sense of how society's evolved so you get all that data and then what? well in introductory statistics courses what you do is consult a flow chart Pick a procedure Which gives you a p-value the purpose of all the procedures that populate a chart like this are to test a null hypothesis To reject a null hypothesis hopefully the research hypothesis itself The target of your scientific investigation is Anonymous in these charts and this has led to a lot of dissatisfaction When you move on to do more Fundamental research in your field because the procedures in these introductory courses don't really address fundamental causation in any satisfying way and they're highly inflexible compared to the Very large domain of hypotheses that scientists consider at the cutting edge of their fields So what do you do instead? There are lots of options, but I've prepared 20 lectures that present the one that I like This is the course that I had wished I had had in graduate school In these 20 lectures, I tried to rethink the role of statistics in Research whether that research is scientific or it takes place in industry or it's just your hobby And the idea is that we want to fully integrate Statistical modeling with scientific modeling and scientific thinking so that they go together and they're woven together throughout the course of a study The rethinking is not the Bayesian part This is a Bayesian course and you will learn to do Bayes and we will do everything with Bayes but Bayes is no longer Some alternative way some some radical alternative to mainstream statistics Bayes is mainstream. It's mainstream in every major science every major scientific publication features Bayes now Everybody's heard of Bayes. Everybody has software and a computer to do Bayes. It's not unusual anymore Bayes is mainstream in this first lecture I want to talk about the content of the course in the framing by talking about three different kinds of creatures the first are golems The second are owls and the third are dogs or excuse me. Dax. I'm in Dax. Let's talk about golems first Our tale of statistical modeling is going to start in 16th century Prague So Prague in 16th century was the place to be in Europe. It was a very fancy center of an empire Emperor Rudolph the second I think his name was was a big fan of the arts and the sciences and Tried to lure all sorts of intellectuals or performers to the city and was often successful Like many important cities in Europe at the time there was a substantial Jewish minority and that Jewish minority was persecuted and As legend has it rabbi love rabbi in charge at the time Constructed using the Kabbalah a defender of the Jewish people of Prague in the 16th century a golem Which is a mythological clay robot that would defend the people This is this legend has an unhappy ending unfortunately because the The point of the mythology is that toying with the power of creation is risky and the golem while quite powerful had no intellect or intent of its own and Ultimately became too dangerous to maintain and was Decommissioned as it were by the rabbi that created it Prague today if you have a chance to visit it you should Uses the golem legend as a tourist lure and there are golem statues and Pastries and cookies all over the city. It's a very nice city Our interest in the golem legend is that this is a metaphor for statistical modeling the Golem is powerful much more powerful than its creators and it can do things for them that they cannot do for themselves But it has no intent of its own and unless you're extremely careful with the instructions you give it it can Injure you or the people you care for So let me review Golems the things you want to know about golems They're clay robots mythological clay robots. They're extremely powerful They have no wisdom or foresight. They merely carry out their instructions. And so your instructions must be extremely precise Otherwise you will encounter great danger and peril The same as true of statistical modeling, unfortunately each of the little procedures in that flowchart I showed earlier is a little robot a little clay robot. I use clay here playfully Mean silicon right most statistical models these days are instantiated only in computers We don't do them on pen and paper with pen and paper anymore or with slide rules And these little clay robots are very powerful They can they're very good at stuff that people are not good at this is of course why we like mathematics at computers These logical procedures can can very quickly and accurately do things that people have a lot of trouble doing So they're powerful But they have no wisdom and foresight their purpose and their results is entirely up to our wisdom in choosing to apply them and Interpret them correctly and therefore they can be quite dangerous when misused in The first chapter of the book that goes along with these lectures I I discussed this example this chart figure 1.1 of little golems and the flaws with it and I'm not totally against using Heuristic charts of this sort. I just think we have to be very careful. I don't think they're very useful in Advanced research they're useful in particularly highly controlled and even industrialized contexts These charts are the procedures in these charts are incredibly limiting. They're good in and only very limited circumstances And each of them focuses on rejecting a null hypothesis instead of a research hypothesis and if you think that Falsification and rejection hypotheses is a good philosophy of science then I agree with you, but unfortunately These procedures get it backwards Karl Popper and the philosophy of falsification ism was not Focused on falsifying null hypotheses, but on falsifying research hypotheses by understanding their implications and Comparing those implications to data and we'd like a way of doing statistical work that helps us do that In these procedures and in these charts the relationship between a scientific hypothesis and the test is really not clear Because the research hypothesis is not represented in any transparent way only the null These procedures are not always bad. They're good in industrial frameworks or highly controlled experiments So what we want to do instead is be very careful about the relationships between our scientific hypotheses the scientific process models that emerge from those hypotheses and The statistical models we use to connect those process models to evidence So here's a figure 1.2 from chapter 1 of the book I'm going to step through this figure one piece at a time so you can understand the point I make through an example from population genetics And don't worry if you're not a population geneticist or you have no interest in population genetics I'm sure you can think of some analogous situation in your own field because the the situation I want to present here is actually quite commonplace. So let's start with one hypothesis and Walk through the connections to process models and statistical models. So on the left here We have a column for hypotheses and the the hypothesis I have shown is that evolution is neutral and this has been an ongoing debate for a long time and the evolutionary science is about the extent to which the sequence of Base pairs in DNA is to be explained largely by selection or by other evolutionary forces Neutrality is a very hard position on that that it's basically just mutation is what explains most patterns of variation I have a squiggly outline on this because hypotheses like this are quite vague To do any work with them you have to map them in the middle column to some kind of process model And what I mean by that is a real scientific model that has logical causation And if there are entities that you instantiate and you say which entities affect other entities and then what the consequences are of those things and So what I have one example in this slide and that is a process model of neutral meaning there's no selection Equilibrium meaning that the population size stays the same And this is a very particular kind of no model or neutral model that was used to try and understand evolutionary dynamics in the 20th century And then from this process model you can construct one or more statistical models and I'm just I just have one example here on the right the circle in two and The reason that process models and statistical models are not the same is that statistical models Examine associations. They don't really have causal forces in them Instead what you have to do is Study process models so that you know which implications to look for statistically Will tell you about causal forces in the process model help you estimate their strength and so on but you need both And statistical procedures for example might look at distributions And in this case, that's the way this story unfolded in the 20th century the neutral equilibrium model implies a particular distribution of the frequencies of different alleles in the population A power law distribution if I remember correctly But you can have other neutral models So here's another process model that also maps on to the idea that evolution is neutral the neutral non-equilibrium model So you can have no selection making it neutral But the population size can still fluctuate and almost all wild populations of animals and plants Have fluctuating population sizes over time sometimes radically so And this gives you a different statistical expectation than The neutral equilibrium model does so in that sense you could actually test these two against one another because they imply different statistical distributions Now let's consider a different hypothesis altogether that selection matters Now we all are prepared to accept that selection matters But the question is to what extent does it matter for the frequency distribution of alleles in wild populations? Everybody agrees that selection matters for design And again, we find that there are multiple process models that are that are attached to this hypothesis First would be a constant selection And another would be fluctuating selection the distinction here is that in constant selection That some trait is good and it's always good in fluctuating selection A trait may be good in one season or one year and then not in the next And Biologists argue about the extent to which selection fluctuates, but everybody agrees it fluctuates to some extent. Well now here's the moral of the story Many fluctuating selection models Generate statistical distributions that look like neutral models And so you can't easily tell them apart just by looking at the frequency distribution of alleles And again, this was this was a this saga that this slide represents is over. It happened in the 20th century But it's a general lesson that has reemerged in a number of fields Ecology went through the same drama with the neutral niche model the extent to which species are really different from one another and again A model with niches and a model without niches can produce the same frequency distributions of species and so on the point I want to get across is that In situations like this at the cutting edge of research with complicated natural phenomena There is no unique null hypothesis Instead we have to just be very careful about what process model we're talking about what its connections to hypotheses are And which aspect of its statistical implications We're going to use to contrast it to test it and to contrast it with other process models And that's not always easy. There are lots of things In my own work at my institute that have this this inconvenient feature, but I've seen examples in all kinds of fields So the ones I'm most familiar with Phylogenetics there is no such thing as a null phylogeny What does it mean to randomly permute species on trees or traits? So much of the structure of evolution is already baked into the data we have And so you have to have a non-null process model to make any sense About phylogenetics same goes for for ecological communities There is no uniquely null ecological community. You can't one does not simply permute a community of plants and animals And structurally similarly social networks in people and other animals Again, there's no unique null To randomize to instead we need process models and to study the implications of those process models So I hope that I've at least for the moment persuaded you that we need more than little tiny null robots tiny null golems We also need precise process models And then we need some set of statistical models. Hopefully justified from those process models From their implications so that we can get at some particular scientific question And I'm going to call repeatedly in this course the estimate. What do we want to know? What are we trying to estimate in the first place? So that's golems golems are going to keep coming up in the course over and over again And I hope you don't get tired of them The second creature that I want to talk about today is owls And owls have a Outsized role to play in this course because we're going to draw a bunch of owls Metaphorically, so there's this joke on the internet And I know some of you already know it that goes like this. The idea is you you have a How-to guide a step-by-step guide on how to draw an owl A very useful thing Step one is to draw some circles. So we have here our first step one There are two circles sketched out kind of a well one circle for the head and then An ellipse for the body and then step two is draw the rest of the owl And that's the joke So the the point of the joke is that this is not very useful There's obviously a bunch of steps missing In the middle here We need to draw some more circles and draw a branch and draw some features of the face and then do some shading And finally put in the detail. There are lots of steps to drawing an owl Technological skills are often like this because the how-to guides leave out a huge number of steps And often the experts of those skills aren't aware of all the steps either Which is why it's so nice to learn and close proximity to experts Because they're not always the experts are not always consciously aware of their expertise and all the all the steps that go into it So in this course, I want to really draw the owl for you as much as possible And that means coding and it means forcing you to express statistical models and scientific models In code in detail so that the assumptions are not hidden and we can talk about the connections among the Among their pieces and so that means slides like this. We're going to we're going to take what would normally be Just a command and a stats program and we're going to program it by hand In and this is not a particularly complicated calculation This is just the calculation of a Bayesian posterior distribution But we break it down into five steps and walk through them First step of setting up the calculation calculating the prior Calculating the likelihood and finally the posterior the normalized posterior distribution so that we draw the owl We'll also have detailed expressions of statistical models So if you're accustomed to expressing statistical models like regressions with a single formula one line of code That's not going to happen in this course, but it's for your own good We're going to express everything that goes on in it And that will tell you how to draw the owl So that you know things why do I want you to draw the owl because I realize drawing all the drawing the owl every time Be a little annoying There's three modes of drawing the owl in this course The first is to help you understand what you're doing So that you're not trusting in somebody else's can procedure the black box of the little golem And I think if you're taking this course you want to understand what you're doing Second There are selfish incentives beyond understanding if you document your work future you will thank you I'll say that again if you document your work then future you will thank you Uh future we will thank you because errors will have been avoided When you carefully document your work It's like reviewing your work during the documentation and this removes some kinds of error not all kinds but some kinds And then secondly you get to reuse your work in careful ways in the future as well when you document it Point and click interfaces do none of these things they leave no trail of breadcrumbs for you to review later And let's face it. We all have complicated lives. We can't remember what we did exactly But if you if you document your work by drawing in the bayesian owl as I say It'll all be there for you to return to and then finally We want a respectable scientific workflow and what I mean is it's not enough for you to trust your own work You have to work in a way so that others can trust it And that means having a documented orderly Justifiable scientific workflow that involves setting up your scientific hypotheses connecting them to to scientific models and then finally Two statistical results Okay, so the the outline of the scientific workflow that I'm going to reuse in examples in this course through the next 19 lectures after this one Have I think five steps? Let's count them out and see if I remember correctly So the first is to have a clear idea of the theoretical estimate that is what are you trying to do in the first place? And we'll have a number of different kinds of examples I it may seem silly to have this as step one, but it's often quite hard to tell what a scientific study was trying to do in the first place All too often you have a vague metaphorical connection between research buzzwords and some data set And then some figures are drawn and that's not good enough. We have to do better second You need some sort of scientific causal model and that'll give a step two of drawing the owl and The theoretical estimate will be precisely defined in the context of scientific causal models And again, these are these are models that can produce data. They're forward simulating models Um, our logical models that generate synthetic observations and let us design statistical procedures So that's step three We use the theoretical estimate and the precisely defined scientific models to build statistical procedures That can get at the estimate or tell us that it's not possible to get at the estimate sometimes That's that's true as well, but that's good to know because that means we need to Find a different way of investigating the phenomenon step four we simulate From step two the scientific models to validate that number three our statistical model works And this is a way of checking again. This is to justify Our our workflow so that our colleagues can believe that our software works This is also good for your own peace of mind to know that it works as well Most of the models in this course are not so complicated that step four is strictly necessary But nevertheless, I'll show you examples because I think it's something that I can teach you to do that You'll be glad you learned And then finally We analyze the real data We've passed through step four. So we feel like our our statistical procedure works It gives us in theory The theoretical estimate that we want And now we're prepared to put the data in notice that we we have not designed the scientific models or the statistical procedure Conditioned on the data we use Yeah, the data are entered at the last step But when we get to that point of analyzing the real data, we may realize we forgot something and that's okay We can back up and we can do things again as long as we document how all those decisions are made Okay, before we leave the owls, I want to say a little bit more about being a Bayesian owl and what all that is about so The reason we're going to do Bayesian owls in this course is because it's a very flexible approach So what you see on the screen here is believe it or not Saturn as Galileo Galilei would have seen it in 1610 you see the Scan of his personal notebook in the lower right here The telescope that he was using one of the first Was not very good and it made very blurry images And so when you look at Saturn through a bad telescope or or highly out of focus The rings look like little ears on the side of it like this and that's how Galileo drew it Now the inference problem here is what's generating this blurry image What does Saturn really look like now? You know what Saturn really looks like because you've seen pictures of it since you were a child, but Galileo did not Um This is an interesting kind of inference problem because there's no sampling variation involved No matter how many times Galileo looked through his telescope. He got the same blurry image Nevertheless, there's uncertainty about what the generating image is what the planet actually looks like So the question I put to you is is this a statistical problem or not? And you know, I'm going to say it is of course. It's a statistical problem. It's a Bayesian statistical problem The Bayesian approach is permissive and flexible No matter if you've got a data generating scientific hypothesis You can analyze it with Bayes It doesn't matter if the uncertainty is due to sampling variation or to some other process like light scattering in a bad telescope You can express uncertainty at all levels whether it's measurement or observation or sampling biases Uh missing data all those different sources of uncertainty live together in the same analysis And near the end of the course, I'll show you how to use the Bayesian approach to get Rather direct and immediate solutions to measurement error and missing data Expressing them in the kinds of regression models you'll be using throughout the earlier parts of the course and As I said a little earlier What Bayes let you do is really focus on scientific models because you can go straight from the scientific models To statistical procedures with minimum fuss. You don't have to worry about which kind of estimate Sorry, you always worry about the estimate which kind of statistical estimator you're going to use or Which kind of standard errors and and all those other sorts of decisions We have only one estimator in Bayes and it's the posterior distribution, which you'll learn about in the next lecture Okay The third critter I wanted to talk about before I finish this first introductory lecture is dags. So what are dags? So I just said a little bit about Bayes and and most of you know that Throughout much of the 20th century, especially the early 20th century. There was this Competition between the Bayesian approach just to statistical inference and the frequentist approach to statistical inference These giants fighting it out over aircraft carriers and so on I'm not very interested in that fight and the reason is because Both are very capable frameworks and the problem scientists really have is not which of those to choose But that they have no training in how to connect Their causal models to their statistical procedures. And so That's what I want to talk about now. Now that said, of course Bayes is better. This is a Bayesian course But you could teach this course using frequentist tools and it would be Not so different in most places near the end. It would be quite different But in most places wouldn't be so different The important part is the part about the causal inference part connecting our causal models to the statistical procedures So how are we going to do that? Well, the slogan that I often give is to put the science before the statistics and no one's going to complain about that slogan What do I mean for statistical models to produce scientific insights? You really need something outside the statistical model And I've already mentioned this in the in the justifiable workflow when we when I talked about drawing the owl What we need are scientific models or sometimes called causal models models that contain in them Entities that influence other entities and not the reverse The reasons we do a statistical analysis a certain way are not in the data themselves It's not enough to simply have a big data table and then look at it and Ask how many groups there are and so on and get to some meaningful statistical analysis And the reason is because Data tables only have associations And so we believe in causes because we believe in them And that leads us to interpret associations in causal ways And there's no way out of that. So you have to have the scientific model and I'll I'll have endless examples of this as the course progresses So the Point of all this is that the causes of the data cannot be extracted from the data alone There's a philosopher of of science. Nancy Cartwright has this great slogan No causes in no causes out and that'll be another motto for us in the course So other reason to think about Causation explicitly here is our big looming problem is that even description and research design are all aspects of this The models and the information you need to put into analysis to successfully Conduct causal inference is the same information you need to accurately describe a population Or to design a study which gets at the desired estimate. These are all really the same sort of task And I want to I want to emphasize this issue about description here and this This may come up a few later in a few later lectures, but if not, I want to really want to emphasize it now because I'm An anthropologist and a lot of what anthropologists do is describe things. It's a and and that's not a Some sort of low-class occupation to try to describe things description is fundamental to scholarship But to do description, right? You need causal information You need causal information about how the sample differs from the population I'll say that again to do description, right? You need causal information You need causal information about how the sample differs from the population And when you're studying humans at least the sample always differs from the population sometimes in very systematic and important ways And you need to account for those things the right way to adjust for those sampling biases Is to understand what caused them And we'll have examples of these sorts of things in later lectures Okay, so what is causal inference? We're we're approaching DAGs rapidly. We'll get to what a DAG is in a moment. So causal inference is the attempt to understand the scientific causal model to Understand the pieces of it using data that may have been produced by it So everybody's heard that correlation Doesn't imply causation. Well, unfortunately causation doesn't even imply correlation as we'll talk about later But there's good news. It is possible to learn about causes from data But those Causal learning those causes requires more than just the association between variables And there are two ways to think about what causal inference is and this helps us think about how we would find it in data First way of thinking about it is that causal inference is prediction But it's a very special kind of prediction And i'll say more about that on the next slide And the second is that causal inference is a kind of imputation Which means a a counterfactual Imagining of something that could have happened and i'll say a little bit more about this later as well These are the same thing actually. There's one kind of causal inference. We do in statistics But if you do it right, you can do both these things Okay, first let's talk about causal prediction So prediction and causal inference are really different tasks But there's a there's it's nice to think about causal inference has a special kind of prediction So It's a kind of Prediction where you predict the consequences of intervening in a system Knowing a cause means being able to predict the consequences Of an intervention that is you do something to change the system and you observe what happens If you understand the causes operating in the system, you'll be able to predict the consequences of that intervention So the key question is what if I do this the causal inference is getting at that question The trees are here as an example A simple kind of kids example to think through this if you If you're inside your house and you look outside and you see the wind blowing the trees It's it's not immediately obvious from the observation alone. Which is causing which Right, you believe the wind blows the trees because you believe in the wind causes the trees to blow But it's it's the wind and the movement of the trees and the wind are always associated So it's not clear which causes which Now if you were to do an intervention like getting you and a few hundred of your friends to climb up all the local trees and shake them It would not cause much wind That's the prediction at least Because you understand which is causing which it's the wind that makes the trees blow It's not the movement of the trees that generates the wind The other way you can think about cause is through causal imputation, which are the counterfactual outcomes Knowing a cause means being able to construct unobserved counterfactual things things that did not happen But if they had happened You would be able to predict what would happen as a consequence So these are alternative histories like imagine some other country like china had gotten to the moon before the united states How would history have changed if you understood the causes of history you could say What the effect of that Change would be So that is the what if I had done something else or if things had turned out differently This is the part of causal inference that we often think of as explanation Although explanation is a philosophically difficult term. So i'm going to try to avoid it for the most part Okay, finally daggs This course has a lot of daggs in it daggs are heuristic causal models They're the simplest sort of causal models that you can work with and they're really good for onboarding scientists into thinking About scientific causal models that are distinct from statistical models that contain extra information causal information Dagg stands for directed acyclic graph and you don't have to worry about what that means right now We'll talk about it later But there's an example in the upper right As I said, these are heuristic models. You can analyze them with your eyeballs and that's what I'll teach you how to do They help you clarify your scientific thinking and you can actually Apply logical rules to them to design statistical procedures as well. And that's that's what we'll start doing In in a couple weeks For now, just let me try to be a little bit provocative by Talking about what you can represent in these and why they're needed Each letter represents a variable something you can measure Well, you don't always have to be able to measure it, but it's a thing that exists And is caused by or causes other measurable things And the arrows represent causes the arrows point in the direction of causation So typically in a scientific study, we have some theoretical estimate like we want to estimate the effect of x on y represented By the arrow at the bottom here The x with the arrow pointing to y But to do that we unfortunately have to consider other kinds of relationships in the system It's not enough everybody knows just to focus on the two variables of interest There are other kinds of things going on First of all, there are other variables influencing the outcome like b in in this This particular dag And there are other variables influencing the cause of interest like a pointing at x in in this dag So here's a question for you and I assume most people listening this have had one or more statistics courses Which of these a or b should you add in a model? When you try to learn the effect of x on y, are they the same? Are they different? Should you add them both? Should you add neither? We'll get to the answer later in the course, but if you're like me, you were never taught this At all to even think about it in these terms Give you a hint is they're really different from one another and you should do different things with them There are other variables like c which are common causes of the two variables of interest here c is pointing into x and y c is the classical confound and we'll talk a lot about confounds in the course and obviously we need to deal with confounds Because x and y can end up having a strong association even if x doesn't influence y at all And then finally the other variables can have relationships with one another Which create complicated problems as well And in this particular graph the fact that a and b also influence c does create a complicated problem Which i'll talk about again as I said in a couple weeks when we focus on dag's more So the practical reason to learn this stuff these heuristic causal models Is that different statistical queries different scientific queries require different statistical procedures So even with one process model one dag like the one in the upper right of this slide If you have questions about x influencing y or rather about b influencing y you would need different statistical procedures to do that correctly to estimate those things You can't necessarily do it in a single model So questions like which control variable should you use are really not so innocent once you learn this framework and Just to caution you and I'll demonstrate this later. It's absolutely not safe to just add every potential confound to the model There are things called bad controls Controls that can actually make things worse The other thing you want to do with these heuristic causal models is Test them and so we can talk about that as well There are testable implications of any particular scientific process model and of course scientists have been testing such implications for centuries Now dag's are extremely useful and they're useful in research and they're extremely useful in teaching But eventually of course every scientific field aspires to move beyond purely heuristic causal models If you've got more scientific information You can represent your scientific models with that information and you go beyond dag's to other things and it's a very Last lecture, uh, the second to last lecture. I think of the course. I give some examples of this of More elaborate process models and how we can do stats with them Okay, let me just try to summarize this because this is the three critters That we're going to see over and over again in the course first golems. This is my Metaphor for the brainless powerful statistical models that we rely upon and we need golems. We absolutely do But you've got to design the right golem and you have to deploy it in a very Constrained set of circumstances so that it doesn't do damage Second are owls. We're going to draw the owls and what I mean by this is documented objective procedures and working in a way That gives our gives us confidence in our work and also Justifies confidence of others in our work And third dag's directed acyclic graphs A way to make transparent our scientific assumptions so that we can justify the scientific effort Expose it to critique and directly connect scientific theories to the powerful golems We're going to use to extract associations from data So this is a 10 week course the way I've planned it Although if you're watching the lectures online, you can take as long or as short As you like I plan two lectures for each week and in this first week at the top of the of the table here It's the focus is just learning the foundations of Bayesian inference And if you've got the book and you're following along in the reading you should read chapters one two and three To accompany the first two lectures in the next lecture. We will focus on really doing Bayesian inference So i'll see you there


Hi everybody, how you doing? Welcome to the latest episode of from the rock to the cloud and As you know, we talk about everything to do with Windows Server a little bit of Azure all that kind of exciting stuff and As always we've got a guest to help the incompetent post Through understanding this technology. So today we've got a very special guest We've got the Batman which is why I've got bar up in my up in my pixel box behind me But we've got Anthony Bartello All the way from well, you're you're from you're from the rock. So this is like perfect We're you from like we couldn't have a better expert to talk about server and rock and We're gonna talk about I think as you're back up recovery today. I mean, so you're you're the right man for this Tell us a little bit about yourself So yeah Maltese background. I live in Canada in Toronto. It's warm here right now You see have snow a lot of people say do you live in an igloo? No, it's it's hot here right now We do use Celsius. So it's about 30 degrees right now and I'm excited to talk about as your backup, especially because of the Need for it in hybrid, right? You know, we hear the stories. Hey, I was back in the day of solid on-premises implementation of architecture and I remember going through putting the DLT tape into the into my Proline 3000 Doing the backups on a nightly basis doing the weekly backup doing the monthly backup the yearly backup You name it to the fiscal year Checking the tapes having unlocked in a safe did the whole thing When you know, I was going further on in my career the big thing that changed for me in terms of backup was the whole aspect of Are the tapes enough right like the example? I give is the the organization. I was supporting was a law firm and The from an outside was from a third-party perspective I was supporting law firm as a IT specialist and the IT administrator was at this law firm Was diligent in terms of tape backup the LT tapes changed every day changed every week would take the tapes home and Lock it into a safe Unfortunately, it was a magnetic safe But tapes home into and put them into a magnet safe and would lock the safe and This That and that's the thing you don't know what you don't know, right? It's completely oblivious like he got this safe at the local store and put it in this house and has it Oh, you know all my tapes are off like he was very diligent about it and Would go for it he would even test the tapes prior to putting them in to make sure the backup was there and then would put them in like a but wouldn't do another test after the fact and The tapes are going to the safe and then this law firm was in a strip mall And beside it was a restaurant and the restaurant caught fire They had a bad grease fire and it took out the entire law firm the whole strip mall was burned to the ground But the I professionals like hey, no problem I have this all in backup tape after tape after tape all the tapes were corrupted because of the magnetic safe He lost everything and it was the biggest things in his life Like it just he was that was his responsibility Aside from the upkeep infrastructure and everything went down and everything was lost That for me was an eye-opener in terms of you know, then you're looking at disaster recovery solutions You're looking at scenarios where you having off-site the law firm was exploring into that whole aspect of well Yeah, what if I was to have a separate data center off-site and the costs for that and you know for a small law firm it didn't make financial sense to have that type of configuration and So that's where the discussion of Azure backup came in at that time and we're talking a long time ago And it was you know in its infancy and a lot of concerns around you know My data has to be on-premises for my ISO certification or my standards for my my organization in my industry I can't have data in the cloud. I can't have that, you know, show my data being gone But in respect to that if the data is not active is just at rest, you know There are special considerations that can be put into play for a disaster recovery solution and Azure backup has a capability to provide that functionality so That's a great Explanation of why I mean that tapes Like goodness knows that I had a great mixtape once upon a time and you know, and then that you know actually strength, you know, yeah, like I actually I got it out and listen to it It was all crinkled and like it just it didn't sound as good as I remember, right? So That kind of nicely moves us on to You know, what is Azure backup recovery? What like what is it? Do you know what I mean? Because like you've kind of given the real world example of like what people think or what was backup, you know Like I remember as a kid You know working in a store and the manager of the store doing exactly the same thing as the person from your story They give me the give me the tape and they'd be like right now You go around to the other office and they store it in their safe I'm like this again makes no sense, but I have to do it every day But like surely there's got to be a better way of doing it We are we saying that as your backup recovery is that way? Like what is as your backup recovery? So here's the thing as your backup or backup in general is part of your disaster recovery plan as an organization You have to have a solid plan to understand when catastrophe strikes What happens to your organization? How is your data packed backed up? How is your data retrievable after that? The biggest thing that you have to remember is data is the new currency and so that's worth There's a huge amount of value in the data that you have and you have to treat it as such So in respect to your data having it backed up on a tape. Is that enough and having it at a separate location? What have you? Further to that in terms of disaster recovery. It's not enough just to test that It's not enough just to have a backup. You also have to test it You also have to ensure that the functionality that backup has in terms of your data Is usually so it's not just enough to put it somewhere and forget it The other aspect to that is from a development perspective We're seeing a lot of situations where hey developers coming in to build a solution You don't want to have these developers building a solution directly on your live data So you want to build a dev environment doing that from a tape It could take hours if not days to set up the whole environment as it's just spinning up in a dev instance based on your backup You know, there's so many different reasons why The biggest thing to remember though when you're looking at this is what is required of your organization? I can tell you to you know till I'm blue in the face how awesome as your backup is But it also has to meet your requirements and your needs for your organization has to be something where it fits We talked a little bit is before before the show You know, it's awesome to have this ability to push it out to the cloud But a lot of organizations still have an on-premises implementation of architecture because of need because of cost whatever the functionality requirement is as your backup is that capability of having off-site if It you know meets your requirements not need these tapes not needing the deal DLT back Configuration automating the whole solution Even the testing can be automated, which is huge and then I require pulling it You know, you're looking at if you have your offices completely down You can do that swap over to your backup that's in cloud and have everybody put up and running within hours You have that scenario where you're moving sites And you're moving to another site and pushing off your data to the other site your backup to the other site And then deploying your image that way. There's so many different uses just above and beyond the civil backup Implementation and it's not windows.  So let's just put ourselves in a on-prem scenario Right. How do we use Azure backup? Like how can I get Azure backup? And I've got on-prem. So say I've got you know Windows Server 2019 on-prem infrastructure as it is at the moment How how do I go about enabling that as your backup recovery piece? Like like what we're gonna do because I want to be secure and I don't want to work on tapes anymore So inside of the Azure portal You would actually add in your on-premises servers to the Azure portal a client is then pushed down to the server itself And then your backup is scheduled accordingly when love about this It's it's fully your control in terms of Right, right. Sorry. Sorry. Sorry. Sorry. Sorry. Sorry. Sorry. Yeah. Yeah It's that it like you just log on to a portal you push a client down you put it on your server and then you've got it And then you've got it Sorry, I'm just I'm just taking a moment because I thought it was going to be surely more complicated than that No, that's it Eat my short what happens is that? When you've added your server when you've added your server to the Azure portal into the Azure backup service In essence provides an authentication key for the security aspect, right? And then pushes down the client to your machine Which acts as your actuator and capturing that data to be pushed out to the cloud and then having the ability to select You know full backup incremental backup. Yeah, whatever is required by your organization folder backup You know, whatever that may be if I don't need to back up your entire Architecture that's on-premises having the specific instances pushed out of the cloud totally customizable to your needs Okay, so and I think I read somewhere that is like there's volts or Like again like again, I'm probably just gonna ask you things that you you're like, that's obvious. Yeah But right there like it in in in backup and recovery there there's volts, right? So first of all, what are volts? Why they important and then is there like a limitation on what you can do, you know within within this like do you need like You know, like a special subscription or like how does that stuff work? So you're staying in Azure subscription has the ability to to utilize Azure backup There is no additional Subscriptions that are required as long as you have Azure the other aspect to that in terms of the vaults is your security For your data. There are scenarios where data is specific in terms of access and who can gain access to that If you're using our back or role-based access control You have the ability to govern over who gains access to what data and respect to your on-premises implementation or even cloud For all your data the same holds true for Azure backup in regards to setting the authentication rules for specific individuals To access that data that's in backup because that that backup data is just needs It's the same type of security as your on-premises Data for specific instances or specific uses Because of this you have the ability to accept governing rules in terms of if you're going to go and access the backup data What rights do you have you have the ability to redeploy? Do you have the really the ability to redeploy in subscription out of subscription into another subscription? You have the ability to push down to an on-premises Implementation you have the ability to fully access that data. You only have read access to specific parts of that data Do you have the ability to spin up specific accesses of that data for dev environments? Like it could be very granular in terms of the capability more so than the functionality that you would have in the backup on a DLT tape Okay, so that kind of like grouping of the the backup and resources is like a bolt So that did I just understand that right? Yes Cool just make sure I'm on the right track because like you know like I said, I'm learning This is not like we haven't pre talked about this and and I'm learning this life You know that's that's pretty is there a limitation to the number of servers machines that can be registered into a vault like where you keep all people you know You know, you know, you're you're safe data and everything. Does it matter? So there's no limitation Do you know of course there's an incurred cost in terms of the amount of data that you're backing up So make sure you're aware in terms of what you're backing up into the cloud There are actually cost analyzers that are available as well that can actually provide you some prediction In terms of what your estimated cost will be in terms of as your backup The larger charge is for the actual transfer of data more so than the storage of data So when you're doing your initial backup, obviously, you'll see a spike and respect in respect to your costs But once that's been completed and you're doing incremental backups the cost is substantially less In terms that implementation the biggest thing out of this though is that on a dime I can turn on that infrastructure that's in the cloud and it would be seamless to your end users should disaster strike And you your your organization would continue to function Without the need of you know restoring to another infrastructure. It can be a temporary scenario It doesn't have to be a you know, a hundred percent. That's it for going forward You can go back to on crisis if required But there's a piece of mind that you have a availability like having an off-site architecture and infrastructure should disaster strike its organization and None of these votes are metallic like a magnetic in any way None of this is magnetic loss Yeah, that's cool. Just make sure like What's interesting about this is it's you know, when you hear about solutions like Azure backup you immediately think okay We're talking Fortune 500 companies banks and law firms and medical, you know, I've seen deployments of Azure backup for floor shops Because that's their bread and butter in respect to their customers and their you know daily activities and the transactions that are Being captured and so this you know a couple floor shops here in Toronto actually have deployed to Azure backup instead of having an on-premises Implementation of tape because should disaster strike data is up in the cloud. They're they're safeguarded by it It's it's a minimum cost of them because it's literally it's one server inside of their The floor shop, but it's something where it's a piece of mind that they know that their data is always going to be there So it's it's really the combination of like the right hardware Right infrastructure and then that extra little layer on top of protection just to make sure that everybody's getting the right services And I suppose what I've kind of picked up from you It's about being smart and I'm kind of you know It's about being smart with what you choose to make sure that you've got there that you don't need to back up everything You just need to back up the things that are critical like your customer data or you know that kind of stuff The stuff is irreplaceable. That's the stuff you need to make sure it's safe and then it can be cost effective So you know that makes sense it how many data sources that you know It can you have in in you know in in this backup environment in a vault say like is there is there a limitation to that or you know Or is it just really how much you're willing to pay for how much data it like it's scalable right? It's elastic Yeah, it's completely elastic right there is no limitation in terms of I can only have so many data points that are Pushing up to the cloud it is you know something that you have to think about in cost And that's why it's so important to Understand what your strategy is for disaster recovery when using as your backup Do you require to back up everything or you can you just back up a couple things like customer information and transactional information? It's it's something that you you know, you can keep in mind. You can also supplement it with on-premises Backup as well. It doesn't mean that if you've gone as your backup that you've now Eliminated the need for on-premises backup You can definitely do both if that's a requirement of your organization for ISO standards or certifications that you may have to adhere to It's totally up to you the big thing though with this is The amount of time it then would take you it should a disaster strike To be up and running and it's you know, it's it's it's something where you're looking at, you know Complete disaster and a complete disaster could be you know a tripped alarm inside of your organization and the Water sprinklers go off and takes out your full server room And I you know sometimes a server room is in the closet and unfortunately is susceptible to water leaks and what have you That's a disaster and you've lost your data, right? It's not you know, it's just something to be prepared for when you're looking at your entire solution And as your backup is a part of disaster recovery. It's not the full solution Okay now that that makes sense and I just wanted to kind of go back to something you kind of talked about it earlier, but the policy piece When I think about like obviously my data is there I can set policies is that again? Is there a limitation to? You know how many policies I can create the you know the whole like Again, how does how does the policy bit work because I imagine that's really important to the Because again, I'm kind of leaning towards the security question here, right? Which is again, it's in the cloud is there But is it the it's the policies that create the security layer will help build that security layer for the business So policies is you know something a part of your organization in terms of who gains access to what who can spin up? What resources who can you know set up? You know a Deployment of your backup This is something that's to be planned emits your entire as your implementation and subscription You would have it at the top layer in terms of your access who gets access to what? Like the whole role-based access control of spinning up of resources spinning up a compute cycles That then trickles down to any resource that you've deployed You can then get granular specifically on as your backup and say who gains access to what there are different roles In terms of backup one to just do testing one to do an actual deployment You'd have to go through all the roles itself and then you can even customize your own roles In terms of your implementation and I know of a lot of it shops that you know support organizations What they'll actually do is they'll build these custom roles and export them as JSON templates And then import them into other subscriptions to give other customers a leg up in terms of having that implementation of that role already created It's completely up to the organization deploying as your backup in terms of their security requirements And in terms of the customization or use of existing templates that are better there for them to take advantage of Cool, so I kind of think of it It's kind of almost like if you were actually in a physical world and you were allowing people to access to different parts of a building It's kind of like that. That's how my my kind of simplistic brain thinks about it But that's kind of basically what the policy is allowed to do, right? So yeah, that's cool That makes sense. It's kind of it's this is sinking in now Anthony is sinking in I'm starting to get it. I'm feeling good about a backup and recovery. So My organization what we decided to do we've got like one volt and How do we isolate different data from you know different? You know different servers in the vault when I'm restoring data like how do I get it back out? Do you know what I mean in the right place when I've only got one? I've got one ball But multiple things in there. How do I how do I know what what the right thing is to go back? Well first thing you would do is assign roles to the individuals to even gain access to the backup data, right? I don't know if you want to allow that for the entire organization usually that's held specifically within the IT department in terms of that capability Once you've set up that role, then you have that individual have the ability to if you're going to Restore the data that you put into Azure backup They would then be responsible for the restore functionality after that whatever policies are in place after the restore has been completed That's succinct with your organization. So that doesn't change. Okay, individuals have access to what data that's in this Is what happens if you need to add additional roles to access your backup? So as an example, there's a developer and it's the organization They want to spin up a copy of the backup as dev A dev arm so that they can build out their applications on you know Copy data not live data to build out their solution and then punt it over to IT to have it deployed You know you can enable that type of functionality that when they restore that data They can't restore it in the live production area. They would have to create that developer platform You can even use inclusion of blueprint technology that allows that IT to control what the developers do in terms of their deployment But allowing them to be self-sufficient in creation of that deployment of that area and then push out that backup to that Blueprinted dev area for them to have that functionality, but not affect the on-premises live data So to you know ensure that nothing gets stopped in terms of production Like it sounds ideal and it sounds very very simple. So, you know, I'm just it's just great that we can tell people about it the whole thing that works on the Azure subscription piece, right and Is there a whole thing between say if I've got data on one subscription and I want to move? You know do I need to you know, I've got another six You know dial up another subscription. Can I move that data around as as the the owner of those subscriptions? Is that is that a thing that I can do? So you can but again, it's based on rights, right? So from a perspective of having the forest and multiple multiple Right it we're still we're still in that scenario of forests and then the subscriptions replace the actual on-premises Implementations, but like your on-premises implementation usually you have one service per application The same would hold true in terms of subscriptions You'd have a subscription for a specific application We should be part of a forest that can have that connection across multiple Subscriptions and having the ability to actually push out that data to the subscription that's required So long as you have access to that other subscription, right?  We'd love to have your feedback So we you know, we definitely want to get those comments and all that stuff coming anyway But if I'm lucky with the power of a producer and if I say three two one show us a meme Three two one show us a meme we get a meme Okay, so we've got So server says if you don't know what's wrong, I'm not gonna.  I don't know what's wrong with this meme. Does this does this mean anything to you Anthony? So it dies it tells me that you have your windows admin center is not set up properly Or you haven't incorporated as you're monitoring to understand what's going on in your impremises environment Usually, uh, that's not the case anymore. But yeah, I get the meme Right now that so like I said, uh, they are certainly there to make me look silly, right? Okay, so we're gonna do another one really quick meme number two. Here we go Boss the server is down Just restart it. Hmm. It's not that simple Um, well like this if now if there's ever a reason for back, you know for backup and recovery like this is the mean for it Right. Oh, there you go This reminds me of a story. Um, a new organization and you a new retail Uh, um organization was setting up shop, uh, and they had these servers in the back room And somebody decided to use them as a uh a table to cut wood Uh on top of the servers because it was a level Right. Now the blade didn't hit the server by that individual that was doing the construction was like immediately Reminds me of that It is amazing though that that that so the disc the disregard that the old server has in the corner of like, you know Someone's store or like, you know, someone's office or whatever. Like it's just like, you know, that just keeps on working And you know, I think Sorry, sorry Yeah, you go Oh, I'm just gonna say the amount of servers that I've seen that have houseplants on top of them Uh, with water scenes rolling down the side of the server, you know, I've seen it so many times Uh, people just don't know, right? Yeah, and I mean and that water calling technology is is not gonna you're not gonna aid you back up Uh, do you know that? Um, well, I think uh, let's just kill that meme because uh, you know, I mean, we've seen enough We've seen enough damaged and stressed servers I'm just going to recap really really quickly about kind of what we learned about today So what we've learned is is that you've got an on-premise windows server scenario You can actually take advantage with With most up-to-date version of a bit you can actually take advantage of that as your backup recovery You can add that that layer on um, I've written down here that um And these are kind of my two takeaway points, right? First of all, it's scalable to whatever size business you need be or small and it's also customizable So you can control the policies of who access is the data where you put the data where you move the data All of that is completely controllable in that as your environment. So, you know, I think You know, is there anything else that you know, you'd like to say to the audience maybe Anthony before we go that, you know Again, I've super simplified it.  So so I actually wanted to add to your piece because you'd said windows. It's not just windows. It's linux as well So you have the full functionality backing up your linux, which in retail is huge Especially on transactional the capture that information and then it can also get granular in terms of your data Specifically around sequel. So if you have your sequel database, there's a specific client that you can actually deploy for sequel as well And get granular to, you know, the data that's inside of that database for extraction of partial or full or what have you, right? The biggest thing to take away from all of this is sky's the limit in terms of your backup solution But don't just deploy or implement a backup solution without understanding What are the needs of your organization that you support having a disaster recovery plan that your organization has to adhere to And everybody has to agree to it's not something that it can just walk in and deploy anymore This is in partnership with the developers and the business decision makers of your organization And ensure that your solution is such that Should disaster arise and that's something as simple as a houseplant being watered to too frequently and taking out your server You have to have the ability to ensure that your business stays up and running or you may not have a job Yeah, you know, ultimately the cost of A bit going wrong is a lot more than the cost of protecting yourself So you'd be very very naive exactly like you said Anthony not to take those proportions So that everybody like thanks very much for tuning into this episode. As I said, we've had the bark man Is that shoulder even smart? We have the bark man with us today And Anthony Bartello. So thank you so much for your time. Thank you for your wisdom And everybody if you've got any thoughts or comments or you want to find out a bit more or you want to You know talk talk to Anthony probably when he talked to Anthony, but maybe we'll get maybe we'll see what we can do Um, but obviously he's over in Toronto on Toronto time. So thank you so much So I really really really appreciate it and hopefully you've enjoyed this episode from the rock to the cloud Or in fact sky's the limit as Anthony said and we look very much to seeing you on the next episode


Ukrainian partisans breach Moscow region missile attack alert centre. The Atezh resistance movement talked about their reconnaissance work in the Russia's lair. Ukrainian partisans worked in a missile launch warning centre in the Moscow region. The Atezh agent left Russian territory, so the movement decided to share the details of another successful foray behind enemy lines. For a long time, the occupiers had no idea that a Ukrainian agent was lost among them and their secret military information fell into the hands of the Ukrainian defence forces. So, the partisans say their agent served in the 820 main missile attack warning centre in the village of Timonovo in the Moscow region. The centre warns about missile launches. In addition, it monitors the launches of Russian missiles, both combat and training, and then it gives instructions to air defence. Thanks to the work of the Atezh agent, it was possible to find out information about the state of all individual radio nodes in Russia, in particular their vulnerabilities. Based on this data, the defence forces have successfully bypassed and continue to bypass Russian air defence and create bangs at Russian military facilities. In conclusion, Atezh intrigued the public with yet another statement. They emphasised that the movement's activities in this military unit have not ended. The agent let his legacy, which will make itself felt. The partisans emphasised.


My mind was spinning a million thoughts and ideas cascaded down the glaciers of my mind as I tried to redirect them into a peaceful sea Why don't we get that we're here for God? We let so many things distract us So many things take our gaze off of him. This whole thing is so much more than this moment To begin to see our world not just as the dronings of the day-to-day Rather the grand story that we are invited into the story of relating to each other the way Jesus himself Related to us a story of suffering the pain and brokenness God is using the broken pieces to form wholeness in him That the greatest and deepest joy one can find is not based on changing Circumstances, but on a God who transcends our reality The glow of the phone light covers us like a blanket it's echoes fill empty moments of silence with noise Notifications taunt us with human approval never leaving a satisfied We can't take a moment to look up from our phone because we're scared of missing out But maybe just maybe as we look down at our phone We're missing out on a deep life changing all-encompassing spirit rejuvenating connection with God Tomorrow I'll pray tomorrow. I'll begin to study the word of God tomorrow I'll put the screen down and look tomorrow. I'll take these headphones off and listen How can we expect to hear from God when we continually pump noise into our ears? That's all it is noise But what God offers it's a symphony like a tree planted by the water We're nourished we grow thirsty when our roots draw away from the water power and presence God is near and God is active in his power Distracted just too distracted to begin to open our eyes to see what God can do Prideful just too prideful to lay down our plans to see that God has already written ours scared Just too scared of living a life that looks different What am I missing out on? What will I regret missing? nothing absolutely nothing


Keep your distance misty us looking for trouble. Are you stop it or die is that clear enough closer to stop that?