For years, I've been talking to leaders about this idea of be here now Which is simply being present in the moment Be words exactly where you are and too many of us including myself We're somewhere physically, but we're somewhere else mentally Or we're somewhere physically and We're agitated with where we are. So we're only there partially mentally. I had a good opportunity this evening to be able to Spend some time with my executive coach a guy named Paul Nakai one of the most lovely human beings on earth and He was talking about Be here now being present from a different perspective, and I think it's something that could help anyone said are you in the moment in Judgment and frustration or irritation Or are you in the moment with humility and laughter? And as I reflected on that I realized The best work I've ever done the best relationships. I've ever had the best I've ever felt was When I was in the moment, but not in judgment of the moment in judgment of the people and judgment of the activity irritated and frustrated and Was far more effective Because the moment that was entered with humility and a sense of connection and laughter So as you look at what's going on for you as a leader Where are you most of the time? Do you find yourself frustrated? irritated judgmental Are able to you are you able to be in the moment and be connected? laughter humility I Can tell you both exist for all of us But one is the source of better connection and better performance the other Drains us of energy So reflect this week Check when you're in judgment and irritation and when you're in humility and laughter Notice the public will difference. It will make a difference. I wish you the best of luck with it


Friends, welcome back to my channel and welcome if you're new here. It is Monday, so it is another meal prep Monday. I have three absolutely delicious recipes coming your way. I have breakfast, lunch, and a snack or a pre or post workout snack that is divine. Everything is clean eating with whole, real food. I'm so excited to share these three recipes for you. So let's, without further ado, jump in to this meal prep. I'm gonna run from my given disaster. Speed away from the holy mine. For my breakfast this week, I'm going to be making a protein blueberry quinoa breakfast bowl, and I'm gonna pair that with a hard boiled egg. So I'm gonna show you guys how I use my instant pot to boil my eggs because they come out perfect every single time. But first let me show you what is in my breakfast bowl and then we'll get some eggs in the instant pot. First you're going to need some quinoa, milk of your choice. You can do fat free milk or almond milk, whatever your preference is. You'll need some sort of berry, and this is optional to top your bowls. I'm gonna be using goji berries. You could really use anything. These are really good. They are organic. They come from Thrive Market. Thrive is an amazing, amazing market. You pay a membership fee and you can shop on there for clean eating, organic food. They have the best prices for everything and they have things you never would think would be clean eating, delicious food. So for Thrive Market down in the description there's a link. If you click the link, you'll get $20 off of your subscription. Thrive Market gives back to the community and again, best prices you will find for these foods. Hands down, best, best prices. So again, just click the link down in the description box. You can sign up for a one month, three month or year membership. I recommend the year membership. It works out to be like $5 a month and it's free shipping when you order your products on Thrive with your membership and you'll get $20 in free groceries. So you basically pay your membership, place an order and you get $20 worth of free stuff. Can't beat it. I'm gonna be talking a little bit more about Thrive in my What I Eat on Wednesday but check it out, you guys. Make sure you're going through the link.  So I'm gonna add some goji berries to mine. You'll also need a lemon. We're gonna zest that. Blueberries of your choice. You can use fresh or frozen. Also some raw honey and then just some eggs. And again, we're gonna throw these in the Instant Pot, get them boiled so that I can have hard boiled eggs with my breakfast. So let's get started on this week's breakfast. So I love using my Instant Pot to boil my eggs. So what I do is in the bottom, you can either use one of the hard boiled egg containers or inserts but I just used the little rack that came with my Instant Pot. And then generally I'll add about a cup of water to the bottom. And then what I do is just place my eggs here on the rack. And this works just as good if you don't have an Instant Pot insert for eggs. This little rack comes with your Instant Pot or most of the time comes with an Instant Pot. So you can simply use that for your eggs and it works just as well in my opinion. If you are looking for a great accessory kit though for your Instant Pot, I do have one linked in my Amazon store down below for you guys, that is awesome and comprehensive kind of comes with everything. But if you just wanna boil eggs, this is a great way to do it. And all you do again is just put them on that little insert. So I put about 10 eggs in there and then the next thing that we're going to do, we're gonna go ahead and put our lid on our Instant Pot and then make sure it is turned to sealing and very easy. If you go down to your display panel here on your Instant Pot, just hit the one that says egg. It's going to set the timer for five minutes. When your timer goes off, you're going to naturally release it for five additional minutes, which means that you're not going to turn this. It's just going to naturally release the pressure. When that five minutes is up, go ahead and turn it here to venting and then we're gonna put our eggs into a ice bath and voila, we have perfectly boiled eggs. Our eggs are done and then I have a bowl here which is some ice and water. So what I'll generally do is just use a pair of tongs and I'll just put the eggs directly into the ice bath and you want to leave your eggs in the ice bath a minimum of five minutes. So you're essentially using the five, five, five method, five minutes of cooking, five minutes of naturally releasing and then five minutes in an ice bath. But to be honest with you, I generally end up leaving my eggs in the ice bath a lot longer than five minutes and then literally they will peel so easy. I will show you once they come out of the ice bath. So I'm gonna let those sit in there for just a few minutes. My eggs are ready to go. So in my bowl are my nice cooled eggs. I wanna show you guys how easy these are. So I have one paper towel that I just crack it on and look, the shell just falls off and they're perfectly boiled every time and you can even pull the shell off in one big swoop. I love cooking these in the Instant Pot and then I'll just rinse them real quick in the water to remove any excess shell and set them on a separate paper towel. That way I can dry them really, really good before I package them up. How I store my eggs once I've peeled them is I put them in a Ziploc bag with a paper towel. That way the paper towel absorbs any extra moisture that I didn't get off my eggs when I dried them. But this, you guys, the best, easiest, fastest way to boil eggs and I'm not kidding you, they come out perfect literally every single time. So once all my eggs are peeled, I'll grab another paper towel and I just kind of really quickly dry them off and then in my Ziploc bag I have a paper towel and I just throw all of my peeled eggs in here and they're good to go for the week and they will last the entire week even longer. Actually, if you put the paper towel in to help absorb any extra moisture. We're gonna get our quinoa going. So in my measuring cup here, I have one cup of dry rinsed quinoa. The one at Costco comes already rinsed so it makes it so much easier. You don't have that extra step. And then to that we're gonna add two cups of our fat-free milk or almond milk, whatever it is that you're using. We're gonna go ahead and bring this to a boil and let our quinoa cook. Once your quinoa and milk come to a boil, we're gonna go ahead and put a lid on it and we're gonna let this simmer for about 15 minutes. Or until the quinoa is cooked completely through. And our quinoa is done. Look at that perfectly cooked quinoa. So to this, we are going to be adding in the zest of our lemon. So I went ahead and just zested my entire lemon. So we're gonna go ahead and add that. We're also going to add in about a cup of fresh blueberries and again, you can use frozen whatever your preference is. And then we're going to add in two tablespoons which I weighed out on my food scale of raw honey. And then we're literally just going to stir this together and our quinoa bowl is just about done. So once your quinoa is done, look how good this looks. It smells really sweet from the honey. We're gonna go ahead and divide this evenly into four bowls. This recipe makes four servings. So I'm just going to start with just a little bit at the bottom of each of my meal prep bowls. And then I'll make sure that I get as close to even as possible for all four bowls. Ah. I ain't trying to, I see you dancing on the polar eyes. Once you have everything divided out into your bowls, so this is a good amount. And remember, we're pairing this with a hard boiled egg for a little bit of added protein. We are gonna go ahead and package up one tablespoon of our goji berries. So I have my little to go containers here which makes it really easy. I'm just gonna slip that into my bowl and then I have a tablespoon here and I worked into my points one tablespoon of the berries which is actually, I count as zero points because it's literally just a berry. There's no added sugar or anything in these but you can count it and I think it would be one point for a tablespoon. So however your, whatever your preference is if you wanna count it or not. So I'm just going to pop my little containers in with my quinoa breakfast bowl here and then put a lid on this. And when I'm ready to eat it, I will just pour the goji berries right on top and these are so good you guys. You can really use any berry. You could add jam. You could really do whatever you want to top it but I really like these goji berries so I think that's gonna be the perfect pairing. So there is what my breakfast quinoa bowl looks like.  So here is breakfast. So I have one of my breakfast blueberry quinoa bowls. If you count the tablespoon of goji berries these are six smart points on both blue and green and four smart points on the purple plan. If you choose not to count the goji berry you can go ahead and just deduct a point but think about what you're going to add as a topping and really construct your points from that. So I would say that the bowl itself is five smart points on both blue and green and three on the purple plan. And then whatever topping you choose is what's going to add those additional points. So I'm gonna go ahead and count these as six and then I have my hard boiled eggs here ready to go for the week. I'll just have one of these with my quinoa bowl for just an extra bit of protein for zero smart points. So this breakfast you guys clean whole food I can't wait for it is only six smart points. For lunches this week I'm going to be making French onion chicken meatballs. I am so excited about these. I am going to pair these with some roasted vegetables. So I'm first going to show you what is in the meatballs and then I'll show you what I'm going to roast up as far as vegetables go. You're going to need some sort of bread crumb or binder. I'm gonna go ahead and use my simple mills crackers. I'm just going to crunch these up just because these have really good ingredients and they'll serve as a really good binder for my meatballs. You'll need some garlic, salt and pepper, thyme and parsley. You can use fresh which would probably be ideal. And onion, butter. I'm going to be using this melt plant-based butter. I am going to give you the points using this butter and the points using light butter for those of you that aren't fully on the clean eating side of things quite yet. And then chicken, sausage, better than bouillon. This is what I'm going to be using to make beef broth. Again, this is something I probably won't continue to buy but I wanted to use it up. And I have some organic shredded mozzarella. So let's get started on the meatballs and then we'll get those vegetables ready to get roasted. So let's get started on the meatballs and then we will actually make the sauce. So in my bowl here, I went ahead and put my one pound of the chicken sausage. This is the Incerno's Italian chicken sausage. I just decided to use that because that's what I had on hand. And I think those Italian flavors will be a nice touch in the meatballs. So we're going to add all of our ingredients directly into our chicken. So in my little bag here, I have 24 of the Simple Mills crackers. I went ahead and broke those up. And again, that's going to act as the binder in my meatballs. And then I have one half of a cup of the organic mozzarella. I just weighed that out on my food scale. We're also going to add some parsley. And again, I think fresh would actually be better. I kind of wish that I had fresh parsley but it's not quite that time of year here. So once the weather gets a little bit better, I'd love to actually have an herb garden. It's something I'm thinking about. So if you have availability of fresh parsley, highly recommend. And then one egg that I just went ahead and beat quickly with a fork. And then we're going to add in some minced garlic. And I'm just going to put in just a good spoonful. I like a lot of garlic in my meatballs. And then lastly, we're going to put in just a dash of salt and a dash of pepper. And then we're just going to mix this together. And these are going to be our meatballs and yum. You guys, we have cheese in our meatballs. I'm so excited. I'll probably end up digging in with my hands when I actually go to form the meatballs. But there you have it, that was easy peasy. And let's get ready to get these cooking so that we can make the sauce. So for the meatballs, I have a sheet pan here. I lined it with some parchment. Here is my meatball mixture. It looks amazing. I'm going to try to get about 16 meatballs. I'm going to roll those out and get them here on the parchment so we can put these into the oven. I see you dancing on the ballerites. Just want to have a good time, don't you? It all goes so long. So here are the meatballs. These look so good. These are going into a 425 degree oven for about 25 minutes or just until they're cooked through. And in the meantime, we're going to make the sauce then do our veggies. For the sauce for our meatballs, I have four tablespoons of my melt butter. I did go ahead and just weigh that out on my food scale. We're going to go ahead and add that to our pan. Let that kind of get melted down a little bit and then we're going to stir in our onions. Once your butter is melted, you're going to go ahead and add in your onions. I went ahead and chopped up two. They were rather large sweet onions because we want a lot of onions on our meatballs. And we're going to let these cook for 20 to 25 minutes. Until they are kind of a golden brown. For veggies with my meatballs, I'm going to be roasting up four different vegetables. First, I have some organic butternut squash here. I picked this big container up at Costco. It's such a great deal. I'm going to not add a starch to my lunch because squash is kind of a starchy vegetable. I was going to roast potatoes, but I think just having the squash is plenty for me. So I'm going to roast up the squash. I also have this bag of Brussels sprouts. My husband doesn't like them and I love them. So whenever I can throw them into a lunch meal prep, I'm in. I also have some organic baby bella mushrooms. I'm going to roast those in some organic mixed squash. I'm going to slice up some of that, throw it all on a sheet pan, season it up, and I'm going to pair this with my meatballs. So the first thing I need to do is chop. So I'm going to go ahead and chop up some squash, my Brussels sprouts and my mushrooms and get these on a sheet pan. My butternut squash is good to go. So I'm just going to go ahead and add it directly to the sheet pan. Okay, I cannot wait. Look at this delicious platter of veggies. Squash, Brussels sprouts, zucchini, yellow and green and mushrooms. So what I'm going to add to this in my container here is some avocado oil. I'm going to add about a tablespoon and a half. Mine comes out really, really slow. So I'm going to put about a tablespoon and a half. That's going to add one smart point per serving to my vegetables. So instead of these being zero points, these are going to be one point and I am completely fine with that because I'm all about getting in those healthy oils. So we'll add some avocado oil. And then for seasonings, I'm going to use the brand new everything but the Elliot from Trader Joe's. I'm excited to try this. It's salt, chili pepper, cheese, chipotle and cumin. And I have a feeling it may be kind of spicy so I'm not going to put very much because I don't like a lot of spice but I really wanted to give this a try. I'm also going to add some of the Trader Joe's onion salt just because I love the onion salt and I ended up not putting any onions on my platter this time of my roasted vegetables.   Now I've talked about Dax a lot on my channel but you guys, now that this clean eating thing is become my life, this seasoning is stellar in ingredients. No MSG, no artificial anything. This is the best seasoning you can pick up and they have over 20 of them. This one as you can see is the green zest. And what is in this is spices, dehydrated onion, dehydrated garlic and lemon peel. That's it. And it is amazing on vegetables. I load my veggies up with it. Definitely check out Dax. Again, they have over 20 seasonings. My code here on the screen will give you 10% off and free shipping and just pick up them all. I mean, every single one of these is delicious. So there are my seasoned oiled vegetables. As soon as my meatballs come out, we'll get these into the oven. Once your onions are done, you're going to go ahead and add in your minced garlic and just give that a good mix. Let that kind of cook down. Ooh, you guys are steamy. There you go. Let that cook down for about a minute or so just until your garlic starts to become fragrant. This smells so delicious. I'm going to go ahead and add my two cups of beef broth that I made out of my better than bouillon. And then I'm also going to add in some thyme. And again, darn, I wish I had some fresh thyme. I'm going to put a decent amount because that's really the only spice in here other than some salt and pepper. So I am going to put in just a pinch of salt and a pinch of pepper. And then we're going to let this cook down for just a few more minutes, probably about 10 to 15 until it begins to thicken. And it will thicken up a little bit as it kind of sits here and cooks. And that's going to be some delicious gravy to top our meatballs. You can see my sauce has gotten nice and thick. I ended up having to add a thickening agent. It just wasn't getting as thick as I wanted. So what I ended up adding is this arrowroot powder. I picked this up from the Thrive Market. So again, another great purchase from Thrive. But this is a all-natural thickening agent, much better choice than corn starch. So I literally just added about a teaspoon, mixed it with some water, made a roux, and added it here to my onions. And now I've got that nice thick sauce. So that's kind of the consistency that I was looking for. Oh my goodness, look at these meatballs. I am so excited for these cheesy goodness. So I'm gonna let these cool for a little bit and you can see here that my kind of sauce for my meatballs is cooking along. In the recipe, they want you to add the meatballs directly to the sauce. I'm not going to do that. I'm going to put the meatballs in my meal prep container and put the sauce on top. That's just what I'm going to do because I'm not adding any additional cheese as the recipe calls for, because I'm trying to save on some smart points. And you know, look at these. These, my friends, are cheesy enough. And I actually just tossed my roasted vegetables into the oven. So let's let this sauce finish cooking down just a little bit more and we'll put together our meal prep containers. All right, so I put my meatballs in my meal prep container. I did decide to put them in the small compartment because I'm going to add quite a lot of vegetables. Also, I decided to make five meal preps instead of four. The original recipe calls for four servings, but I wanted to have enough for each day and three meatballs is plenty for me because I'm having a lot of roasted veggies as well. And then here is my yummy onion, French onion sauce. So what I'm going to do is I'm just going to pour that like I had mentioned right over the top of my meatballs and just try to be as even as I can. That's going to just give that really yummy kind of French onion deliciousness to my meatballs when I go to eat them. And it helps keep your meatballs nice and moist as well because I did use a pretty lean cut of the chicken sausage. So I'm going to add all my sauce on and then as soon as these veggies come out of the oven, we'll get those put into the meal prep as well. Veggies are out, look how good these look. So I'm going to get these put into my meal prep container with my meatballs and I'll be back to show you the completed meal prep and give you all of the smart points. So here is my completed lunch. Look at how amazing this looks. So let me jump in and tell you guys about this week's lunch. So here are my roasted vegetables. Again, my veggies are zero points but I'm going to count one point for the avocado oil that I used. And I actually ended up having another serving of veggies that I'm going to have for lunch today. So it made quite a bit. And then I have my meatballs here. So I have three meatballs with one fifth of the sauce that I made over the top of my meatballs. Now the original recipe was for four servings but you can see here that I ended up changing that a little bit and making five servings. I just think an extra meatball per day would probably be a little bit too much for me. So I'm going to give you the points for all of the different ways that you can make this week's prep. So as it stands in five servings for the meatballs it is going to be five smart points on both the blue and purple plan and seven smart points on the green plan. And then of course that additional point for the veggies. Now if you decided to go ahead and just do four servings it would be six smart points on blue and purple and eight on green. If you sub out the butter I used for a light butter it knocks it down for four servings to four smart points for blue and purple and six for green. I will list all of this information down in the description box with the recipe. So my lunch is five points for the meatballs, one for the veggies, that's six points. I'm going to have some pineapple. I picked this up at Costco. It's delicious. And then I'm going to have one of my organic dark chocolate scores with blueberries. Again, I picked these up at Costco. You can have one of these for two smart points. So that makes my lunch five for the meatballs, one for the veggies, two for the chocolate. That makes this an eight smart point lunch. First we treat this week. I was going to make those no bake workout bars but I tried and tried to get the points to a level that I would actually eat them and eight points for a little bar. I wasn't going to do that. So I switched up my recipe and I'm actually making a no bake protein ball. This is almond joy. It's going to be delicious. These are far more reasonable when it comes to smart points. So let me show you what is in our protein balls, almond joy, and then we'll jump into making these. First you're going to need some protein powder. I love the devotion. This is pretty clean ingredients. It doesn't have entirely clean ingredients, but it's decent. So I'm going to use it up before transitioning to a fully clean protein powder, but I love devotion. You guys know that the angel food cake, the brownie batter, they're delicious. They make great protein shakes, great baked goods. I do have a discount code for devotion that you can get 10% off. I will put that code here on the screen in the description box is the link, click the link. It'll take you over to devotion and then you can simply enter my code and get the 10% off. So this is the brownie batter. That is going to be the chocolatey part of our protein ball. Also, I have some organic oats from Thrive. One thing about oats that I didn't mention when I was talking about my staples and didn't really add oats to that, but if you are going to eat oats, it's recommended to have them organic because they are heavily sprayed with glycosate, which is the ingredient in Roundup. So oats is one thing you should really buy organic if at all possible. That's just my suggestion. It's fact that they are sprayed heavily with pesticides. I'm not telling you what to do. I'm just simply saying that it would be smart to buy your oats organic. They don't cost very much more, especially from the Thrive market. And again, the link for Thrive and the $20 off is down below. So I have some organic rolled oats, unsweetened coconut flakes, Lily's chocolate chips, raw honey, and then my favorite nut butter from Costco. So let's put together these no bake almond joy protein balls. So for these protein balls, it's very simple. We're literally putting everything into one bowl. So first I'm going to add a cup and a half of my rolled oats. To that, I'm going to add two tablespoons of the unsweetened coconut flakes. I'm also going to add two scoops of the devotion protein powder. I haven't done that measure that quite out yet, but we're gonna go ahead and do two scoops of the protein powder. So there's one. And that's what's gonna give us this yummy chocolate of the almond joy. Two scoops of the protein powder. I also have four servings of the Lily's chocolate chips. And then lastly, we have the good stuff. I have my raw honey here. I did go ahead and measure this out as well on my food scale. It's about a half of a cup of the raw honey. I know that seems like a lot, but this actually makes quite a bit of the protein balls. And then I also have measured on my food scale, one cup of the mixed nut butter from Costco. That was a lot of my nut butter. It made me sad. I'm gonna definitely have to pick up a lot more of that. I am loving it. It's so delicious. So there's one cup of that mixed nut butter. Now with the raw honey, it is a little harder to combine than the traditional honey, but you're just gonna have to put some muscle in here and get this stirred together really, really well. And then we're gonna roll this out into 16 protein balls. So let me get this mixed and then we'll roll out our snack for the week. Once you get everything mixed together, you can see I went ahead and just ended up using my hands. We're gonna go ahead and roll these into 16 protein balls. So they're gonna be fairly small, but you guys, these are packed with such great whole food ingredients.  It's gonna give you that nice, big kick of protein from the nut butter, but you get the sweetness and health benefits of the honey, the oats, and then of course the Lily's chocolate chips.  All right, here they are on this messy tray, but I ended up getting 20, no, 30 of the protein balls. So it made the points half what they originally were, which is super exciting. And you guys, this is plenty. Considering what's in here is the nuts, the oats, the nut butter, you also have honey. I mean, all of the good stuff that's in this, this is plenty big. So if you were to get 16 balls, so you could almost double the size of these, they would be four smart points apiece. Now, because I cut my recipe into 32 or 30 balls, it makes them only two smart points apiece. So this is a great snack option. I mean, great, these are delicious. They literally taste very similar to an almond joy. They are so good. These would be great in your kid's lunch as well because it's a sweet treat, but it's made from the best wholesome ingredients. So I can't wait to munch on these all week for two smart points. So here is what I'm gonna be having for snacks for the week.   I cannot wait for you guys to try these and what a great wholesome treat. So I'll be having those probably one a day when I'm working out or maybe as like part of my afternoon snack. I'm also going to be having some yogurt. This is the Trader Joe's plain whole milk yogurt with the cream on top. It's seven points for a cup, but I probably will just do about a quarter of a cup or a half of a cup. And then what I'll do to sweeten it is add some of my 100% fruit huckleberry jam. Somebody asked what brand this is. It's Wild Huckleberry Mountain. Here is the website if you're interested, but I don't think you can buy this anywhere about the Pacific Northwest because this is where huckleberries are grown. So we have literally huckleberry everything. But what's great about this jam is the ingredients are literally wild huckleberries, all fruit concentrate, fruit pectin and lemon. So I think it's one tablespoon for one point of this. So I'm just going to mix that in with the yogurt and that's going to make a really good high fat satisfying a snack. And of course, I'll be having my Bilt Bar. You guys know I love these. Morning snack, my friends, every single day. So I pulled out just a couple of my favorites to show you. So first is the coconut almond. This is literally an almond joy. It's that good. It reminds me very much of an almond joy. So you can have almond joy protein bites, almond joy Bilt Bar. These ones are three smart points. All of the Bilt Bars with nuts with the exception of the coconut almond. So for example, the peanut butter here, they're going to be four smart points. The toffee nut is four smart points. All of the nut bars are four points. All of the regular non-nut bars with the exception of coconut almond are three smart points. So just kind of depending on the points that you want to spend on your snack. But I love the peanut butter especially. It's worth the four points to me because it has 20 grams of protein, seven grams of fat. So it keeps you nice and full and it has a real organic peanut butter. So it's actually one of my favorites and I will take the extra point for it. But again, you have options between three and four smart point Bilt Bars. So head on over to Bilt Bar, check them out. My code here on the screen will get you 10% off and free shipping. And save my code, write it down because you can use it over and over again. And there is a link in the description box that will take you directly to Bilt Bar if that's a little bit easier for you. So those are going to be my snacks for the week. Thank you for joining me on another weekly MyWW Meal Prep. If I didn't mention I am on the blue plant and I do follow a clean approach to weight loss, which means my food that I eat is considered clean or mostly clean food. Basically what clean eating is, is eating foods that come in their most natural state. So very minimally processed, zero fat free, zero sugar free, that type of thing. So my recipes are going to include whole real ingredients. However, down in the description box, which is where you'll find all of today's delicious recipes, I will write out any modifications that you can make to lower the smart points. That way, if you are still interested in some lower fat options, fat free, sugar free, I try to share those with you guys as well. Whatever I can do to help make your journey a little bit easier and still eat really delicious food here on WW. So again, all of the recipes are down in the description box, as well as the links to my favorite things and some of the codes that I have available to help you save some money on those favorite things. And lastly in the description box is the link you head on over and join my Facebook group. You guys, we have 14,000, actually I think 14,300 members in our Facebook group, which is crazy. It is very loving and supportive. It's a great way to interact. I'm on there regularly.  So just click the link down in the description box and head on over and join us. Also, if you're new, welcome, welcome. I do a meal prep every single Monday. In fact, I do upload most days of the week.  If you love meal prep, give this video a big thumbs up and leave your comments down below. Let me know what you think of this week's recipes and which ones do you have to give a try. Thank you guys again so incredibly much for watching. Have a great rest of your day and I'll see you in my next video. Bye.


Multiplayer is one of the biggest features in Pokemon Scarlet and Violet and a lot of people had a lot of questions about all the things You can do because it was really confusing So what I did was I did a lot of testing and I provided a lot of answers to a lot of questions You had on multiplayer and I just want to note that this was tested in local multiplayer Which means it's most likely going to be applied on multiplayer online. Let's get into it number one How do you host an online session press X and open the poke portal if you are local You don't have to connect to the internet and if you are trying to go online Just press L and then open up the union circle after that You're going to form a group and then you'll get a link code if you are the host with that link code You're then going to provide that to anyone who is going to join your world Remember if the host disconnects the whole entire session ends So make sure the person who starts this session has very good internet connection If you're trying to join the online session Just follow the exact same steps of opening up the poke portal union circle and inputting the code That the player host gives to you after that you'll start your game And you will teleport into a random spot in the game that it decides you to be in where the pokemon center is The pokemon and all that and you can see each other in game and wave at each other and emote And just like that you have been connected to each other can pokemon scarlet and violet players connect to each other Yes, they absolutely can and if they couldn't there would be something wrong with game freak and pokemon for not letting you do that So you can connect with the opposite version of the game and because we can connect with the opposite version of the game A lot of people were asking can you capture scarlet and violet pokemon that are version exclusive? I would like to give you some great news the answer to that question is yes You can absolutely catch scarlet and violet pokemon version exclusive now for this example My player was from pokemon scarlet and the other player was from pokemon violet and when I joined the pokemon violet world We went to a location and when we climbed up that mountain We were first able to see a bunch of larvitar which was strange because this was pokemon violet world After we caught that larvitar then all of a sudden we saw baggan and then baggan is a violet exclusive And I was seeing that in the same game as a larvitar. They were both there So what we then discovered was that all the version exclusive pokemon in the overworld Are tied to the players walking around and spawning them in and that's when we also wanted to test out by going to the desert And when I walked in I saw a stonejourner and the other version captured that version exclusive Which is scarlet and then we walked all the way north to a beach where we saw a bunch of ice cues We also caught them there, which is a version exclusive to pokemon violet So based on this information you only have to connect two opposite versions once And you don't have to go back and forth and try to get the opposite version exclusive So that myth is put to rest just have two versions connect together And you'll find all the pokemon spawns you need to do you have complete free roam Or do you have to stay in proximity to the host? You are allowed to do whatever you want and go wherever you want in the host world Speaking of proximity You should stay in proximity to the channel by hitting that subscribe button and helping maraidon catch all these dragon pokemon Seriously, you don't want to miss videos like these because these are going to be game changing for you on your adventure in paudea So please subscribe how far away can you be from the host? You can be as far as you need to be from the host Just keep this in mind that you will initially see each other on the map If you open the mini map you can see the player icons as you start to get further out from each other You will then see the icon separating further if you get really really far the game might glitch a little And you might not see the players icon on the map if you go to far extreme ranges But don't worry you are still in the multiplayer session unless the game tells you you have disconnected from it I've also noticed things that if a player enters into a gym area Or if a player opens a picnic or enters an instance of something in the game You won't see their icon on the map anymore But you may see something on the right side saying that they may be catching a pokemon Doing something the icons notify you the activity of the other player This one went a little viral on twitter when i tweeted it out But can you steal someone's shiny pokemon in multiplayer? The answer to that question is absolutely Yes, if a shiny pokemon spawns in the game and you both see that shiny pokemon It's really going to be who is going to interact with that shiny pokemon first A great example over here is with this lovely new electric flying type pokemon that we encountered on a north beach We both saw the pokemon and to test this i interacted with it since i was connected to the violet world And i bumped into a shiny and it was shiny and the other player just has to watch in the background With absolute sadness as you catch this pokemon they cannot interact with it It cannot bump into it once you have engaged in battle It's done and once you catch the pokemon It's yours and the other player can just stare at it when you throw out the pokemon and be like yo I got this shiny can you do group picnics? You can absolutely join each other's picnics in the game Now there is going to be something called the picnic host What this means is it's anyone's choice to start a picnic in the world The first person to start the picnic in that world is going to be called the picnic host at that point Nobody else can make a picnic because the game will notify you that someone already started a picnic There'll also be an icon indicated on the left of the screen saying that someone started a picnic The only way you can join that is by going to them Entering into their picnic by confirming and then being part of it The picnic host is going to be able to have all six of their pokemon out at the same time Anyone else who joins can only bring out the first pokemon from their party So this applies when you even have more players It's only going to be the first pokemon in your party if you're joining and you're a guest to someone's picnic Can sandwiches that the host makes boost other players the best one to test this out was with encounter power Now what we did was we for example If you were to eat something that boosted electric type then when that person walks out And starts to spawn pokemon around them because of the encounter power being up for electric pokemon Those spawns will be electric pokemon kind of sound familiar to how the version exclusive pokemon spawned around those players It's almost exactly the same thing and it kind of gives you an idea how spawns start to work in the game Now it doesn't have to be the host to initiate this boost. It could even be the guest player So anyone can initiate this sandwich that will boost something and people will benefit from the players Are around the next question coming up is can you stack encounter powers together now? Not necessarily stack them But each player will be able to cause an increase of something and if you take both those players And put them together then both the encounter powers of their tables are going to line up around the spawns around them If that makes sense at all for example I was able to get electric sandwiches and the other player my wife She was able to get fire sandwiches or a fire encounter booster We both got these boosters and then went out to test them wherever I walked first and spawned in pokemon They were electric wherever she walked ahead of me and spawned in pokemon They were fire types whenever we were in somewhat close of a proximity range to each other We started to see electric and fire pokemon in this case It was a bunch of pikachu and charcutette showing up on the map when we started to walk together to test this a little further We then just noticed it was non-stop Just electric and fire pokemon literally just next to each other at all times So this is a great thing to know And it is actually a big deal because if you join a world and that person is in the end game And they are making shiny sandwiches That you can benefit from that shiny sandwich because that player just has to walk in front of you And when that player walks in front of you and spawns in pokemon They have a chance of bringing out a shiny pokemon that you as the person who is around them And walk into and benefit from so they're going to control the shiny spawns and the boosted shiny spawns and that encounter type Well, you can also benefit from that if you don't have an encounter booster yourself If both of you have encounter boost stuff for two different types of pokemon Then everyone is winning and it's going to be a shiny fest of pokemon and spawns So have a lot of fun with that And I hope you guys can test that out and give me some feedback down in the comments below It might even get more crazier when you have four players joining a multiplayer or local world Can both players continue their own stories while playing together? The answer is yes You cannot team up for your own stories together, but you can do stories at your own pace together We wanted to test this out in the most extreme way So what I did was I had a new switch copy from pokemon scarlet play with another copy From pokemon violet who was already in the end game that end game had gym rematches only on the map And on my map I had all the team stars available all the gym leaders available and all the titans I have not touched one of them So on my map I could see all of them and on the other map you can only see the gym leader rematches the test is further We went inside of the glaciato gym to see what would happen in my copy of the game It was a regular empty gym where I had to talk to the receptionist And they give me the gym challenge in the violet end game They saw gruscha who was waiting for a rematch if that helps you confirm that stories are completely different Well, then there it is and you can do whatever you want But you can also play together speaking of end game players if you join an end game players world Another question was can you buy the items from that end game players world? You cannot if you go to the pokey mart and look at the items. It's going to be your own world's items I did this on my pokemon scarlet and I had the base items you get when you have not completed any gym battles At all in the game while the violet player is going to have a huge list because they completed everything Can players do mass outbreaks at the same time players can absolutely do mass outbreaks at the same time But the mass outbreaks are going to be tied to the host of the game So whatever they had naturally in the world is going to show up when they host the game for you So you're going to see their mass outbreaks You can both approach those mass outbreaks together and start auto battling In fact mass outbreaks are so big that having two players will probably make the job easier And if you have to get a shiny pokemon, it's going to be a fight for it So maybe you can negotiate and decide with your friends and other people you connect with online Which person is going to get what shiny pokemon first now? I do want to talk about one version exclusive pokemon armor roots and serral edge This one's a little different because you need an item that you can buy from zapa pico town in my game When I joined the pokemon violet version, I walked into that area where there was a Vendor that gives you the armor unfortunately for my luck. It was not the violet vendor It was my same exact guy from pokemon scarlet and for the violet player They're going to see their exact same lady from their game giving them that specific armor So if you want to get serral edge, you're going to have to join a terra raiden Or you're going to have to have that person trade over a pokemon that is carrying the item That gives it to you so you can evolve it and have your original trainer name on that opposite version exclusive pokemon How do terra raid battles work in multiplayer? When you are in the host world, you cannot interact with any of the terra raid dens This basically means you cannot start the matches What you can do is you can go up to them touch the terra raid dens and grab lp But it's up to the host to start it now Let's just say you're somewhere else completely in the game and you're doing whatever you want And then the host clicks on the terra raid battle and initiates it You will then get a pop-up on your screen saying that person has started a terra raid battle Press this button to join which is really cool because you could be doing anything else you want and that icon appears And you hit join you do all the menu stuff and just like that you battle it out And then when you're done with that terra raid battle, it just teleports you back to whatever each player was doing in the world So remember terra raid battles are controlled by the host Also, if you are a new player and you walk into the host world those terra raid dens are going to be the exact same For the endgame players So maybe you can get a little bonus experience canes and stuff by having a good player Carry you through these terra raid dens in their world. How do pokemon interactions work? We've talked about shinies that if you catch a shiny it's not going to be there for the other person But this also applies for any single pokemon that you interact with the moment that you engage that pokemon in battle It is going to disappear for the other player Whether you destroy it whether you catch it or here's the worst part if you run away from it Yeah, if you bump into a shiny pokemon and then decide Hmm, I want to give this to my friend and you run away that pokemon will despawn So please be careful when you're doing these things If you see a shiny pokemon and you want to be nice to someone don't interact with it Just stand there look at it do an emote and clap or something and have that player run up to that pokemon another glitch with pokemon interactions is When you go to a new area and you get the sign or symbol saying you've entered area one area two Certain pokemon will just disappear right off the bat when I was running around in my game on my karaitan mount I was walking towards a town and I just noticed a shiny lechonk and by the time I was going to turn around I initiated the screen that said area one or something like that that you could see in this footage And you could see that little lechonk in the corner. I missed it and it just poofed off my map It's gone. It's never coming back. They disappear So please be careful when you're getting close to an area in multiplayer because that it's very sad You just don't don't let that happen to you when everyone disconnects from the world What happens when you disconnect from another player's world your screen will just return you back to your own world You will no longer see their mass outbreaks. You will no longer see their terror raid dens and any Combined version excuse the pokemon will disappear off your map And you would just be stuck in your own world all lonely playing single player But no one considers shiny so that's a good thing right and I traded my friends world You can trade in your friends world by opening up the multiplayer menu And selecting trade and then enter a link code and boom you can trade with somebody And you also battle with your friends. You can absolutely battle with people in their friends world All you have to do is plug in a code Initiate the battle set up the rules and it's going to be whoever hosts the battle whoever started the link code for the battle And what's going to happen is once you set that all up select your team You're then going to be teleported into the school yard You're going to have your fight there and once you're done You're going to get teleported back to wherever you were before you did that and it's going to be right In the other player's world that you joined ahead of time If you're watching this video up to this point You are very lucky because now you know that you can breed pokemon with your friends. Yes, that is possible I didn't believe it at first But you can now the way to do this is we both first got egg power level one And then we joined into the picnic the pokemon violet player My wife had ditto and I had armor root and what they did was they started to make eggs And both of us started checking the basket and eggs just started to appear and fill into the basket Which is really cool. And by the way, I think it's up to 10 eggs that can stack at max So you can just afk there 10 eggs show up 10 eggs show up and you guys can run around the entire world and start hatching This is really big for breeding because this can imply that players with high level dittles or five IV dittles or six IV dittles can add an item onto the ditto and start passing down those IVs to the baby pokemon In fact, it's like a group breeding activity And you and your friend can be the proud grandparents of your baby pokemon that your children pokemon made If i'm saying that right but breeding's in the game And there's a lot of mechanics that go along with it But I will be discussing all the mechanics for this in a breeding guide Speaking of doing things together in picnic you can also make sandwiches together So you don't just have to make your own sandwich or buy a sandwich from a marketplace You both can sit there and create the exact same sandwich together in a multiplayer sandwich menu You just have to queue up It'll say one out of one players one out of two players one out of three are joining in And anyone who joins in is going to be able to partake of that sandwich and the board players A bigger the sandwich is than the single player one and you will all have the exact same boost And now you already know how boosts work in the game when it comes to things spawning around a player Can you catch each other's version exclusive paradox pokemon? The answer is no they do not spawn it's your own version paradox pokemon that will spawn for your own version That's it. The only way to get the opposite version is by having another game copy or by trading them over Those are the only two options There's just so much to do in this game and it's overwhelming So make sure you go ahead and check out this video right over here to help you in your game


Hi everyone. Thanks for joining. I'm Kellan Betz, a course lead in the MITx MicroMasters and SCM program here at MIT Center for Transportation and Logistics. I'm co-hosting today's live event with my colleague Laura Alegha, also a course lead in the MicroMasters program. Today we're very fortunate to have Dr. Sergio Capiero with us, a senior data scientist at Amazon. Welcome, Sergio. Hi, Kellan. Hi, Laura. Thank you for having me. It's always a pressure to participate in live events and be in touch with the MicroMasters community. Awesome. We're very fortunate to have you. Before we kick things off, we wanted to kick the event off with a poll for those of you who've been to our events before. We like to start things off with a poll, and so if we could launch our first poll here. Awesome. And so we want to learn a little bit more about our audience today. Why are you here today? I'm a few options here. I'll just go through a couple of them. I want to learn about spy chain data and analytics in general. I'm interested in knowing more about data preparation processes and strategies. I want to know more about using data from machine learning. There's lots of interesting topics. Hopefully we'll cover many of these, but we wanted to give you a chance there for a few minutes or so to fill out that poll and why I do. I'll pass it to my colleague, Laura, who will go through a brief agenda for today's session. Thank you, Kellan. Welcome, Sergio, and welcome everyone in the audience. During the next 15 minutes, Sergio will talk about the importance of data preparation to train accurate machine learning models. We will also discuss about the challenges typically encountered when preparing data, and then we will share some best practices that may help you. Kellan and I will then ask a bunch of questions we have prepared for him, and we will save time at the very end for your questions. Please make sure you use the webinar Q&A feature to ask the questions, and be sure to be logged with the name because we will not read any anonymous questions. So just be ready to participate. We want to make this a very interactive event. And with that, I guess it's good timing for going to the poll results. So it seems Sergio that most people here want to learn about supply chain data and analytics and learning about data preparation. So I think we are ready to kick it off with you, and that probably you will address all of this. So hopefully the audience will be very happy to be here today. Okay, so let's get the party started. Awesome. So let me share my screen. Okay, so let's get it started. So the main topic of today is going to be about data preparation for supply chain analytics with particular focus on how we can be ready for a machine learning. So in the last decades, we've seen how machine learning has expanded to the consumer industry. So basically, many of the products that we buy and the services that we use somehow embed some kind of machine learning algorithm. And this trend will continue to accelerate in the future. So for instance, so machine learning is embedded in these voice control devices that can play music or read the news, or we can just use them to order online groceries by talking then from anywhere from our houses. So every time that we use Google and start typing a search term, and Google recommends possible search terms, that's also a machine learning in action. Online details such as Amazon and also using or collecting our data, our preference and also buying habits in order to tailor their shopping experience to our needs. And similar machine learning algorithms are used every time that we watch a movie or listen to music in these online streaming services. So for instance, for instance, the music streaming services are collecting our listening habits. So basically the music that we listen to and also our preference in order to suggest new music or new songs that we might like to hear as well. Also, we've seen the application of machine learning in services such as banking as well. So every time, for example, that there's an unusual large transaction happening in our bank accounts. So typically we receive an alert message through a text, alerting about these uncommon behavior. And also in transportation, all the navigation services such as Google Maps. So every time that we are looking for the quickest path or the nearest path to a location, also transit time should be estimated. And this transit time estimation are mainly based on machine learning models. So one of the key contributor factors to seeing this expansion of machine learning in the consumer industry, of course, is the data proliferation. And this is reflected in the amount of digital data that is being produced. So basically on a daily basis, roughly 2.5 quintillion bytes of data is being generated. So 90% of the worst data has been produced over the last two years. So this means that every two years, the amount of digital information that is out there is being doubled. And of course, supply chains are also generating massive amounts of data. So for example, Amazon sells roughly 480 millions of unique items to close to a quarter of a billion customers. So Walmart handles more than one million customer transactions on every single hour. And on a daily basis, UPA is delivering more than 20 billion packages to roughly 8.4 million delivery points. And you can imagine that these companies are generating a vast amount of information, right? And this information not only about the product itself, the different characteristics of the product that are being sold and delivered, but also information about the customers. So basically what are the preference, what are they are searching for? What are they buying? And also information related to supply chains, right? Information about what are the transportation legs that these are that we are using? What are how the road routes and stop look like transit time, et cetera, et cetera. So behind all of these massive numbers, we can see that there's also massive information that is there. But it's not only volume. So in these figures, we observe that supply chains or large supply chains are generating massive amount of information. But it's also the speed and the variety at which the data is being generated. So basically on every single second, supply chains are generating different types of information. And also the type of information that we're generating is much broader now. But what is machine learning? So basically, machine learning is so disruptive because it offers a fundamentally different approach to program computers. So the traditional approach to teach, for instance, a computer, a new task is a focus on codifying existing knowledge. So basically, what we try to do with the traditional approach is try to reflect what is the knowledge of the programmer and trying to translate that knowledge into a different lens of code. So basically, we're abstracting a task based on the knowledge of the programmer. And we are writing a few lines of code to execute the task so that the machine or the computer will follow these lines and perform the task. But basically, what we are doing here is trying to reflect the knowledge, our knowledge into a few lines of codes. And of course, this has the main weakness. And that weakness is related to what is known as Polanese paradox. So basically, this paradox says that we know more than we can tell. So meaning that so our knowledge is so extensive that we might know how a certain task works, but it's difficult or sometimes impossible to translate this into words. For example, how can you tell a computer how can the machine or the computer recognize a cat face. So it will be almost impossible to write a code in order to perform that identification or visual attacks. If it is possible, it will require a thousand of lines of code. So this is where the machine learning comes into play. So basically, machine learning offers a paradigm shift. In this case, machine learning involves programming computers in a different way. So basically, we are asking the machine to learn from data and from past experience. So this approach will be similar to the approach that we use with our quick, for example. So if I want my child to recognize a cat to learn what's the cat, I will show a cat in different contexts. So every time that I see a cat, I will tell him, so this is a cat. And she will understand what is the meaning of a cat in different contexts and will be able to recognize. So this is similar approach in the sense that we're going to be offered into the machines, to the computers, so different examples. And we'll tag them as cats or different animals in such a way that based on this experience, based on this exposure to different images, so at the end, the machine will be able to recognize the object that you're trying to catch. Okay, so let's see how the machine learning process end-to-end works. So basically, everything started with some question that we want to answer. So we have a business problem that we're trying to solve. And based on this business problem, we start a data collection. So another beginning. So this data collection is basically collecting historical information. So that is information or data that we have accumulated over time. And of course, these data will come in different from multiple sources and might come in different formats. So we might receive information or data in numerics. So coming from tables, but also might get information, for example, that are coming from as text or even images. So the next step will be, okay, so we need to somehow, we need to prepare the data. As you can imagine, so the machine learning algorithm will require that the data is in specific format. For example, we need to provide the machine learning algorithms with enumeric data. So somehow we need to translate all the information that are coming from different sources formats into a single format. And this is typically what we do during data preparation. So we prepare the data in order to be ready for further analysis. And so based on the historical data, after we clean it up, we prepare, we will get a training data. So these training data are going to be the examples that I were referring to. This is going to be the images of our cats, or similar information that are going to be feeding the machine learning in order for them to be trained. And this is when the training happens. So we offer these examples. So we offer these samples to the computers. And there's going to be a training happening. So at the end, after the machine learns to recognize the cat, for example, so it will come up with a model. So basically the model is going to be just saying, okay, if I get an image, I would recognize if it's a cat or not. So basically it's going to be just a translation. So this is only one piece of the equation, of course. So at the end, so we start asking about trying to solve a problem. And we ended with a model. But of course, this is not the final goal. So we have a data set with an image of cat for instances, for instance. But we don't, we know already that information. So we know the training is set. So basically what we want the machine to do is to recognize new objects. So basically offer the machine new objects and see if the machine is able to recognize the object that is in the picture, for example. So that's why there's also another stream of data that we need to collect. So basically this is the live or ongoing the, of ongoing data, the search team. So again, so this is coming from the similar sources and it's coming from a different format as well. But the main difference with the historical data basically is that we are using, we're gathering this information live. And as before, this data also should be harmonized and aggregated. So basically it's going to be following the exactly the same processing steps that we did for our historical data. And at the end, so then we're going to be using the model that was trained. I'm going to be using this model with the new information that we have. Basically we're going to be using the model with this new ongoing data. And at the end, we care about the outputs. So we're going to be making the prediction on the new data. So it's, as you can see, so data preparation is not only key before training the data, but it's also very important to process our data only also when we're receiving this live data, because at the end, we mainly care about the output. We mainly care about using the computer or the machine in order to recognize new objects are not only in historical data. As you can see, so what we basically are doing in the model is a prediction. So basically, in this case, prediction will imply taking information of one kind and getting information of another kind. Okay, so basically what we're doing is we're providing, for instance, the object, the machine, okay, or a picture, which contain an object and the machine or the computer will tell us what is the object that is containing the image. So basically, we are getting this prediction. And the prediction could be just interpret as a simple A to B, A to B mapping. So basically, the model, they're going to be taking some inputs. So that will be the A. And then it's going to be based on the train model, based on that information that on the training data is going to be getting a response to B. So this is just an A to B to be mapping. So we're converting information that we know, so the image, the pixels on the image. And based on this information, we're going to get any information that we don't know, but we care about. And it will, in this case, will be the object that is in the image. And we can, we can, we can mention a different example for this. So for example, every time that we get an email, so Gmail, for example, needs to decide if the email is spam or not. So basically, based on different characteristics of the, of the emails based on the train set, we're going to be saying if the current email that we are getting is classified or is flagged as spam or not. I mentioned the example of the, of the image and the, and the object. A similar, a similar use case will be every time that we're going to be translating, we're going to be transcribing from an audio clip. So the audio clip is going to be the input. And then we're going to use it in order to get the task. So basically transcribing the audio clip into, into a text or the, or the other way around. So every time that we have a text and we ask, for example, word to read that, that, that text. And finally, in translation services as well. So every time that we're going to be translating for it, for instance, from English to, to French, this is exactly the, what is, what is happening. And in supply chains, we are typically, we're typically as associate prediction with the, with the future. So we make, so we look at historical information in order to make a forecast that will imply some behavior of the future. However, as you can see in, in all of these examples, so the, the third prediction in machine learning has a broader meaning. So meaning that prediction could be, of course, could be used to predict something in the future, to forecast something in the future. But also we can use this prediction for real time or even, or even the past. For example, every time that we get an email, so Gmail should make a decision, live decision in order to, to, to determine if the email is a span or not. So we're not forecasting about the future, we're forecasting about the, the pressing, we're making a prediction about the, the press. And same thing happened every time that we have an initial transaction in banking for, for, for instance. Another key characteristic of the machine learning algorithms is the, also the, that they provides more accurate predictions compared to traditional, traditional statistical methods. And this can be a, can be a better understood with these graphics. So in this graphic, what we're trying to reflect is the performance of the algorithm or the prediction. So you can say, for example, how accurate is the machine able to recognize cuts versus the amount of data that we feed. So basically based on the number of images or pictures are going to be provided into the machine to train. So the beginning, or if we use traditional machine learnings, so this will be the typical behavior of the, of the performance. So we start feeding with more and more data and we can see that there is a rapid increase in the, in the performance of the algorithm, right? There's a rapid increase in the accuracy of the algorithm. But at some point, so it reached the threshold and basically, regardless if we feed with more and more data, so the accuracy is going to be, is going to be basically the same. But what happened if we move to a more advanced machine learning? So what's happening? For example, we use a very small neural network if we use a deep learning. As we can see, again, we have a very gain, a very rapid increase in accuracy in performance. And we can feed more and more data and we can see that there is likely increase in the performance. And this increase can be even more drastic when we have a large neural network. So basically when we have millions of millions of neural, oh, neural, new neurons, we can expect a similar performance. Of course, there's a theoretical, a theoretical maximum of the, of the accuracy and performance. But the main point here is we can use these very advanced machine learning algorithms. So basically the performance or the accuracy that we can, we can achieve is, it's much higher. And of course, this implied using more and more data. As I was mentioned, machine learning requires data to be in a specific format. So most of the machine learning will require all the inputs to be as numeric. So of course, we can use a machine learning to image or text. But somehow we need to process that data in order to convert in the proper format to the machine learning to use. And of course, we will require some preparation in the data.  So regardless, if you have a perfect model, if we are feeding the model with information or data that is not, is not of good quality, of not precise, of course, the prediction that we are getting is going to be also uses or going to be misleading. So it's very important for the machine learning algorithms to have a quality, quality inputs. And usually when we talk about the data preparation, what we are referring to is mainly getting all the data ready for a, for a, for further analysis. And typically data preparation involves two tasks. On one end, we have data purposes, prepossessing. And on the other, we have data in general. And let's, let's, let me talk about a more in detail about this. So when we refer to data processing, basically we're using about specific tasks that we need to do on the data. For example, we need to clean the data. Somehow we need to replace, for instance, some erroneous values. We need to input misinformation. We might need to, we, we, we need to partition, for example, data days, and those similar structures. The first one is, of course, cleaning the data. And when doing the cleaning data, there are two main activities that we have to look for. So the first one is removing, removing outliers. So basically we're going to be looking for or identifying these are typical, a very high or very low, low, low values. And usually we need to define what is the better strategy for us based on the context of analysis to deal with this, with these outliers. The second thing is about duplicates. So basically if we, if we have duplicates information, we also might need to remove this information. But also data cleaning includes additional tasks. So for instance, replacing and missing the inaccurate data or even correcting missing values. So we might use some imputation techniques here. Also, as part of these data preposings, so we need to partition the data. So typically we will divide our data set in three different data sets. So we'll get a training, a validation, and also a test set. So as the name says, the training data set is going to be used to train our models. So the validation set is going to be used in order to tune the different parameter that the model might need. And finally, the test of the model, the test data is going to be used to evaluate the performance, for example, the accuracy of the model. And of course, we will need to somehow randomly create or divide our original data set in this through three subsets. Also, scale, it's important also that all the features, all the columns, all the variables are going to be used in the model are in a similar scale, in order to make sure that all features are equally important. And this is particularly true for specific machine learning algorithms, which calculate distances between features. So here are two strategies that can be followed. So we can normalize the data or we can normalize the future, basically meaning that translating or transforming our data into a zero, one scale. So basically, all the values of the new columns are going to be contained between this range, between zero or one. And the second option is to standardize the data. So basically, all the data is going to be having, is going to be centering to me, which is going to be zero, and we'll have a standard deviation of one. And finally, we can also explore data augmentation. So basically, this will be the strategy to artificially create data from existing data. And this is quite useful, especially when we have a small data set. So basically, we're going to be just creating new data or synthetic new database on the current historical information that we have. Regarding a future in generating, also, so in this part, basically, what we are trying to do is to identify what are the best future are going to be feeding into the into the into the model. So in the previous step, what we did just we cleaned the data, we divided in three in different in different sets, and we generate more data if it needs. But now it's time to focus on, okay, what are going to be the variables that are going to be using the model. And of course, there's a lot of there's a lot of visualization that can help can help here. Here, we're trying to identify what will be the more meaningful, a minimum, meaningful features or variables that are going to be using in the model. So it's a good way to identify these will be different and different analysis correlation analysis or different, different, different graphs. But also there are specific, specific tasks that I can perform during future in generating. And one is a feature selection. And basically, this is going to be just the process of trying to identify what are the best, the best features of the of the model. And this can be achieved through or getting an important score. So basically telling telling me or doing a printed analysis in order to identify what will be the more important features. But also we can run, for example, correlation analysis in order to see what will be the best choices to make. But basically here what we are doing is just filtering what are the most important features to be included in the model. We might also require to to transform somehow the data. So these tasks just implies and modifying the data, but keeping the same information. So for instance, we can somehow make some, some, some transformation in our data. It will might, for example, take the logarithm of the specific specific variable, or we might need to use Cartesian product, for example, and we believe there that may be a correlation between categorical, categorical value. So basically, we are just playing with the data and coming up with different additional features. And similar thing happened with feature creation. So in this, so here our feature creation basically involves a creating new data from existing data. So for instance, every time that we're going to use one holding coding, so when we translate a categorical variables into a dummy variables. So we are creating a new feature based on one holding coding, or every time, for example, that we calculate new features based on the current variables that we have every time that we do, we are creating a new feature. And finally, feature extraction. So this will be case is particularly in which we have a very large data sets and not talking about the examples or samples that we have, but regarding the features that we would have. And in that case, it might be a good idea to reduce the amount of data being processed, especially to consume less computational resources and have a model that runs faster.  For example, we can use PCI, which will allow us to reduce the dimensionality of our data sets. So basically, it's going to be just producing the number of colors that are going to be used. And with that, so let me stop here and let open to questions that you might have. Awesome. Well, thank you, Sergio. A great fascinating presentation really highlights a central role that data preparation really plays in the process and how it fits into that pipeline. And where we can start to then utilize some of these advanced techniques or some of these kind of transformational techniques like machine learning. Maybe before we start diving into some specific questions on data and machine learning, and I also see that there's a bunch of questions there in the Q&A. So that's awesome. Keep it up. Please jump in there with your questions if you have them. We'll keep an eye on that Q&A feature. But before kind of diving into some specific questions about data and machine learning, I wanted to start out with maybe a broad question on your experience and what you see the role for data and supply chain, supply chain strategies, supply chain designs, supply chain operations. What role does data and machine learning play in these different functions? Okay. So data is very important for the definition of the supply chain strategy. But also it's very important to streamline operations. And also data is very important for instance to come out with new product and services and also to improve the user or consumer experience. So by collecting data, so what we can do or what we can achieve, so basically is to improve our operations. Because at the end, so the data will become a matrix and based on this matrix, we can improve the operations. So this is one use that we can we can do in our data. So basically to improve our operation, but also data can allow us to improve transparency and VCB. For example, if you relate to the leverage to shipment, so we can track every single moment where my shipment is. So we can use data in order to enhance the visibility that we have on our data. And of course, data is just data and unless we can further analyze it. So and that is where the importance of data analytics come into play. So data analytics or supply chain analytics, so basically will allow us to analyze, to process the data in order to improve this decision-making. So at the end, the main goal of the data analytics and data should be support the decision-making in all the aspects and in all the functions of supply chains. Thank you, Sergio, for bringing that. I love connecting data to visibility because that's, you know, it's very important to us and we always work on this end to end vision. So that's a great insight. And I wanted also to bring it to like down to earth tasks we do when we are working in supply chain. We also have a lot of questions on this from the audience. So it's great that we bring it right here right now. We would love to hear about the importance of a role of data. For example, in topics like network design models, when we are defining optimum location for a new facility or we're trying to think on what's the best physical flow for our products at a certain point in time. We would love to know how may data collection and preparation be different when we're approaching different type of supply chain problems. And if there is any challenge, you foresee that you would like to share with us. Sure. Optimization models in particular, the network design models are also a dating test. So we need to collect or we need to forecast the mandate. For example, we need somehow to estimate distances of transit times in order to fit this model. So we need data collection and data preparation also for these models. So however, the main difference that I identify is basically on the level of intensity that we need in this task. As I mentioned, so machine learning really needs a lot of data. So data collection and data preparation are usually a very resource and time consuming task. And usually in optimization models, at least in those that are strategic decisions. So every time, for example, not going to be designing the network or a facility location problem. So this is not going to be the decision that we're going to be doing on a monthly basis. So usually the time span is going to be evolving at least a few years. So basically that means that the data collection and the data preparation is going to be one single effort and that effort is not going to be repeated in the next five or four years. However, in the case of machine learning, so we might have one time effort to collect the training data. But as I mentioned, we also need to collect live data. So basically, the effort to record that data is going to be on a daily basis. On a daily basis, we should be collecting the new data. We should be cleaning the new data and that data is going to be feeding the model in order to get the predictions that we want. So the main difference I would say is the level of intensity that is to start going to be consuming. Awesome. Thank you. That's a great insight. So I want to pull in a couple of questions here. I'm going to tie in a couple of questions that I see in the Q&A also with one we had prepared on just this process of cleaning data, but then just the concept of cleaning data in the first place. If you think about it, if we had perfect data to begin with, maybe you have your perfect model, but if you had perfect data to begin with, we wouldn't need to clean it in the first place maybe, or maybe we would have to fewer or different types of data transformation or data processing needs. So then maybe the question goes to, and this also goes to ties into a question by Rishi on what is really this maybe some of the sources or what's been your experience on some of the sources, some of those data quality issues upstream? Is it all the way upstream to the point of capture with the sensors or is it the systems or is it the databases that the data is stored in? What are some of the I guess maybe weaknesses within that data pipeline and then how what are some of the processes that you can then utilize to try to account for some of those issues, for some of those quality issues you notice upstream? Got it. Yeah. So two things here. So on one end, so data quality might be an issue and basically data quality is mainly coming from issues that we may have during a data capture. So we may have different levels of security in our measuring systems. That's one source of error, but also so it's related to the precision of the instrument that we are using, but also it's maybe the instruments are not very well calibrated. So there are some measuring issues that we may have precision related or maybe just a calibration, calibration issue, but also keep in mind that there's still an important important size of the data that is being collected manually. So basically companies still using some manual inputs to feed the systems. And of course this manual system or human intensive processes are prone to error. So that's one thing. So issues related to quality, so mainly coming from the data capturing and piece. However, even though we have the perfect data in terms of quality, so we might need also some processing as well in the data. Because the data that we might need for a particular model almost for sure will be coming from different sources. And these different sources might be providing the data in different levels of granularity. So we'll need some data to be aggregated, some data to be aggregated, and we need to put it in the right format as well. So somehow the data preparation is going to be required for our models. Thank you, Sergio. And I want to switch gears to talk about tools specifically, because we have a lot of questions about tools and we were also wondering what's your approach or your recommendation? I would say that it depends on the use case. So for instance, we are supply chain managers. So we mainly care about the business and we might be running some analysis or getting some machine learning models a few times in a year. So maybe three, four times a year. So the specific use case, specific analysis that we need to do. It's not going to be, of course, an in-depth analysis. It's going to be just some exploration and trying to come up with a hypothesis about the business. In that situation, for example, we can use many of the plug-and-play tools that we have out there. So for example, orange. So it's not really, it doesn't require any specific coding skills. So we just plug-and-play, identify or create a few charts in orange, identify what might be the relevant relations. And based on that, we just plug-and-play, we can train a few models. So that will be the best time investment, for example, for this time of users. But of course, if you are a data supply data analyst, I'm going to be running this model more times a year. So somehow you will need to go to these tools that will require some coding skills. And ARRI, for example, is an option here for Python as well. But this is, again, so this is like the intermediate user. I'm going to be running this analysis on a frequently fragmented basis. But at the end, the analysis is going to be used to make recommendations or to inform decisions. But your models are going to be not only implemented in company systems, right? It's only to inform decisions. However, if the use cases might be different, right? So we may have, for example, as an example that I was showing, right? So recommendation services, Spotify for instance, right? To suggest new songs that you might like. In those situations, there is a machine learning model that is in the company systems. So there are some recommendation systems in there. And if that's use case, of course, not going to be using these plug and play tools or going to be using a JAS, a Python or AR. In those situations, you have to use systems that will allow you to scale your operations. Scaling in terms of the data that you're going to be using, but also in the computational power that you will need. In those situations, so the cloud tools are going to be your best options. Awesome. Thank you. Definitely a diversity of tools out there. And it makes sense to contextualize them based on the use case. Makes a lot of sense. So I know I'm keeping an eye on the time here. I know we're going to get close to the time here. So if we could maybe launch our second poll here, we'll be closing things out. And I think we may have time for maybe one more question there, Laura. And I see one here that looks interesting from Alton Edis. And I apologize I'm not pronouncing your name wrong, but Alton Edis, this question I think is an interesting one. How do neural networks, how are neural networks built and what infrastructure is used? Kind of how do you actually build and run a neural network machine learning model? Sounds like a strange thing because you mentioned those as some being some of the more accurate types of models in your presentation. So Yeah. Also, you can also run. So there are specific libraries, Python libraries for instance, that you can use to build a small and also a large neural networks. So the main difference compared to traditional method is of course the accuracy that I mentioned that you can achieve with models. But also you can get rid of part of the data preparation. So basically all related to future engineering. So in order to select what are going to be the ideals, the ideal features to include in your analysis or your model. So all of these tasks can be taken care of the of the neural network.  And then the model itself will identify what are the the most relevant relevant features. Thank you, Sergio. I hope you hear me well now. I want to bring one last question before we go to the poll results. Michael and Lucas are bringing small and medium size businesses. How does it work for this kind of businesses to improve data gathering and preparation and the use of data and the implementation of machine learning, how possible and feasible it is when we're talking about small businesses. Yeah, in for the small businesses, I think it's it's critical first to identify what will be the machine learning and need. So basically, once the use case, what's the problem that they are trying to solve. Once they identify, okay, once the problem they're trying to solve, then the next question will be if some machine learning could be a solution to that, right? So as I was explaining, machine learning offers a way to come up with predictions. So the main usage for machine learning will be to these predictions. So if machine learning is an option, so the next question will be about data collections, data collections, because especially for small businesses, so we cannot just collect as much data as we need because data collection is also coming with the cost. So in that case, we have to focus on the data that we at least we believe is going to be relevant for analysis and just collecting the data that we need is going to be critical for that. And then we can use some tools online for all the data processing also to come up with a model. So they are the typical service where you just pay a variable cost. So by just using or training the model a few times. In that way, you don't have to invest in a factory or additional resources to train your models. Awesome. Thank you, Sergio. It definitely makes sense. The difference between a company, you mentioned Walmart and Amazon as examples where Walmart is processing like a million transactions per hour, which is kind of like mind-boggling scale versus a small business which might have a few customers in our, you know, definitely difference in scale and the different approaches and different volume of data to manage. So maybe if we could share, take a look at that poll really quickly here and then we'll wrap up our live events of the poll. The question was, you know, what is the most interesting part of today's session for you? And it looks like the most common answer was expanding my knowledge of data and analytics. I'm learning about the data preparation process. I'm learning about specific applications of data and ML and spy chain. So that's great. We're glad you found those topics interesting. I know we, there's lots of questions in the Q&A. We always have way more questions that we have time for. But I don't know, Laura or Sergio, you don't know if you have any final comments you'd like to say before we wrap things up here. I would just like to thank Sergio for joining us today and for bringing a lot of great insights to our audience. You have addressed most of the questions we've received before we even got to the Q&A feature part. So that's great. It means like everything we provided was of their interest. So that's amazing. And we would love to see you in the future again. So hopefully we can host you again soon. It's been my pleasure. So it's, as mentioned at the beginning, it's always a pleasure to be in touch with Wichita and also within my McMaster community. Thanks, Kay, for having me. Yeah, thank you, Sergio. And thank you, Laura, for co-hosting. And thank you, everyone, for attending today. Bye. See you in the next one.