WEBVTT 1 00:00:00.160 --> 00:00:04.320 In a world absolutely flooded with data. Mastering complex tech 2 00:00:04.440 --> 00:00:07.759 like deep learning and cloud infrastructure can often feel like 3 00:00:08.919 --> 00:00:11.119 trying to drink from a fire hose totably. But what 4 00:00:11.160 --> 00:00:13.400 if there was a shortcut, you know, a way to 5 00:00:13.480 --> 00:00:16.559 truly understand what matters, cut through the noise and get 6 00:00:16.600 --> 00:00:19.079 straight to the impactful insights. 7 00:00:19.399 --> 00:00:22.480 That's precisely our mission with this deep dive. Yeah, tailor 8 00:00:22.559 --> 00:00:25.280 made for you. Yeah, I mean imagine a startup let's 9 00:00:25.280 --> 00:00:28.640 call them Precision Analytics. Yeah, they want to revolutionize healthcare 10 00:00:28.760 --> 00:00:32.640 predictation outcomes at scale. Okay, big goal, huge goal, and 11 00:00:32.719 --> 00:00:36.960 their challenge moving beyond manually crunching data to building a 12 00:00:37.039 --> 00:00:40.880 really robust automated system, something they could handle like petabytes 13 00:00:40.920 --> 00:00:43.759 of health records and train these cutting edge neural networks. 14 00:00:44.399 --> 00:00:46.719 So today we're can uncack the most important nuggets of 15 00:00:46.759 --> 00:00:49.719 knowledge from our sources. Will reveal how they and how 16 00:00:49.759 --> 00:00:52.520 you can actually conquer this using tools like pie Spark, 17 00:00:52.679 --> 00:00:57.240 pietorch TensorFlow and apatche airflow, all on Amazon Web services. 18 00:00:57.479 --> 00:01:02.200 Absolutely from predicting stock prices to classifying medical conditions. This 19 00:01:02.320 --> 00:01:05.799 deep dive is your personalized guide. We found some really 20 00:01:06.000 --> 00:01:08.920 surprising facts and insights that should give you some serious 21 00:01:08.920 --> 00:01:13.799 aha moments. So let's trace that journey and really unpack 22 00:01:13.920 --> 00:01:14.719 this whole thing. 23 00:01:15.000 --> 00:01:15.519 Let's do it. 24 00:01:15.680 --> 00:01:20.439 So why the cloud, specifically AWS? Why is it the 25 00:01:20.799 --> 00:01:24.959 sort of undisputed champion for these deep learning pipelines. Our 26 00:01:25.000 --> 00:01:29.120 research really hammered home the why traditional on premises infrastructure 27 00:01:29.200 --> 00:01:31.400 it often just hits a wall. I mean, with the 28 00:01:31.439 --> 00:01:34.200 exponential growth of data we're seeing, that makes perfect sense, 29 00:01:34.239 --> 00:01:34.719 doesn't it. 30 00:01:34.719 --> 00:01:35.319 It really does. 31 00:01:35.400 --> 00:01:37.239 Simply can't keep buying hardware fast. 32 00:01:37.079 --> 00:01:41.519 Enough exactly the computational muscle and frankly, this shear scalability 33 00:01:41.560 --> 00:01:44.799 needed for modern deep learning workflows are just immense, and 34 00:01:44.879 --> 00:01:48.280 on premises setup just can't offer that elastic capacity. Like 35 00:01:48.280 --> 00:01:51.000 what happens when you suddenly get ten times the data. 36 00:01:50.799 --> 00:01:51.560 Right, You're stuck. 37 00:01:51.840 --> 00:01:55.319 You're stuck. And this is where cloud based deep learning 38 00:01:55.319 --> 00:02:00.480 steps in. It offers incredible flexibility, true scalability, and often 39 00:02:00.680 --> 00:02:05.519 it's surprisingly cost effective. It fundamentally changes how organizations like 40 00:02:05.560 --> 00:02:09.159 our Precision Analytics example, can rapidly develop and deploy these 41 00:02:09.199 --> 00:02:10.439 advanced mL algorithms. 42 00:02:10.479 --> 00:02:12.680 And when we talk about cloud, the data we pulled 43 00:02:12.680 --> 00:02:16.560 it consistently points to AWS as the clear leader biggest 44 00:02:16.599 --> 00:02:21.639 market share, so their infrastructure becomes this incredibly robust foundation 45 00:02:21.919 --> 00:02:23.159 for these kinds of tasks. 46 00:02:23.280 --> 00:02:28.039 Indeed, AWS provides a really comprehensive suite of services and 47 00:02:28.080 --> 00:02:32.000 they're pretty well tuned for orchestrating these complex, data intensive pipelines. 48 00:02:32.120 --> 00:02:34.319 Okay, all right, we frame the challenge, We get why 49 00:02:34.319 --> 00:02:38.199 the cloud is necessary. Now let's visualize this whole operation. 50 00:02:38.360 --> 00:02:41.000 What does this end to end deep learning workflow on 51 00:02:41.039 --> 00:02:45.120 AWS actually look like, from the initial data intake all 52 00:02:45.199 --> 00:02:47.280 the way to a model actually making predictions. 53 00:02:47.319 --> 00:02:50.479 Okay, think of it like a meticulously engineered assembly line. Yeah, 54 00:02:50.520 --> 00:02:52.759 you know, for your data in your models. It starts 55 00:02:52.759 --> 00:02:56.680 with raw data ingestion and it ends with automated model 56 00:02:56.719 --> 00:03:00.919 runs hopefully in production. Our sources detailed the critical components 57 00:03:00.919 --> 00:03:03.840 that make this journey well not just possible, but actually 58 00:03:03.879 --> 00:03:04.560 pretty efficient. 59 00:03:04.599 --> 00:03:08.639 Okay. First up, the backbone data storage Amazon S three 60 00:03:09.159 --> 00:03:12.719 Simple Storage Service. This is like the central nervous system 61 00:03:12.759 --> 00:03:13.840 for all your data, isn't it. 62 00:03:13.919 --> 00:03:16.360 That's a great analogy. Yeah. S three acts as the 63 00:03:16.439 --> 00:03:21.360 centralized data Like basically, it stores everything your raw data sets, 64 00:03:21.719 --> 00:03:26.039 the carefully preprocessed data, even the final model artifacts, everything everything. 65 00:03:26.120 --> 00:03:30.599 It offers virtually limitless storage capacity and ensures incredibly easy, 66 00:03:30.879 --> 00:03:32.080 highly available data. 67 00:03:31.840 --> 00:03:34.719 Retrieval, which is non negotiable when you're dealing with terabytes 68 00:03:34.800 --> 00:03:38.000 or even petabytes of patient records like precision analytics would. 69 00:03:37.800 --> 00:03:40.879 Be absolutely and Once that massive amount of data is 70 00:03:40.919 --> 00:03:43.639 safely sitting in S three, the next challenge is actually 71 00:03:43.680 --> 00:03:47.639 processing it, transforming it into something usable. That's where pistpark 72 00:03:47.719 --> 00:03:51.520 really shines right absolutely. Pistpark is the engine for large 73 00:03:51.560 --> 00:03:56.599 scale distributed data processing. It's essential for efficient preprocessing and 74 00:03:56.639 --> 00:04:01.479 transformation of those massive data sets. Without its parallel processing power, 75 00:04:01.680 --> 00:04:04.759 preparing data for deep learning would be agonizingly slow, a 76 00:04:04.840 --> 00:04:08.439 huge bottleneck, a major bottleneck, yack, and resource intensive. Too 77 00:04:08.680 --> 00:04:10.240 bad for any high volume operation. 78 00:04:10.400 --> 00:04:13.879 Okay, so data is prepped. Now you need serious computational 79 00:04:13.879 --> 00:04:15.680 horsepower for the actual model training. 80 00:04:16.480 --> 00:04:20.639 Enter amazon EC two correct amazon EC two or elastic 81 00:04:20.639 --> 00:04:24.800 compute cloud. It provides the necessary virtual servers with powerful 82 00:04:24.839 --> 00:04:29.120 CPUs and importantly GPUs for model training. It ensures efficient 83 00:04:29.199 --> 00:04:32.360 utilization of cloud resources. You can quickly spin up or 84 00:04:32.360 --> 00:04:36.959 spin down instances based on your specific training needs. Saves time, saves. 85 00:04:36.600 --> 00:04:40.240 Cost, very elastic, and then the actual brain of the 86 00:04:40.319 --> 00:04:44.480 operation PyTorch and TensorFlow. These are the deep learning frameworks themselves, 87 00:04:44.480 --> 00:04:46.720 the tools you use to actually build, train, and evaluate 88 00:04:46.720 --> 00:04:47.199 your models. 89 00:04:47.279 --> 00:04:50.079 Yes, they are the real powerhouses of the deep learning world. 90 00:04:50.319 --> 00:04:52.839 And finally, to kind of glue it all together, to 91 00:04:53.040 --> 00:04:56.199 automate and streamline the entire process, we have a patchy 92 00:04:56.199 --> 00:05:02.879 airflow or it's fully managed AWS counterpart Amazon. Mwaa ah, okay, 93 00:05:03.040 --> 00:05:06.199 this is your orchestrator. It ensures every step from data 94 00:05:06.240 --> 00:05:10.879 prep all the way to model deployment run seamlessly like clockwork. 95 00:05:11.040 --> 00:05:11.439 Got it? 96 00:05:11.839 --> 00:05:15.199 Okay, So if you're like our hypothetical company, Precision Analytics, 97 00:05:15.240 --> 00:05:17.360 and you want to get your hands dirty, what are 98 00:05:17.399 --> 00:05:20.120 the foundational steps setting up this environment from scratch? 99 00:05:20.399 --> 00:05:23.040 Yeah? The foundation is absolutely critical. First, you need an 100 00:05:23.040 --> 00:05:27.199 AWS account obviously. Then you provision your EC two instances 101 00:05:27.600 --> 00:05:30.439 your virtual servers. Right, you can do that manually or 102 00:05:30.480 --> 00:05:34.000 for more complex, repeatable setups, you'd probably use automation tools 103 00:05:34.000 --> 00:05:36.399 like AWS. CloudFormation makes life easier. 104 00:05:36.480 --> 00:05:38.399 And getting S three ready, what does that involved? 105 00:05:38.399 --> 00:05:41.720 That involves creating your S three buckets, carefully configuring the 106 00:05:41.759 --> 00:05:46.519 appropriate access permissions super important to keep sensitive data secure crucial. Yeah, 107 00:05:46.600 --> 00:05:49.199 and then uploading your initial data sets, but you know, 108 00:05:49.319 --> 00:05:52.480 beyond the raw AWS services. One really crucial insight from 109 00:05:52.480 --> 00:05:55.920 our sources was just the importance of organization. How so 110 00:05:56.240 --> 00:06:01.000 well having a well designed project directory structure with distinct 111 00:06:01.040 --> 00:06:05.639 folders for data logs, output SRC for your code visualizations, 112 00:06:05.680 --> 00:06:10.199 plus those keyfiles like readymmy, dot MD, requirements dot txt 113 00:06:10.600 --> 00:06:12.040 and maybe a config dot YAML. 114 00:06:12.319 --> 00:06:13.199 Oh okay, it. 115 00:06:13.199 --> 00:06:17.759 Sounds basic, but it's paramount for collaboration, for reproducibility, and 116 00:06:17.879 --> 00:06:21.519 just clear documentation. It's off and overlooked, but honestly it's 117 00:06:21.560 --> 00:06:22.800 a huge timesaver down the. 118 00:06:22.720 --> 00:06:25.199 Line, I can see that. And for ensuring everything runs 119 00:06:25.240 --> 00:06:29.759 smoothly without conflicts. Isolation is key with Python virtual environments, right. 120 00:06:29.800 --> 00:06:33.839 Yes, absolutely, creating a Python virtual environment like maybe Miandi, 121 00:06:33.959 --> 00:06:37.079 as we saw in the sources, is paramount. It neatly 122 00:06:37.120 --> 00:06:40.800 manages all your project dependencies, okay, and it ensures reproducibility 123 00:06:40.800 --> 00:06:44.680 across different systems by preventing those pesky conflicts between different 124 00:06:44.720 --> 00:06:47.399 Python versions or library versions. Think of it like a 125 00:06:47.439 --> 00:06:49.920 clean custom sandbox for each project. 126 00:06:50.199 --> 00:06:52.600 Nice, and where does all this coding actually happen? What's 127 00:06:52.639 --> 00:06:53.600 a typical workspace? 128 00:06:54.199 --> 00:06:57.879 Development environments like Jupiter lab are really commonly used for 129 00:06:58.040 --> 00:07:01.759 writing and developing the machine learning models. Within this whole setup, 130 00:07:02.000 --> 00:07:06.040 they provide that interactive, iterative workspace that's so crucial for 131 00:07:06.120 --> 00:07:06.720 data science. 132 00:07:06.920 --> 00:07:10.959 Makes sense. Okay, environments provision organized. Let's talk about the data. 133 00:07:11.000 --> 00:07:13.920 It truly is the foundation. You mentioned pisce Spark as 134 00:07:13.920 --> 00:07:18.079 the powerhouse for data prep. How does it supercharge this process, 135 00:07:18.160 --> 00:07:20.160 especially with massive data sets? 136 00:07:20.240 --> 00:07:23.600 Right? Pisce Park's secret weapon is its parallel processing. It 137 00:07:23.680 --> 00:07:27.279 dramatically enhances it efficiency and speed. Instead of one computer 138 00:07:27.399 --> 00:07:31.560 just slogging through everything sequentially. Yeah, it intelligently breaks down 139 00:07:31.600 --> 00:07:36.040 these large data tasks into independent subtasks that run concurrently 140 00:07:36.199 --> 00:07:39.959 across a whole cluster of machines distributed power exactly. And 141 00:07:40.000 --> 00:07:43.279 we discovered several key optimization techniques in our sources that 142 00:07:43.319 --> 00:07:44.759 can really transform performance. 143 00:07:44.920 --> 00:07:46.800 Oh yeah, like what give us an example. 144 00:07:46.959 --> 00:07:51.120 Okay, take repartitioning. It intelligently redistributes your data across a 145 00:07:51.160 --> 00:07:55.680 specified number of partitions, say ten partitions, to really improve parallelism, 146 00:07:55.879 --> 00:07:58.800 get more work done at once or caching. This keeps 147 00:07:58.879 --> 00:08:03.000 data frames in memory for lightning fast access during repeated operations, 148 00:08:03.319 --> 00:08:06.959 so you avoid costly recomputations. Are And what was fascinating 149 00:08:07.079 --> 00:08:12.199 was how a seemingly minor pist spark optimization like broadcasting. 150 00:08:11.639 --> 00:08:13.240 Ah I remember reading about that. 151 00:08:13.439 --> 00:08:17.160 Yeah, it dramatically reduced processing time for a multi terabyte 152 00:08:17.199 --> 00:08:20.480 data set from hours down to minutes. In a specific 153 00:08:20.560 --> 00:08:23.399 real world case study we found Wow, it's a common 154 00:08:23.439 --> 00:08:27.160 pitfall teams overlook when they're scaling up and also saving 155 00:08:27.240 --> 00:08:30.759 large data sets in par qu format that supports compression 156 00:08:30.800 --> 00:08:34.080 and optimized read operations, another crucial performance game. 157 00:08:34.480 --> 00:08:37.360 So these aren't just minor tweaks, they can have huge. 158 00:08:37.080 --> 00:08:38.840 Impacts, huge impacts exactly. 159 00:08:38.919 --> 00:08:40.879 We saw a real world example of this in the 160 00:08:40.919 --> 00:08:45.000 sources looking at historical Tesla stock prices. How exactly was 161 00:08:45.039 --> 00:08:46.080 pist spark used there? 162 00:08:46.279 --> 00:08:49.159 Right in that Tesla stock example, piscepark was used to 163 00:08:49.200 --> 00:08:52.159 swiftly explore the data set. It efficiently checked for null 164 00:08:52.240 --> 00:08:56.639 values luckily the source showed none, which simplified things very handy, 165 00:08:56.759 --> 00:08:59.600 and then visualizing closing prices over time. It was just 166 00:08:59.639 --> 00:09:03.320 the perfect tool for that initial large scale data exploration. 167 00:09:03.639 --> 00:09:07.120 Okay, and feature engineering that crucial step that can really 168 00:09:08.000 --> 00:09:09.759 elevate a model's predictive power. 169 00:09:09.919 --> 00:09:13.399 Yes, feature engineering is where you get creative, you create new, 170 00:09:13.960 --> 00:09:17.320 hopefully more informative features from your raw data. For the 171 00:09:17.360 --> 00:09:20.799 Tesla stock, this included calculating things like price range so 172 00:09:21.120 --> 00:09:24.320 high minus low, okay, price change close minus open, and 173 00:09:24.399 --> 00:09:28.360 even volume price interaction volume multiplied by clothes, trying to 174 00:09:28.399 --> 00:09:29.960 capture more dynamics. 175 00:09:29.559 --> 00:09:31.200 Right, creating signals exactly. 176 00:09:31.559 --> 00:09:34.320 And then tools like vector assembler and standard scaler and 177 00:09:34.320 --> 00:09:38.240 pie spark prepare these newly engineered features. They transform them 178 00:09:38.279 --> 00:09:40.759 into the right format and scale for the deep learning 179 00:09:40.759 --> 00:09:41.679 models down the line. 180 00:09:41.720 --> 00:09:45.200 Got it now for the brain of the operation, the 181 00:09:45.240 --> 00:09:49.519 deep learning models themselves powered by pietrch and TensorFlow. These 182 00:09:49.519 --> 00:09:52.440 are the two big titans dominating the deep learning landscape 183 00:09:52.480 --> 00:09:53.320 right absolutely. 184 00:09:53.679 --> 00:09:58.039 Both pietorch and TensorFlow are incredibly powerful frameworks. They build 185 00:09:58.080 --> 00:10:02.399 deep learning models capable of tackling really diverse tasks from regression, 186 00:10:02.879 --> 00:10:06.720 like predicting continuous values, say future stock prices like the 187 00:10:06.759 --> 00:10:10.240 Tesla example. It's exactly to classification like predicting the presence 188 00:10:10.240 --> 00:10:12.919 of diabetes, which was the other main example in our sources. 189 00:10:13.039 --> 00:10:16.080 How do these two heavyweights stack up against each other? 190 00:10:16.240 --> 00:10:18.799 The materials provided a pretty clear showdown. 191 00:10:18.919 --> 00:10:22.200 They certainly did. It's interesting PyTorch typically uses what are 192 00:10:22.200 --> 00:10:25.840 called dynamic computational graphs. They're defined during run time. 193 00:10:26.000 --> 00:10:27.600 Okay, what does that mean practically? 194 00:10:28.000 --> 00:10:31.080 Think of it like building legos one piece at a time. 195 00:10:31.559 --> 00:10:34.519 You can easily adjust things and see the immediate impact. 196 00:10:34.919 --> 00:10:39.519 It's incredibly flexible, really ideal for research and rapid prototyping. 197 00:10:39.720 --> 00:10:41.399 More interactive, yeah, more. 198 00:10:41.240 --> 00:10:44.559 Interactive, more pithonics, some would say. Cancer flow, on the 199 00:10:44.559 --> 00:10:49.320 other hand, traditionally use static graphs defined before execution. This 200 00:10:49.399 --> 00:10:52.120 is more like following a detailed blueprint, right, which is 201 00:10:52.200 --> 00:10:56.639 incredibly efficient for optimization and deployment, especially with its seamless 202 00:10:56.679 --> 00:10:57.480 caras integration. 203 00:10:57.720 --> 00:11:00.600 So maybe one for research, one for production. Is that 204 00:11:00.639 --> 00:11:01.200 too simple? 205 00:11:01.320 --> 00:11:04.480 It's a common pattern. Our sources did indicate teams often 206 00:11:04.519 --> 00:11:08.120 gravitate towards PyTorch for that initial experimental phase because it's 207 00:11:08.120 --> 00:11:12.120 so flexible. Then they might potentially transition to TensorFlow for 208 00:11:12.200 --> 00:11:16.679 more robust production scaling. But TensorFlow is becoming more dynamic too, 209 00:11:16.720 --> 00:11:18.080 so the lines are blurring. 210 00:11:17.799 --> 00:11:21.480 A bit interesting. What about the training loops themselves, any 211 00:11:21.480 --> 00:11:23.639 differences there in how you actually train the model? 212 00:11:23.879 --> 00:11:28.320 Yes, PyTorch often requires a bit more manual implementation of 213 00:11:28.360 --> 00:11:30.600 the training loop because you really find grain control, which 214 00:11:30.679 --> 00:11:31.639 research is often like. 215 00:11:31.759 --> 00:11:32.080 Okay. 216 00:11:32.159 --> 00:11:36.120 Tensorflow's care is API, however, provides a higher level model 217 00:11:36.159 --> 00:11:38.759 dot fifth method. It autom makes a lot of that process, 218 00:11:38.840 --> 00:11:41.639 makes it very accessible, maybe easier to get started with 219 00:11:41.679 --> 00:11:41.919 for some. 220 00:11:42.159 --> 00:11:45.360 And how did they actually perform on that Tesla stock 221 00:11:45.399 --> 00:11:47.080 price prediction task? Did one win? 222 00:11:47.559 --> 00:11:51.000 Well? Both models achieved an exceptionally high R squared score 223 00:11:51.320 --> 00:11:55.200 like point nine to nine eight, which indicates excellent predictive accuracy. 224 00:11:55.279 --> 00:11:56.559 Wow. Okay, so both very good. 225 00:11:56.600 --> 00:11:59.559 Both very good. What was particularly interesting, though, was that 226 00:11:59.600 --> 00:12:02.440 the ten flow model had a slightly lower test loss 227 00:12:02.480 --> 00:12:05.519 twelve point one one compared to Pytorch's twenty point five 228 00:12:05.559 --> 00:12:08.320 to four. Now, this difference might seem small, but in 229 00:12:08.360 --> 00:12:12.039 a financial context like stock predition, even marginal improvements and 230 00:12:12.159 --> 00:12:16.960 loss can translate to significant real world financial impact and 231 00:12:17.000 --> 00:12:19.559 potentially better generalization to unseen data. 232 00:12:19.639 --> 00:12:22.879 Good point. And for the diabetes classification example. 233 00:12:23.039 --> 00:12:26.759 For diabetes, both models showed pretty comparable accuracy, tensorflows at 234 00:12:26.799 --> 00:12:30.320 point seven six ninety two PyTorch at point seven six 235 00:12:30.440 --> 00:12:34.879 zero seven very close. A key insight from analyzing that 236 00:12:35.000 --> 00:12:37.799 data was that the glucose level had the strongest correlation 237 00:12:37.879 --> 00:12:40.720 with the outcome the diagnosis about point four to eighty 238 00:12:40.720 --> 00:12:43.759 eight interesting, but the source is also importantly noted the 239 00:12:43.799 --> 00:12:48.639 presence of skewed data in several features things like pregnancies, BMI, diabetes, 240 00:12:48.679 --> 00:12:49.679 pedigree function and. 241 00:12:49.759 --> 00:12:51.000 H Why does that matter? 242 00:12:51.279 --> 00:12:53.519 Well, skew data isn't just a technical detail. It can 243 00:12:53.559 --> 00:12:57.600 profoundly impact model bias and learning. It really emphasizes why 244 00:12:57.639 --> 00:13:01.080 appropriate metrics like precision rec call and the F one 245 00:13:01.080 --> 00:13:05.480 score are absolutely crucial for evaluating performance on imbalanced classification 246 00:13:05.559 --> 00:13:08.679 tasks like this, where just looking at overall accuracy can 247 00:13:08.759 --> 00:13:10.240 be really misleading. 248 00:13:09.879 --> 00:13:12.720 Right, you might mispredicting the rarer cases. So once you've 249 00:13:12.720 --> 00:13:15.320 got your basic model built, how do you really boost 250 00:13:15.320 --> 00:13:20.360 its performance tackle those common challenges like overfitting or underfitting? 251 00:13:20.480 --> 00:13:23.399 Ah? Yeah, that's where the advanced techniques come in. Yeah, 252 00:13:23.440 --> 00:13:26.360 and our sources gave us some fascinating practical insights here. 253 00:13:26.600 --> 00:13:30.919 Overfitting and underfitting are like ubiquitous challenges in deep learning, 254 00:13:31.080 --> 00:13:34.840 always fighting them always. For instance, early stopping it doesn't 255 00:13:34.879 --> 00:13:39.360 just prevent overfitting by halting training. When your validation performance 256 00:13:39.440 --> 00:13:44.120 maybe the loss stops improving. For the Tesla stock example, 257 00:13:44.480 --> 00:13:50.200 it explicitly demonstrated significant cost savings by preventing unnecessary compute cycles. 258 00:13:50.879 --> 00:13:53.759 Training stopped at at bock eighty seven. But crucially, it 259 00:13:53.799 --> 00:13:56.240 restored the weights from the best epoch, which was actually 260 00:13:56.240 --> 00:13:58.480 ep box seventy seven. So you get the best model and. 261 00:13:58.440 --> 00:14:01.320 Safe compute smart drop out I hear that's a powerful 262 00:14:01.320 --> 00:14:01.639 one too. 263 00:14:01.759 --> 00:14:05.399 It is dropout randomly drops out a certain percentage of neurons, 264 00:14:05.440 --> 00:14:09.399 maybe fifty percent during each training stat turns them off temporarily. Yeah. 265 00:14:09.679 --> 00:14:13.720 This prevents complex coadaptations between neurons, sort of forces the 266 00:14:13.759 --> 00:14:17.799 network to learn more robust features. It significantly improves the 267 00:14:17.840 --> 00:14:22.279 model's ability to generalize to new unseen data. Our source 268 00:14:22.279 --> 00:14:24.080 has kind of likened it to the model learning from 269 00:14:24.120 --> 00:14:26.159 multiple perspectives to become more robust. 270 00:14:26.320 --> 00:14:30.320 Interesting analogy. There's also L one and L two regularization, 271 00:14:30.919 --> 00:14:33.120 which sounds a bit like putting your model on a diet. 272 00:14:33.320 --> 00:14:34.919 That's a great way to put it. Yeah, think of 273 00:14:35.039 --> 00:14:38.039 L one regularization as a strict diet for your model's weights. 274 00:14:38.600 --> 00:14:41.240 It actually forces some weights to go completely to zero, 275 00:14:41.399 --> 00:14:44.639 oh okay, which makes the model simpler promote sparsity, meaning 276 00:14:44.679 --> 00:14:48.240 it uses fewer features. L two regularization is more like 277 00:14:48.240 --> 00:14:51.080 a gentle nudge. It makes all ways smaller but keeps 278 00:14:51.120 --> 00:14:54.360 them present. It helps prevent any one feature from dominating 279 00:14:54.360 --> 00:14:58.679 the prediction. They're both powerful tools for raining in that overfitting. 280 00:14:58.279 --> 00:15:02.000 Got it and adjusting the arning rate that seems fundamental but. 281 00:15:02.039 --> 00:15:05.919 Tricky, oh absolutely critical. Learning rate tuning, basically adjusting the 282 00:15:05.960 --> 00:15:09.960 step size for optimization, can profoundly impact how fast and 283 00:15:10.000 --> 00:15:14.440 effectively your model converges and performs. Our sources showed clear 284 00:15:14.480 --> 00:15:18.679 examples where different learning rates like point zero one versus 285 00:15:18.679 --> 00:15:21.919 point zero zero zero one led to widely varied test 286 00:15:21.960 --> 00:15:25.960 loss and R squared scores. It really underscores the importance 287 00:15:25.960 --> 00:15:30.200 of finding that Goldilocks zone, not too fast, not too slow, right. 288 00:15:30.600 --> 00:15:33.240 What about the actual structure of the model itself, like 289 00:15:33.440 --> 00:15:36.000 the number of layers, the number of neurons in each layer. 290 00:15:36.159 --> 00:15:39.679 That's model capacity And a kind of counterintuitive finding from 291 00:15:39.679 --> 00:15:42.919 our sources was that sometimes deeper models meaning more layers 292 00:15:42.919 --> 00:15:46.600 but maybe fewer neurons per layer, can outperform wider models 293 00:15:46.639 --> 00:15:49.360 which have fewer layers but more neurons. Yeah. For the 294 00:15:49.399 --> 00:15:52.360 Tesla stock example, a deeper model with five hidden layers 295 00:15:52.600 --> 00:15:55.879 actually achieved lower test loss and higher are squared compared 296 00:15:55.879 --> 00:15:57.759 to a wider one that only had two hidden layers. 297 00:15:58.159 --> 00:16:00.759 It suggests that for some problems depth that really matters 298 00:16:00.759 --> 00:16:04.440 more than just width. Adding layers can capture more complex patterns. 299 00:16:04.480 --> 00:16:07.279 Fascinating. All this tuning, though it can feel like searching 300 00:16:07.279 --> 00:16:08.320 for a needle in a haystack. 301 00:16:08.399 --> 00:16:10.320 Sometimes it definitely can. 302 00:16:10.480 --> 00:16:14.200 That's where hyper parameter optimization tools like care Stooner that 303 00:16:14.279 --> 00:16:15.639 was mentioned come into play. 304 00:16:15.679 --> 00:16:19.080 I guess precisely, tools like care Student automate that search 305 00:16:19.120 --> 00:16:22.759 for optimal hyper parameter combinations things like the number of 306 00:16:22.840 --> 00:16:26.919 units in a layer, the learning rate itself dropout rates. 307 00:16:26.720 --> 00:16:28.200 Takes the guesswork out well. 308 00:16:28.360 --> 00:16:31.960 It makes a search systematic, It can yield significantly better 309 00:16:32.000 --> 00:16:36.000 performance than just manual tuning alone, and potentially fave countless 310 00:16:36.039 --> 00:16:37.240 hours of trial and error. 311 00:16:37.440 --> 00:16:41.519 Makes sense. And finally, K fold cross validation Why is 312 00:16:41.519 --> 00:16:42.159 that important? 313 00:16:42.480 --> 00:16:46.720 This technique is essential for getting truly reliable model performance estimates, 314 00:16:47.559 --> 00:16:49.240 especially when you have smaller data. 315 00:16:49.039 --> 00:16:50.639 Sets like the diabetes one. 316 00:16:50.679 --> 00:16:54.120 Maybe exactly. It involves splitting your data into k folds, 317 00:16:54.360 --> 00:16:57.200 say five folds. Then you train and test the model 318 00:16:57.279 --> 00:17:00.240 k times, using a different fold for testing each time, 319 00:17:00.519 --> 00:17:03.000 and training on the rest, then you average the results 320 00:17:03.000 --> 00:17:06.000 across all the folds. For the diabetes classification, we saw 321 00:17:06.000 --> 00:17:09.200 on average accuracy of around zero point seventy five sixty 322 00:17:09.279 --> 00:17:12.279 nine across five folds. That gives you a far more 323 00:17:12.400 --> 00:17:15.960 robust and trustworthy performance estimate than just a single train 324 00:17:16.079 --> 00:17:18.400 test split, which could be lucky or unlucky. 325 00:17:18.559 --> 00:17:22.000 Right reduces the chance factor. Okay, wow, it sounds incredibly 326 00:17:22.000 --> 00:17:24.720 complex to manage all of this manually, especially for a 327 00:17:24.720 --> 00:17:27.640 company like our Precision Analytics trying to scale up really is. 328 00:17:28.000 --> 00:17:31.640 So what's the grand orchestrator? What brings this entire pipeline 329 00:17:31.680 --> 00:17:34.759 together from the data ingestion right through to deploying and 330 00:17:34.799 --> 00:17:38.400 running the model? You mentioned apatche, Airflow and Amazon MWAA. 331 00:17:38.680 --> 00:17:42.079 Yeah, you've highlighted the crucial next step manually running complex 332 00:17:42.160 --> 00:17:46.039 deep learning workflows. Maybe just executing a Python script a 333 00:17:46.119 --> 00:17:51.240 main function. It utterly lacks automation, it lacks robust monitoring, 334 00:17:51.519 --> 00:17:54.400 and it lacks the reproducibility you absolutely need for any 335 00:17:54.440 --> 00:17:58.119 real world application. It's simply not a scalable or reliable solution. 336 00:17:58.480 --> 00:18:01.119 So air flu rides in to save the How does 337 00:18:01.160 --> 00:18:04.000 it tackle these automation and monitoring challenges? 338 00:18:04.240 --> 00:18:08.759 Well apatche airflow facilitates automated execution. You define your workflow 339 00:18:08.960 --> 00:18:11.519 and it runs based on pre defined schedules or triggers. 340 00:18:11.960 --> 00:18:16.319 It virtually eliminates that need for manual intervention. Nice and critically, 341 00:18:16.640 --> 00:18:21.079 it offers comprehensive monitoring and logging capabilities. These are absolutely 342 00:18:21.160 --> 00:18:23.960 vital for tracking the health and progress of your complex 343 00:18:24.200 --> 00:18:28.240 deep learning pipelines. It ensures every step runs predictably and 344 00:18:28.279 --> 00:18:30.839 if something fails, you know exactly where and why. 345 00:18:31.039 --> 00:18:33.319 And I've heard the term DAGs a lot when people 346 00:18:33.359 --> 00:18:35.480 talk about airflow. What exactly are those? Right? 347 00:18:35.559 --> 00:18:38.039 DAGs? They stand for directed acyclic graphs. 348 00:18:38.119 --> 00:18:38.480 Okay. 349 00:18:38.759 --> 00:18:42.200 In airflow, your workflows are visually defined as these DAGs. 350 00:18:42.720 --> 00:18:45.880 They're composed of individual tasks. Think of them as building 351 00:18:45.880 --> 00:18:49.519 blocks like run, pist park, job, train model, evaluate model, 352 00:18:49.960 --> 00:18:52.799 and you define the dependencies between them. This task runs 353 00:18:52.799 --> 00:18:55.599 only after that one succeeds like a flow chart, exactly 354 00:18:55.680 --> 00:18:58.480 like a flow chart, but one that enforces dependencies and 355 00:18:58.519 --> 00:19:01.240 doesn't loop back on itself. That it's the acyclic part. 356 00:19:01.440 --> 00:19:05.680 This modular design greatly enhances reusability and scalability for your workflows, 357 00:19:06.039 --> 00:19:08.720 makes them much easier to visualize, manage and debug. 358 00:19:08.920 --> 00:19:13.720 Okay, And for AWS users there's Amazon MWAA. What's the 359 00:19:13.759 --> 00:19:16.640 big advantage there over just running Airflow yourself. 360 00:19:16.759 --> 00:19:21.599 Huh? Amazon MWAA managed workflows for Apache Airflow. It's a 361 00:19:21.599 --> 00:19:23.599 bit of a game changer because it's a fully managed 362 00:19:23.599 --> 00:19:28.400 service from AWS, meaning it radically simplifies setting up, managing, 363 00:19:28.440 --> 00:19:31.799 and scaling Apache Airflow environments. It basically slashes all the 364 00:19:31.839 --> 00:19:35.480 manual insallation, configuration, patching, and maintenance overhead you'd face if 365 00:19:35.480 --> 00:19:38.319 you try to run airflow yourself on EC two instances 366 00:19:38.640 --> 00:19:39.839 or using Donker. 367 00:19:39.599 --> 00:19:42.440 So AWS handles the infrastructure part exactly. 368 00:19:42.480 --> 00:19:44.920 It's like having a dedicated team of experts managing your 369 00:19:44.920 --> 00:19:48.240 airflow infrastructure for you, letting you focus just on building 370 00:19:48.240 --> 00:19:49.319 your workflows your DAGs. 371 00:19:49.880 --> 00:19:52.920 That sounds pretty appealing. How does that deployment process actually 372 00:19:53.039 --> 00:19:55.480 work with MWAA as it simpler. 373 00:19:55.160 --> 00:19:58.440 It's remarkably streamlined. Yeah. First, you set up the NWA 374 00:19:58.599 --> 00:20:02.519 environment itself in the AWA console. That involves configuring things 375 00:20:02.559 --> 00:20:05.240 like an S three bucket where your DAG files will live, 376 00:20:05.640 --> 00:20:09.680 setting up the networking ensuring proper security roles. Then you 377 00:20:09.680 --> 00:20:12.440 simply upload your DAG files. Often you'll zip them up 378 00:20:12.640 --> 00:20:15.920 with any custom Python dependencies they need into that designated 379 00:20:16.000 --> 00:20:19.640 S three bucket, configure any environment variables your DAGs need, 380 00:20:20.000 --> 00:20:22.960 and then you can trigger the DAG execution either manually 381 00:20:23.000 --> 00:20:26.880 through the airflow UI that MWAA provides, or set up 382 00:20:26.880 --> 00:20:27.799 a preset schedule. 383 00:20:27.960 --> 00:20:31.079 Seems much less hassle. Okay, So once everything's deployed and running, 384 00:20:31.079 --> 00:20:35.119 maybe on a schedule, continuous monitoring is critical. Why is 385 00:20:35.160 --> 00:20:38.720 that so important? Specifically for deep learning models after they're deployed. 386 00:20:38.920 --> 00:20:42.440 Yeah, continuous monitoring post deportment is absolutely crucial. You need 387 00:20:42.480 --> 00:20:46.680 to detect issues like model drift that's where the statistical 388 00:20:46.680 --> 00:20:49.799 properties of the input data change over time compared to 389 00:20:49.839 --> 00:20:53.160 the training data, so the world changes exactly. Or concept drift, 390 00:20:53.200 --> 00:20:56.160 which is even trickier. That's where the relationship between the 391 00:20:56.160 --> 00:20:59.480 input features and the target variable actually shifts. The underlying 392 00:20:59.559 --> 00:21:03.720 patterns learned might no longer hold true. Yeah. Monitoring also 393 00:21:03.839 --> 00:21:07.000 helps you spot critical resource bottlenecks like is your prediction 394 00:21:07.119 --> 00:21:10.920 service running out of CPU, GPU or memory, and track 395 00:21:11.000 --> 00:21:14.920 latency problems that could impact real time applications, especially for 396 00:21:15.000 --> 00:21:18.920 something like patient diagnosis where speed in accuracy or paramount 397 00:21:19.279 --> 00:21:22.599 you can't have your model suddenly getting slow or inaccurate. 398 00:21:22.720 --> 00:21:25.720 Definitely not. What tools do you use for that kind 399 00:21:25.720 --> 00:21:26.440 of monitoring? 400 00:21:26.880 --> 00:21:31.200 Well, the MWAA console itself and the standard apatche Airflow 401 00:21:31.319 --> 00:21:34.279