BYTE the Cloud

Chris 0:00
All right, let's jump right into it. Today, we are taking a deep dive into Amazon Forecast, and this is really tailored for you mid level cloud engineers out there. Think of this as your secret weapon for acing those AWS exams and navigating all those real world cloud challenges that you might be facing out there

Kelly 0:20
absolutely. Yeah, we're going way beyond just the basics here today. Think of it as your cheat sheet for not just understanding a forecast, but like, really mastering it.

Chris 0:29
I like that a cheat sheet. So let's, let's start with the why. Why should a cloud engineer, like, even care about a forecasting service in the first place? Well,

Kelly 0:37
you know, imagine this. You can actually predict the future usage of all your cloud resources. So that means you can do things like optimize all those EC2 instances you're running. You can right size all of the storage you're using and basically become like a cost saving superhero. No more overspending or like scrambling to scale up when you have those like unexpected demand spikes.

Chris 0:56
Okay, so less firefighting, more proactive planning. I like that a lot. But how does Amazon Forecast actually pull this off? Is this some kind of magical algorithm? It's not

Kelly 1:04
magic, but it's pretty darn close. So Amazon Forecast actually leverages the power of machine learning to analyze all of your historical data. So think about things like your server logs, your sales figures, website traffic, anything that shows patterns over time, and then it uses all of this data to build a forecasting model that is specific to your needs. So

Chris 1:26
it's like learning from the past to predict the future. That's pretty cool, but it's not just number crunching, right? There's like, other factors that play in here too, like seasonality, for example, right?

Kelly 1:35
Yeah, absolutely. Amazon Forecast takes into account all of those recurring patterns that you might see pop up in your data. So think about those predictable spikes in your website traffic during like, certain times the day, or maybe an increase in sales during the holiday seasons. It even considers things like external factors like weather, economic indicators and even social media trends, if you feed it the right data, wow.

Chris 1:58
So it's really about like understanding the bigger picture, yeah, and all of the elements that can influence those future outcomes. This is way more sophisticated than I initially thought. But before we get too lost in the details here, let's like, zoom out for a sec. Where does Amazon Forecast fit into this whole AWS ecosystem? So

Kelly 2:17
think of Amazon Forecasts like your strategic advisor for your cloud infrastructure, it integrates seamlessly with services like S3 for storing all of that historical data you have. CloudWatch for monitoring your resources, and even Lambda for things like custom data processing or automating actions based on those forecasts. Okay, so

Chris 2:35
it's not a standalone service. It's actually designed to work in tandem with like the rest of the AWS toolkit. Yeah, that makes a lot of sense, but I'm sure there are some limitations that we should be aware of. Right? No system is perfect. Yeah,

Kelly 2:47
you're right about that. No system is perfect. The accuracy of your forecasts depends heavily on the quality of your historical data. If you feed it inaccurate or incomplete data, well, your predictions are going to be pretty unreliable, garbage out, as they say. So

Chris 3:01
data hygiene is crucial here. We need to make sure that our data is accurate and relevant if we want Amazon Forecast to really work its magic. And what about the types of predictions that we can make? Are we limited to like, specific use cases here?

Kelly 3:16
Not at all. That's the beauty of Amazon Forecasts. It's super versatile. You can use it to predict things like website traffic, product demand, energy consumption, server loads, really, any time series data you can throw at it. We'll we'll dig into some of those real world examples a little bit later. But really, the possibilities are vast. Okay,

Chris 3:34
so the possibilities are vast. This is all incredibly valuable information, but I know we're here for more than just a basic overview, let's get into like, those nitty gritty details that will really help our listeners ace those AWS exams. Are you ready to tackle some of those exam level questions? Absolutely,

Kelly 3:50
bring them on. All right, perfect.

Chris 3:52
So let's start with a common question that often trips people up. What are the different types of forecasting models that are available in Amazon Forecast?

Kelly 3:58
All right, so here's where things start to get interesting. Amazon Forecast offers a range of both built in algorithms and the option to create custom models. Built in algorithms are kind of like those pre packaged solutions. They're ready to go for like common forecasting needs. They're great for getting started quickly, and they often work pretty well for those less complex scenarios. So

Chris 4:19
it's like choosing a preset filter for a photo instead of manually adjusting all of the settings Exactly.

Kelly 4:23
But if you need more control, or have those like unique forecasting challenges, you can dive into the world of custom models. These give you the flexibility to tailor the algorithm to your specific data and requirements. It's like having a dark room where you can fine tune every aspect of the image. So built

Chris 4:42
in is great for speed and simplicity, while custom offers more control and customization. But how do you know which approach is best for a given situation? So that's

Kelly 4:51
where your understanding of the data and the forecasting problem comes in. If you're dealing with relatively straightforward time series data and a common prediction. Task, yeah, a built in algorithm might be perfect.

Chris 5:03
Okay. So if I wanted to predict website traffic based on historical patterns, a built in algorithm could handle that most

Kelly 5:10
likely, yeah. But if you're trying to forecast something way more complex, like product demand that's influenced by external factors like social media trends or maybe even competitor pricing, you might need a custom model to really capture all of those nuances. That

Chris 5:25
makes perfect sense. So you're not just picking a model at random, you're strategically choosing the best tool for the job, all right, but let's say you've built your model, whether it's built in or custom, how do you even know if it's any good? How do you evaluate its accuracy?

Kelly 5:40
That's a crucial question. Amazon Forecast provides a whole set of metrics to help you assess the accuracy of all of your predictions. You'll often encounter terms like RMSE, M, A, P, E and W, P. Wait

Chris 5:52
a minute, those acronyms sound familiar? Didn't I encounter these in my statistics class back in college? Yeah,

Kelly 5:56
they're very common statistical terms, but in the context of forecasting, they represent Root Mean Squared Error, mean absolute percentage error and weighted absolute percentage error respectively.

Chris 6:07
Okay, that's a mouthful. Can you break those down in a way that's relevant for like a cloud engineer? Of

Kelly 6:12
course, essentially, these metrics measure the difference between your predicted values and the actual values, so they help you understand how far off your predictions are from reality. So

Chris 6:24
a lower RMSE or M, A, P, E would indicate a more accurate model. Exactly.

Kelly 6:28
A lower score generally means that your predictions are closer to that actual data. But it's not enough to just know what the acronyms stand for. You need to understand what each metric emphasizes and how to interpret them in the context of your specific forecasting problem.

Chris 6:43
So we're not just like memorizing definitions here. We're actually learning how to use these metrics to judge the performance of our model precisely.

Kelly 6:50
And examiners might even ask you to explain like the differences between those metrics and when you might choose one over the other. For example, our MSE is very sensitive to outliers, while M, A, P, E gives you a percentage error, which is often much easier to interpret in like business terms.

Chris 7:07
Okay, so we're not just spitting out definitions, we're demonstrating a deeper understanding of how to actually assess the quality of our forecasts. I'm starting to feel like a forecasting expert already. You're

Kelly 7:18
well on your way. And remember, it's not just about acing those exams. It's about applying this knowledge in real world scenarios to make better decisions about you cloud infrastructure, absolutely.

Chris 7:28
So let's let's shift gears here and explore how Amazon Forecasts can be used to tackle some of those real world challenges that cloud engineers face every day. One of

Kelly 7:40
the most common challenges that cloud engineers face is like managing those EC2 instances. How do you make sure you have enough capacity to handle demand without overspending on resources that you're not even using? That's really where Amazon Forecast comes in. So we're talking

Chris 7:54
about cost optimization, which is always a hot topic in the cloud world. Can you give us, like, a concrete example of how forecasts can actually help? Yeah,

Kelly 8:01
absolutely. Let's say you're running like a web application on EC2. You can actually feed Amazon Forecast all of your historical data on your web traffic patterns, so think like hourly or daily data for the past few months or even years. Forecasts will then analyze all of those patterns, taking into account things like seasonality and trends, and then generate a prediction of your future web traffic.

Chris 8:23
So we're essentially getting a glimpse into the future of our website traffic. That's pretty amazing. But how do we translate those predictions into like concrete actions?

Kelly 8:32
So that's where the integration with other AWS services comes in. You can actually use forecast predictions to automatically scale your EC2 instances up or down based on like that anticipated demand. So if forecast predicts a serve in traffic, you can configure your auto scaling policies to launch additional EC2 instances beforehand, and that ensures a smooth user experience even during those like peak periods. So

Chris 8:55
no more scrambling to provision servers when traffic spikes unexpectedly. Forecast gives us the foresight to be proactive. Yeah, I like that. But what about when traffic is low?

Kelly 9:03
Well, that's where the cost savings really kick in. If forecast predicts like a lull in traffic, you can automatically scale down your EC2 instances, which reduces your overall cost. You're essentially paying for what you need when you need it.

Chris 9:18
Okay, that's a game changer for managing cloud costs. But I'm curious, can forecast also help us take advantage of cost saving options like Spot Instances.

Kelly 9:26
Absolutely you can actually combine forecasts predictions with spot instance strategies to further optimize your spending. So for example, if forecast predicts like a temporary spike in demand, you could use Spot Instances to handle that surge without committing to those long term on demand instances. So we're being

Chris 9:43
strategic with our resources, using forecasts as a guide to make smart decisions about when to use on demand versus Spot Instances. Start to see how powerful this can be, and

Kelly 9:52
we're really just scratching the surface here. Amazon Forecasts can also be used to optimize other aspects of your cloud infrastructure, but. Storage, databases and even network bandwidth. By predicting future demand for all of these resources, you can proactively scale up or down, ensuring optimal performance and cost efficiency. Okay,

Chris 10:12
this is all incredibly insightful, but I know our listeners are eager to test their knowledge and see how these concepts might show up in those AWS exams. What's a good exam level question that really digs into the practical application of Amazon Forecast?

Kelly 10:26
So a classic question is, you're responsible for managing a global e commerce platform that experiences significant seasonal fluctuations in demand? How would you use Amazon Forecast to ensure like, optimal performance and cost efficiency?

Chris 10:39
Okay, that's a real world scenario that many cloud engineers can relate to. How will we tackle this challenge?

Kelly 10:44
First, you'd want to gather all of your historical data on sales, web traffic and any other relevant metrics for each region, and then make sure that that data is accurate, complete and formatted correctly for forecast. Then you'd create a data set in Amazon Forecast specifying the relevant information about your data, like the frequency, the time range and the data schema.

Chris 11:06
So we're essentially setting the stage for forecast, telling it what kind of data we're working with and what we want to predict

Kelly 11:12
exactly next. You'd choose a suitable forecasting model. So you could start with one of forecasts built in algorithms that are designed for like retail or web traffic forecasting, or if you need like more customization, you could explore building a custom model that takes into account those specific factors relevant to your business, like promotions, marketing campaigns or even competitor activity. So we're

Chris 11:35
selecting the right tool for the job, considering both the complexity of our data and the specific predictions that we need precisely.

Kelly 11:42
Once you've trained your model and are satisfied with its accuracy, you can actually generate forecasts for future demand in each region, and these forecasts will give you insights into when you can expect those peaks and valleys and demand. And armed

Chris 11:55
with these predictions, we can then take action to optimize our resources exactly.

Kelly 12:00
You can actually configure auto scaling policies to adjust your EC2 instances, your database capacity and other resources based on the predicted demand, and this ensures that you have enough capacity to handle those seasonal peats without overspending during those lulls.

Chris 12:16
So we're proactively scaling up or down following the rhythm of our business cycles as predicted by forecast. But it's not just about auto scaling, right, right?

Kelly 12:26
You can also use the forecast to optimize other aspects of your operations, like inventory management. So if forecast predicts a surge in demand for certain products in a particular region, you can adjust your stock levels accordingly, ensuring that you have enough inventory to meet customer needs. So we're

Chris 12:41
moving from reactive to proactive, using data driven insights to anticipate future needs and make informed decisions. It's like having a crystal ball for our E commerce platform guiding us towaRDS optimal performance and profitability. Yeah, that's

Kelly 12:56
a great way to put it, and remember, examiners might ask you to go into even more detail about specific aspects of the process, like how you'd handle data, pre processing, yeah, choose the right evaluation metrics, or integrate forecasts with other AWS services to automate actions based on those predictions.

Chris 13:11
So we need to be prepared to explain not just the what, but also the how of using Amazon Forecast in a real world setting. This is all incredibly valuable, but I know there's more to explore. What other exam level questions should our listeners be prepared for? All right, let's continue our deep dive into Amazon Forecast and explore some more of those exam level questions that can really trip people up. Yeah, sounds good. Let's do okay. Here's one that often causes confusion. What is the role of IAM in Amazon Forecast?

Kelly 13:39
IAM the gatekeeper of AWS. It might seem like security and permissions are kind of unrelated to a forecasting service, but they're actually super important. You'll need to know how to use IAM to control access to all of your valuable forecasting data and resources.

Chris 13:55
So it's like having security guaRDS for our prediction engine making sure that only authorized users can actually access it. Give us like a concrete example,

Kelly 14:04
sure. Imagine you have like a team of data scientists who need to create and manage forecast data sets and predictors. You can use IAM policies to give them those necessary permissions while restricting access for other users who don't need those privileges. You can even get granular and control which specific actions they can perform, like creating forecasts, querying data or deleting resources. So it's all

Chris 14:26
about like that fine grain control, making sure that each user or role has the appropriate level of access. But it's not just about individual users, right? What about applications or services that need to interact with forecasts?

Kelly 14:37
Yeah, that's a great point. You can actually use IAM roles to grant permissions to applications or services that need to interact with your forecast resources. For example, you could have a Lambda function that automatically retrieves forecasts and triggers actions based on those predictions. By assigning like an IAM role to that Lambda function, you can make sure it has the necessary permissions to interact with your forecast. Predictors without exposing your AWS credentials. So we're

Chris 15:03
talking about secure delegation, granting specific permissions to applications or services without compromising our overall security posture. This is making a lot more sense now,

Kelly 15:13
glad to hear it. And examiners might even ask you about specific IAM best practices for sore cast for instance, they could ask, how do you ensure that only authorized users or services can access sensitive forecasting data? Okay, so

Chris 15:27
what would be a good response to that? Well, you'd

Kelly 15:28
want to emphasize the principle of least privilege, granting only the necessary permissions to each user or role. You could talk about using IAM policies to control access to specific forecast resources, like data sets, predictors and forecasts, and you could also discuss using encryption to protect your data, both in transit and at rest, adding like an extra layer of security.

Chris 15:51
So it's a multi faceted approach, combining IAM policies, encryption and best practices to create a robust security framework for Amazon Forecast. Exactly. Okay. Now let's shift gears and tackle a question that tests your knowledge of data management. How would you handle missing data points in your time series data when using Amazon Forecast? Oh,

Kelly 16:11
missing data the bane of every data scientist existence. I'm sure this is a common challenge in the real

Chris 16:16
world. Oh, it definitely is real world. Data is rarely perfect, and examiners want to know that you can handle those imperfections gracefully. They might ask you something like you're preparing a data set for Amazon Forecast, but you notice that there are gaps in your historical data. What steps would you take to address this issue?

Kelly 16:31
All right, so you have a few approaches. One option is to just remove the data points with missing values. However, this can lead to a loss of information, especially if those missing data points represent significant events or trends, a better approach is often to use imputation techniques to fill in those gaps. Imputation

Chris 16:50
that sounds familiar. Can you refresh our memory on what that means? Sure?

Kelly 16:53
Imputation basically means replacing missing values with estimated values based on the available data. There are various imputation techniques, each with its pros and cons. You could use simple methods like replacing missing values with the mean or median of the available data.

Chris 17:09
So for missing sales data for a particular day, we could just use the average sales for that day of the week from previous weEKS

Kelly 17:16
Exactly. Or you could use more sophisticated techniques like linear interpolation or even machine learning models to estimate the missing values. The choice of imputation technique really depends on the nature of your data, the extent of the missing values and the desired level of accuracy. So we're

Chris 17:33
not just ignoring the problem. We're actively addressing it using techniques that maintain the integrity of our data and improve the accuracy of our forecasts, but wouldn't filling in missing data potentially introduce bias or like skew our predictions. That's

Kelly 17:50
a valid concern, and it's something you should always consider when using imputation techniques. The key is to choose an imputation method that's appropriate for your data and to carefully evaluate the impact of imputation on your overall forecasting accuracy. You might even need to experiment with different imputation techniques and compare their performance using evaluation metrics like RMSE and M, A, P, E. So

Chris 18:11
it's about being mindful of the potential pitfalls and taking steps to mitigate them. I'm learning so much from this deep dive. Is there anything else examiners might throw our way when it comes to data preparation.

Kelly 18:22
Well, they could ask about handling outliers, dealing with different data frequencies, or transforming your data to improve model performance. The key is to demonstrate that you really understand the importance of data quality, and that you have a toolkit of techniques to address those common data challenges.

Chris 18:39
So it sounds like mastering data preparation is just as crucial as understanding those forecasting models themselves. Absolutely

Kelly 18:44
remember, garbage in, garbage out, the quality of your data directly impacts the accuracy and reliability of your forecasts.

Chris 18:52
This has been an incredibly insightful deep dive into the world of Amazon Forecast. We've covered everything from like the basic concepts to those nuanced details that will really help our listeners ace those AWS exams and tackle real world cloud challenges. Do you have any final words of wisdom for our cloud engineer audience out there? Yeah, my

Kelly 19:11
advice is to really embrace the power of forecasting. It's no longer a niche skill. It's becoming essential for cloud engineers who want to optimize resources, manage costs and make informed decisions about their infrastructure. Amazon Forecast provides a really robust and accessible platform to unlock the potential of data driven predictions. So explore the service, experiment with different models and start forecasting your way to cloud success. Well said,

Speaker 1 19:35
and remember, this is just the beginning. The world of forecasting is constantly evolving, and Amazon Forecast is at the forefront of that innovation. So stay curious, keep learning and embrace the future of cloud computing. Thanks for joining us on this deep dive into Amazon Forecast. Until next time. Happy. Forecasting.