AWS SageMaker: Machine Learning on AWS

All right, welcome to The Deep Dive. Today we're going deep on AWS SageMaker. For our listeners, especially those of you who are always trying to stay ahead of the curve, think of SageMaker as like a really powerful machine learning workshop. Kind of, it's a platform that's being used by all sorts of organizations to pull out insights from their data, automate all sorts of things, and really drive innovation. So we really want to make sure you understand the core of what this thing can do. Our main source for this Deep Dive is, well, a deep analysis of AWS SageMaker itself. - Makes sense. - Our mission today is to really break this thing down. What it is, what it can do, why it matters in the world of machine learning, you know, all that good stuff, without getting too lost in all the technical details. We'll look at its core purpose, the capabilities it offers, how it handles some of the big challenges people hit in machine learning workflows, the architecture, how it works with other AWS tools, some real-world examples to see it in action, and then some best practices so you can use it effectively. Ready to jump in? - Yeah, absolutely. It's pretty remarkable to see how a platform like SageMaker has evolved to offer such a centralized and streamlined way to handle what can be a very, very complex area. - Right, right. So let's start by getting a clear understanding of what it's all about. What's the real heart, the core purpose of AWS SageMaker? - So at its core, SageMaker is really about unification. Its main goal is to be a single integrated platform for the entire lifecycle of a machine learning project. Imagine, instead of having separate tools for each stage, like one for data prep, another for model design, then another for training, another for deployment, you have everything you need in one place. And this integration is designed to really boost the productivity of data scientists and machine learning engineers by giving them these purpose built tools and automating a lot of the manual tasks that used to be necessary. The goal is to get you from raw data to a working machine learning solution much faster. - So no more juggling a bunch of different tools and trying to make them talk to each other. - Exactly, yeah. A big part of this is SageMaker Studio. It's a single web-based interface that gives you access to everything SageMaker can do. So you can go from, say, exploring your data to monitoring your models without having to switch between different applications. It even brings together data access by working with a ton of different AWS machine learning and data analytic services. - So it really does sound like a huge efficiency boost. And it handles a lot of the infrastructure management under the hood, right? Like the stuff that can really bog people down. - Exactly. SageMaker takes care of a lot of the complexity of managing the infrastructure you need for machine learning. This lets the data scientists and ML engineers focus on what they do best, the data science and the model development. By taking care of the infrastructure, SageMaker can really speed up the whole process of developing, training, and deploying these machine learning applications. - Okay, so we've got this unified hub. What are some of the actual key functionalities you can use within SageMaker? What can you actually do with it? - Well, the unified studio itself is a core functionality. It's like an IDE, an integrated development environment for pretty much all your data, analytics, and AI stuff. Within studio, you have these unified notebooks that can work across different compute resources and clusters, which is super helpful for scaling up your work. And then there's a built-in SQL editor. So you can directly explore and query your data, whether it's in S3 data lakes or Redshift data warehouses right there in the platform. - Having that SQL editor right there, that's gotta save a lot of time and hassle. - Oh, for sure. And since different people have different preferences, SageMaker Studio offers a choice of fully managed IDE's. You've got your Jupyter Lab, a code editor based on code OSS or VS code, and even our studio for people who love R. And this isn't just about personal preference, it's about letting people work in the environments where they're gonna be the most productive, plus studios built for teamwork. It lets you securely share all kinds of AI and analytics stuff like data sets, models, even generative AI applications. - So you can really collaborate effectively within the platform. Now, you also mentioned something called leak house. What's that all about? - Right, so leak house is designed to solve a big problem data silos. Data spread out all over the place. It unifies data access across all sorts of systems. We're talking S3, Redshift, even external data sources. And you can access and query all of this using tools that work with Apache iceberg, which is a very popular open format for data lakes. And you're working with a single copy of the data. - So no more having the same data copied in a bunch of different places creating all sorts of headaches. - Exactly, it makes data management a lot simpler, no more constantly moving and duplicating data. That saves storage costs and reduces the chances of getting inconsistencies. And very importantly, leak house has built-in governance features. You can set up very specific permissions so you've got good security and compliance across your entire data landscape. - That governance piece is so crucial, especially when you're dealing with sensitive data. Now, there's also something called the catalog. How does that fit in? - So the catalog is built on Amazon Data Zone. It takes the governance aspect even further. It's all about providing a secure and collaborative way to discover, manage, and use data and AI assets across the whole organization. It lets you define and enforce access policies in a consistent way with very fine-grained control. Think of it as a central directory for all your AI stuff with built-in security checks. - So making sure the right people have access to the right resources and that everything is tracked and auditable. - Absolutely, the catalog is really key for building a well-governed machine learning environment. Administrators can set up secure boundaries and control access to all sorts of ML assets. That improves security, compliance, and makes it easier for teams to find, access, and share data and AI resources responsibly. - Okay, let's move to the core of machine learning, which is building the models. You mentioned SageMaker AI earlier. - Yeah, SageMaker AI is basically the whole suite of tools within SageMaker that's specifically for building, training, and deploying machine learning models, both the traditional ones and those big, large foundation models that are becoming so important. It has a whole range of purpose-built tools for every step of the AI lifecycle. IDE's distributed training, inference services, MLObs, tools, governance, observability. It's all there, and it works with all the major frameworks, like TensorFlow, PyTorch, MXNet. So you've got lots of flexibility, you're not locked into any one vendor. - It's really a complete set of tools for the entire process. And then there's generative AI application development, which is a really hot area right now. - Yeah, definitely. This is how SageMaker tackles the whole generative AI world. It integrates seamlessly with Amazon Bedrock. Bedrock gives you access to a bunch of different foundation models from some of the top AI companies, so you can build and scale your own custom generative AI applications. And what's really powerful is how much you can customize these things through Bedrock. You've got knowledge bases to connect models to your specific data, guard rails to keep things ethical, agents for building autonomous systems, and flows for visually orchestrating complex AI workflows. It's not just about using pre-built models, it's about making them your own. - So you're not just using these models off the shelf, you're really integrating them into your own data and processes in a secure and controlled way. - Exactly. Giving you the tools to use these advanced AI models in a real enterprise environment. - You also mentioned SQL Analytics within SageMaker. - Right. SageMaker integrates with Amazon Redshift, which brings SQL Analytics directly into the platform. So you can use a very efficient and cost-effective SQL engine to query and analyze all that data you've got stored in your data warehouses. This integration just streamlines things for data scientists and analysts so they can do things like data exploration and feature engineering right within their familiar machine learning environment. It's all about keeping those processes together. Makes a lot of sense. And finally, what about handling the raw data itself, the data processing piece? - So data processing is of course fundamental to machine learning and SageMaker gives you some robust capabilities for this by integrating with Amazon, Athena, EMR, and AWS Glue. These integrations let you analyze, prepare, and integrate data using open source frameworks on really scalable and cost-effective infrastructure and the integration with AWS Glue is particularly noteworthy. Glue is serverless, so you can do all that data prep and transformation stuff without having to manage any of the underlying servers or anything. And for very large-scale processing, you can even run Spark jobs interactively right within SageMaker Studio notebooks using those serverless Spark environments provided by EMR and Glue. - Sounds like SageMaker's got the tools to work with your data no matter how big or complex it is. Okay, so we've talked about this unified environment. Let's go a bit deeper into the specific problems that a platform like SageMaker solves. What were some of the biggest roadblocks and machine learning development before these kinds of tools became available? - Well, one of the biggest hurdles was how fragmented the development environment used to be. Like we talked about earlier, you'd need all these different tools for different parts of the process, each with its own way of working, and trying to get them to play nice together was a nightmare. SageMaker's unified studio really addresses this by putting all the essential tools for the whole machine learning lifecycle into one platform. We're talking data exploration, preparation, model building with different algorithms, scalable training, deployment, monitoring, it's all there. - So it's not just about having all the tools in one toolbox, it's about getting rid of all that friction and overhead of managing them separately. - Yeah, exactly. It streamlines the whole workflow and makes people a lot more productive. Data scientists and engineers can spend more time on what matters, you know, the actual work. And let's-- time messing around with infrastructure. >> Right. You also mentioned data accessibility and governance is a major challenge. How does SageMaker handle that? >> You know, before a lot of times the data you needed for machine learning was scattered across different data lakes and data warehouses, getting secure, governed access to all of it was a real pain. SageMaker's like house tackles this directly. It gives you a unified view of your data in S3 and Redshift so teams can work with one consistent copy. No more data silos and a much smoother, more efficient way to use your data. Then you've got the SageMaker catalog built on data zone which offers end-to-end governance and access control through this system of domains, projects, and assets. So you can set up very granular permissions which means strong security and compliance while still allowing data teams to collaborate. And the integration with data zone makes the whole governance framework even stronger across all your AI initiatives. >> It sounds like it's designed to walk that tightrope, you know, making data easily accessible for innovation but also making sure it's secure and compliant. Now building and training models can be pretty intense. What does SageMaker do to make that easier? >> Yeah, building and training models can be really complicated and time consuming and you often need a lot of specialized knowledge to do it well. And then scaling up the training process to handle huge data sets has always been a technical challenge. SageMaker AI simplifies this in several ways. It's a pay-as-you-go service so you only pay for what you use. It has a bunch of built-in algorithms that are already optimized and it supports all the popular deep learning frameworks like we talked about. Users get access to really powerful compute resources on demand so they can train their models on enormous data sets without having to manage all that infrastructure themselves. And then you have features like SageMaker AutoPilot. AutoPilot is pretty amazing. It automates the whole model tuning and selection process which can be incredibly tedious and time consuming to quickly find the best performing models for a given data set and problem. And then there's SageMaker Jumpstart which is a collection of pre-trained models and complete solution templates that can really jumpstart your model building. >> So you've got the basic building blocks and you've also got some pre-assembled components that can really speed things up. >> Exactly, it makes it much easier for organizations to get started with machine learning and accelerates the whole process. >> Now once you've got a trained model that's not the end of the story is it. Deploying and managing that model, the whole MLO's world can be tricky too. >> Yeah, absolutely getting a model into production so it can actually do something useful. And then managing its life cycles, setting up inference and points, monitoring performance, making sure it scales and stays reliable. That's historically been a pretty complex thing to handle. But SageMaker has a bunch of tools to make this easier. It's got options for real time and batch inference depending on what you need. Amazon SageMaker pipelines lets you automate that entire machine learning workflow from data prep to deployment with integrated CI/CD. Then there's SageMaker model monitor. Model monitor is super important because it can detect things like concept drift which is where the relationships in your data change over time and performance degradation and it'll alert you to potential problems. And then you have the SageMaker model registry which is a central place to keep all your models and their metadata which is really important for reproducibility, governance, and smooth deployment. >> So it's bringing that same level of organization and automation to the deployment and management side of things. >> Now what about when companies need to use more advanced AI stuff like generative AI or really sophisticated data analytics? >> Well in the past you'd often have to go out and get separate specialized services for that kind of thing and you'd need very specific expertise to use them. SageMaker takes a different approach. It integrates directly with services like Amazon Bedrock which gives you easy access to a bunch of high performing foundation models for all sorts of generative AI applications. And it's integration with Amazon Redshift lets you do SQL analysis on huge data sets right within SageMaker. This means you can use these advanced AI and analytics tools without having to set up and manage a whole separate infrastructure for them. >> It really is evolving into a one stop shop for all things AI, isn't it? >> It really is. >> Another challenge you mentioned was collaboration and governance, especially in larger organizations. How does SageMaker help with that? >> If you want to successfully implement AI in a big company, you need all the different people involved to be able to collaborate effectively. We're talking data scientists, ML engineers, business users, everyone needs to be on the same page. But when your tools and data are all over the place, that can be really tough and governance becomes a real headache. SageMaker Unified Studio provides a central platform where teams can work together on projects, share their stuff securely like notebooks, code models, and access data in a controlled way. And as we talked about, the SageMaker catalog with data zone underneath gives you a way to discover, manage, and share all those data and AI assets across the whole organization. So you can define and enforce access policies, make sure everyone has the right permissions, and create a much more collaborative and secure environment for AI development and deployment. >> It's breaking down the walls between different teams and roles, encouraging a more unified way of working. >> Right. >> And finally, we have to talk about cost. Cloud services can get expensive, especially when you're doing machine learning with all its compute demands. What does SageMaker do to help manage costs? >> Cost management is definitely something you've got to think about. And SageMaker tackles it in a few ways. First, it's pay as you go, like a lot of cloud services, so you only pay for the resources you use. But beyond that, there are specific features and tools to optimize costs. For example, Amazon SageMaker managed spot training. Let's you take advantage of spare EC2 capacity at a much lower price for training jobs that have a little bit of flexibility in their run time. And for inference, if your workload goes up and down a lot, serverless inference can be a lot more cost effective, because you only pay when your model is actually making predictions. Another important thing is auto scaling for your real time inference endpoints. That automatically adjusts the capacity based on how much traffic you're getting, so you don't end up paying for resources you're not using. And just as a general best practice, you want to monitor your resource utilization and delete anything you're not using anymore, whether it's notebooks, jobs, or endpoints. You can even set up idle shutdown policies for your SageMaker Studio notebooks, so they don't just sit there burning money. It sounds like they've put a lot of thought into giving users control over their costs and helping them optimize based on their needs. Okay, let's look behind the curtain now. Can you describe the overall architecture of SageMaker? So SageMaker is built for the cloud, meaning it's designed to work seamlessly with the whole AWS ecosystem. A central concept in its architecture is the SageMaker domain. This is like an isolated secure space where data science teams can collaborate. Within these domains, the main tool for development and experimentation is usually Jupyter notebooks, which you access through SageMaker Studio. A typical workflow involves grabbing data from different AWS data services, like S3, RDS, Redshift, Glue. Then using SageMaker's managed compute to train your models and finally deploying those models as web services called SageMaker Endpoints to make real-time predictions. And the whole architecture is built to be both scalable and reliable so it can handle a wide range of workload sizes and complexities all within the secure integrated environment. So it's flexible, it can grow with you as your needs grow and it's all within the security of AWS. What are the main building blocks the core components of this architecture? Well, we've touched on a lot of them already, but let's recap their roles. SageMaker Studio is the main IDE that unified interface we keep talking about. It supports multiple IDE's like Jupyter Lab, the code editor, our studio, so you've got options. It also connects to a huge library of pre-trained models and solutions through SageMaker Jumpstart, which is great for generative AI stuff. It has powerful data prep tools like Data Wrangler built right in and it seamlessly connects to SageMaker's training capabilities as well as the tools for deploying and monitoring models. Really, Studio is your command center for the whole machine learning lifecycle and it even helps with team collaboration and integrates with those no-code platforms like SageMaker Canvas and AI coding assistants like Amazon Q Developer. Studio is really the heart of the whole system. What about SageMaker Ground Truth? SageMaker Ground Truth handles a very specific but very important part of machine learning, creating high quality label data sets for supervised learning. Basically, it's a managed service for building accurate training data sets through human annotation. It supports labeling for all sorts of data text, images, video, audio, even 3D point clouds. Ground Truth has these really well designed human-in-the-loop workflows, so you can include human review steps to make sure the labels are accurate and high quality. It also uses machine learning itself to automate parts of the labeling, which saves a lot of time and effort. You can choose to use an AWS managed workforce to do the labeling or you can manage your own team, even using Amazon Mechanical Turk, if you want. And the labeled data you get from Ground Truth is usually stored in S3, ready to be used for training models in SageMaker. - It's about making sure the data you're using to train your models is as good as it can be. - Exactly. - And then there's SageMaker Autopilot which automates the model building process. How does that work within the architecture? - So Autopilot is an automated machine learning service, AutoML. It takes a data set, usually stored in S3 and analyzes it, tries out different feature engineering techniques, and then trains and tunes a whole bunch of different machine learning models. Then it gives you a leaderboard showing the best performing ones. In terms of the architecture, Autopilot uses different SageMaker components like managed compute instances for training and tuning. It also integrates with the model registry to store the trained models. And it can even automatically generate SageMaker Studio Notebooks showing you exactly what it did so you can understand the process and maybe refine the models further. Autopilot can handle a variety of machine learning problems, regression, classification, time series forecasting so it's a pretty versatile tool within SageMaker. It really takes a lot of the work out of those early stages of model development. Yeah, it does. And we've got a handy table here summarizing those key components. Studio is that central IDE integrating with lots of things. Ground truth is mainly about labeling and integrates with S3 and autopilot for automated model building relies heavily on S3 in Studio. It really shows how connected they all are. For sure. Each component has its own job to do but they all work together seamlessly within SageMaker. And they all tie into the wider AWS ecosystem as well. Absolutely. Okay, let's talk about how SageMaker interacts with all those other services AWS has to offer. It's not like it's a standalone thing, right? No, not at all. SageMaker has very deep integration with a ton of other AWS services, creating a really powerful ecosystem for end-to-end machine learning. For data storage and retrieval, S3 is fundamental. It's the primary data lake for machine learning. It stores your data sets, model artifacts, the results of your training and inference jobs, everything. And SageMaker can access data in S3 directly so you don't have to move data around all the time. It also integrates seamlessly with Amazon Redshift so you can use your existing data warehouses for machine learning and do SQL Analytics right within SageMaker. And the way S3 tables integrates with SageMaker Lakehouse adds another layer to this, giving you a unified way to access and query data across S3 Redshift and other sources. So basically any data you've got within AWS, you can easily use it with SageMaker. What about data processing and transformation? Getting your data ready for machine learning often involves a lot of processing and transformation and SageMaker integrates with AWS's data processing services to handle this. AWS Glue, which is that serverless ETL service we talked about, is a really good way to clean, transform and prepare your data for machine learning. And you can actually run AWS Glue interactive sessions right from your SageMaker Studio Notebooks, which makes things super convenient. This lets you use the power of Spark and Ray, which are the engines behind Glue without ever leaving SageMaker Studio. And for truly massive data sets, SageMaker also works with Amazon EMR, which is a managed big data service that gives you scalable distributed processing with Spark and Hadoop. And if you need to do some ad hoc querying of your S3 data using SQL, you can use Amazon Athena, which is a serverless interactive query service that integrates with SageMaker as well. So no matter how big your data is or how complex the processing you need to do, SageMaker provides an integrated way to do it. That's right. Now, security and access management are super important in the cloud. So security is paramount, and SageMaker integrates really tightly with AWS identity and access management or IAM. IAM roles are how you control access to different AWS resources. SageMaker can use IAM roles to access things like S3 buckets or ECR without having to store long-term credentials directly in your code. This is called the principle of least privilege, and it makes things a lot more secure. And then the integration with Amazon DataZone extends those security and access management capabilities. DataZone gives you a framework for setting up secure infrastructure like VPCs and encryption with KMS keys and managing access to data and ML assets within SageMaker. So you've got granular control over who can access what and what they can do with it all managed to the existing AWS security tools. Right. What about if people have very specific needs like custom algorithms or software dependencies that aren't part of SageMaker? For those cases, SageMaker integrates with Amazon elastic container registry, ECR. ECR is AWS's managed Docker container registry. So you can package your custom algorithms, specific framework versions, or even your whole environment into Docker images, and then use those images within SageMaker for training, inference, or processing jobs. You just build your container image, push it to ECR, and then point SageMaker to that image when you set up your job. This gives advanced users a lot of flexibility. You know, people who need fine-grained control over their environment, or who are deploying proprietary algorithms. And using ECR also makes it easy to share these custom solutions within your organization, or even through the AWS marketplace. So you can bring your own custom stuff and still use the managed environment of SageMaker? Yeah. What about more complex workflows? You know, ones that involve multiple steps? To automate and manage those kinds of workflows, SageMaker integrates with AWS step functions, which is a serverless workflow orchestration service. You can define and run these multi-step workflows that include all sorts of SageMaker tasks, like launching training jobs, doing hyper parameter tuning, deploying models, running batch transformations. AWS even has a special SDK for this, the step functions data science SDK, which is a Python interface for creating and managing these workflows programmatically. So you can basically automate the entire machine learning lifecycle. This makes things more efficient, reduces errors, and just makes your AI applications more reliable. So you can build these complete automated pipelines connecting different SageMaker components and other AWS services. Now, as your machine learning work gets more sophisticated, managing all the features you use to train your models can become a challenge. How does SageMaker address this? Feature management is definitely important, and SageMaker integrates with SageMaker feature store to provide a dedicated, managed place to store and manage those features. It's like a central catalog where you can find and access features consistently across the whole lifecycle of your ML project from training to real-time inference. This ensures that you're using the same features consistently, makes it easier to reuse well-engineered features across different projects and teams, and streamlines the whole feature engineering process. The feature store supports both online stores for low latency access when you need to make real-time predictions and offline stores, which are more for batch processing and training on historical data. So you've got flexibility for different use cases. That sounds like a big help for organizations that are doing a lot of feature engineering. Yeah, it can make a big difference. And to tie everything together, we come back to the integration with Amazon DataZone for data cataloging and governance. Exactly, like we talked about, this is really important for building a well-governed catalog of your data and ML assets. DataZone lets you set up consistent data governance policies, so you know the data is being used responsibly and compliantly. And the way it integrates with SageMaker means that users can easily find and use these governed data assets in their workflows, which creates a much more secure and collaborative environment. It really is designed to be a central part of the whole AWS ecosystem. Yeah. Okay, let's shift gears and talk about how this actually plays out in the real world. You give us some examples of how people are using SageMaker in different industries. Sure. We're seeing the impact of SageMaker across a wide range of industries. For example, in healthcare, pharmaceutical companies are using SageMaker to automate quality assurance and drug packaging. They're using machine learning to analyze images and find defects, which is leading to higher detection rates and fewer rejected products. Medical imaging is another big area, with SageMaker ground truth being used to label medical images, X-rays, MRIs, CT scans very accurately. That's really important for training accurate diagnostic models. We're also seeing SageMaker used for personalized medicine where models are analyzing patient data to predict treatment outcomes and tailor treatments to individuals. And SageMaker's ability to handle sensitive healthcare data accurately is a key factor in these developments. One example is SageMaker ground truth plus. It can be used for things like analyzing head pose and detecting mobile phone use and drivers, but those same capabilities can be applied to patient monitoring, analyzing subtle behavior and vital signs. So it's having a real impact in healthcare, improving efficiency and patient care. What about in finance? Finance has been a big adopter of SageMaker. They're using it for things like fraud detection, rebuild solutions and templates to build better fraud prevention systems. Risk assessment is another big one using models to predict financial risks and make better decisions. And algorithmic trading platforms are using SageMaker's low latency inference capabilities to execute trades very quickly based on real-time analysis. They're also using it for customer churn prediction to understand why customers leave and figure out how to keep them. AWS even has industry-specific resources like SageMaker jumpstart industry. Financial, which shows you how seriously they're taking this sector. It's helping them manage risk and build stronger relationships with their customers. Right. What about retail? Retail companies are using SageMaker to optimize their operations and improve the customer experience. Demand forecasting is a big one using tools like SageMaker Canvas to predict future demand more accurately so they can manage inventory better and reduce waste. Personalized recommendations are another key area using models to analyze customer behavior and preferences to recommend products that people are actually likely to want. And SageMaker Canvas has really good time series forecasting capabilities which is perfect for predicting inventory needs and identifying sales trends. They can even do quantile forecasting and what if analysis? Retailers are also using SageMaker to analyze customer sentiment and contact centers so they can get insights to improve agent training and customer satisfaction. And they're even starting to explore generative AI within SageMaker Canvas to do things like create and improve call scripts and automate post-call tasks. So it's helping them optimize their supply chains, personalize the customer experience and really understand what their customers need. That's the goal. Okay, so for anyone who wants to really master SageMaker, what are some best practices to keep in mind? Well, if you want to get the most out of SageMaker and avoid some common pitfalls, there are some key things to focus on, especially in terms of performance and cost management. For performance, you want to make sure you're using the right EC2 instance type for the job. GPU instances are good for compute intensive tasks like training deep learning models while CPU instances might be fine for other things. If you're working with very large models or data sets, using distributed training across multiple instances can speed things up significantly. Hyper parameter tuning is also crucial for getting them best performance out of your models, and SageMaker has tools to help you automate that. And for deployment, you might want to consider using the SageMaker inference compiler, which can optimize your models for both cloud and edge devices, improving latency and throughput. So there are definitely things you can do to make sure your workloads run smoothly and efficiently. And then there's the cost side of things. Yeah, cost management is super important when you're using a platform like SageMaker. Like we talked about before, using managed spot training for jobs that have some flexibility can save you a lot of money. And for inference, if your traffic is variable, serverless inference can be a lot more cost effective. Auto scaling for your real-time inference endpoints is also key, so you don't over-provision resources. And just in general, get in the habit of monitoring your resource utilization and deleting anything you're not using. So lots of practical ways to keep costs under control. Definitely. And we've got a handy table here that summarizes these best practices for performance and cost management, showing the specific benefits of each. It's a good reminder that you need to think about both of these things. Okay, as we wrap things up, let's talk about the bigger picture. What's the significance of AWS SageMaker in the world of AI and machine learning? I think it's fair to say that SageMaker has played a major role in the advances we've seen in AI and ML. It's a comprehensive and fully managed platform, which means it's made these powerful tools and the infrastructure they need accessible to a much wider audience. People and organizations who might not have had the in-house expertise to do this stuff before can now take advantage of these technologies. By streamlining the whole development process, it's really sped up innovation in the field, and those MLOPS features are helping to standardize and industrialize machine learning workflows, making them more reliable and easier to manage in production, and the fact that it supports emerging technologies like generative AI through its integration with bedrock is also really important. It's lowering the barrier to entry and making advanced AI much more accessible. For sure. So how does SageMaker compare to other machine learning platforms out there? There are some other big platforms like Google Cloud AI platform, Azure Machine Learning for Microsoft and Databricks Machine Learning. They all have their strengths, but one of the biggest advantages SageMaker has is it's incredibly deep integration with the entire AWS ecosystem. It's got a very wide range of features that cover every step of the machine learning lifecycle, and it's really focused on providing enterprise grade capabilities, things like governance security and collaboration tools. Some people have said that some of the instance types in SageMaker can be a bit more expensive than the competition, but the fact that it's such a unified, scalable, and secure environment that's so tightly integrated with all the other AWS services makes it a very attractive choice for companies that are already using AWS. It's the complete package designed for the enterprise and built on the power of the AWS Cloud. Right. That was a deep dive.